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classes | mentioned_in_github
bool 2
classes | framework
stringclasses 9
values |
|---|---|---|---|---|---|---|---|---|---|
https://paperswithcode.com/paper/no-past-bo-normalized-portfolio-allocation
|
No-PASt-BO: Normalized Portfolio Allocation Strategy for Bayesian Optimization
|
1908.00361
|
https://arxiv.org/abs/1908.00361v1
|
https://arxiv.org/pdf/1908.00361v1.pdf
|
https://github.com/thiago-vasconcelos/no-past-bo
| true | true | false |
none
|
https://paperswithcode.com/paper/dabnet-depth-wise-asymmetric-bottleneck-for
|
DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation
|
1907.11357
|
https://arxiv.org/abs/1907.11357v2
|
https://arxiv.org/pdf/1907.11357v2.pdf
|
https://github.com/Reagan1311/DABNet
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/multimodal-age-and-gender-classification
|
Multimodal Age and Gender Classification Using Ear and Profile Face Images
|
1907.10081
|
https://arxiv.org/abs/1907.10081v1
|
https://arxiv.org/pdf/1907.10081v1.pdf
|
https://github.com/iremeyiokur/multipie_extended_ear_dataset
| true | false | false |
none
|
https://paperswithcode.com/paper/bubblenets-learning-to-select-the-guidance
|
BubbleNets: Learning to Select the Guidance Frame in Video Object Segmentation by Deep Sorting Frames
|
1903.11779
|
https://arxiv.org/abs/1903.11779v2
|
https://arxiv.org/pdf/1903.11779v2.pdf
|
https://github.com/griffbr/BubbleNets
| true | true | false |
tf
|
https://paperswithcode.com/paper/data-augmentation-via-dependency-tree-1
|
Data Augmentation via Dependency Tree Morphing for Low-Resource Languages
|
1903.09460
|
http://arxiv.org/abs/1903.09460v1
|
http://arxiv.org/pdf/1903.09460v1.pdf
|
https://github.com/gozdesahin/crop-rotate-augment
| true | true | false |
none
|
https://paperswithcode.com/paper/bags-of-tricks-and-a-strong-baseline-for-deep
|
Bag of Tricks and A Strong Baseline for Deep Person Re-identification
|
1903.07071
|
http://arxiv.org/abs/1903.07071v3
|
http://arxiv.org/pdf/1903.07071v3.pdf
|
https://github.com/michuanhaohao/reid-strong-baseline
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/imexnet-a-forward-stable-deep-neural-network
|
IMEXnet: A Forward Stable Deep Neural Network
|
1903.02639
|
https://arxiv.org/abs/1903.02639v2
|
https://arxiv.org/pdf/1903.02639v2.pdf
|
https://github.com/HaberGroup/SemiImplicitDNNs
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/equi-normalization-of-neural-networks
|
Equi-normalization of Neural Networks
|
1902.10416
|
http://arxiv.org/abs/1902.10416v1
|
http://arxiv.org/pdf/1902.10416v1.pdf
|
https://github.com/facebookresearch/enorm
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/eikonal-solution-using-physics-informed
|
PINNeik: Eikonal solution using physics-informed neural networks
|
2007.08330
|
https://arxiv.org/abs/2007.08330v2
|
https://arxiv.org/pdf/2007.08330v2.pdf
|
https://github.com/umairbinwaheed/PINNeikonal
| true | true | false |
tf
|
https://paperswithcode.com/paper/macow-masked-convolutional-generative-flow
|
MaCow: Masked Convolutional Generative Flow
|
1902.04208
|
https://arxiv.org/abs/1902.04208v5
|
https://arxiv.org/pdf/1902.04208v5.pdf
|
https://github.com/XuezheMax/macow
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/multigrain-a-unified-image-embedding-for
|
MultiGrain: a unified image embedding for classes and instances
|
1902.05509
|
http://arxiv.org/abs/1902.05509v2
|
http://arxiv.org/pdf/1902.05509v2.pdf
|
https://github.com/facebookresearch/multigrain
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/meta-fine-tuning-neural-language-models-for
|
Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining
|
2003.13003
|
https://arxiv.org/abs/2003.13003v2
|
https://arxiv.org/pdf/2003.13003v2.pdf
|
https://github.com/alibaba/EasyTransfer/tree/master/scripts/meta_finetune
| false | false | false |
tf
|
https://paperswithcode.com/paper/block-bilinear-superdiagonal-fusion-for
|
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection
|
1902.00038
|
http://arxiv.org/abs/1902.00038v2
|
http://arxiv.org/pdf/1902.00038v2.pdf
|
https://github.com/Cadene/block.bootstrap.pytorch
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/throttling-malware-families-in-2d
|
Throttling Malware Families in 2D
|
1901.10590
|
http://arxiv.org/abs/1901.10590v1
|
http://arxiv.org/pdf/1901.10590v1.pdf
|
https://github.com/mnassar/malware-viz
| true | true | false |
none
|
https://paperswithcode.com/paper/a-meta-transfer-objective-for-learning-to
|
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
|
1901.10912
|
http://arxiv.org/abs/1901.10912v2
|
http://arxiv.org/pdf/1901.10912v2.pdf
|
https://github.com/authors-1901-10912/A-Meta-Transfer-Objective-For-Learning-To-Disentangle-Causal-Mechanisms
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/estimating-multi-year-247-origin-destination
|
Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data
|
1901.09266
|
http://arxiv.org/abs/1901.09266v1
|
http://arxiv.org/pdf/1901.09266v1.pdf
|
https://github.com/Lemma1/DPFE
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/rap-net-region-attention-predictive-network
|
RAP-Net: Region Attention Predictive Network for Precipitation Nowcasting
|
2110.01035
|
https://arxiv.org/abs/2110.01035v1
|
https://arxiv.org/pdf/2110.01035v1.pdf
|
https://github.com/luochuyao/rap-net
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/packet2vec-utilizing-word2vec-for-feature
|
Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet Data
|
2004.14477
|
https://arxiv.org/abs/2004.14477v1
|
https://arxiv.org/pdf/2004.14477v1.pdf
|
https://github.com/sandialabs/packet2vec
| true | false | false |
none
|
https://paperswithcode.com/paper/action-robust-reinforcement-learning-and
|
Action Robust Reinforcement Learning and Applications in Continuous Control
|
1901.09184
|
https://arxiv.org/abs/1901.09184v2
|
https://arxiv.org/pdf/1901.09184v2.pdf
|
https://github.com/tesslerc/ActionRobustRL
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/automated-treatment-planning-in-radiation
|
Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks
|
1807.06489
|
http://arxiv.org/abs/1807.06489v1
|
http://arxiv.org/pdf/1807.06489v1.pdf
|
https://github.com/rafidrm/gancer
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/3d-hand-pose-estimation-using-simulation-and
|
3D Hand Pose Estimation using Simulation and Partial-Supervision with a Shared Latent Space
|
1807.05380
|
http://arxiv.org/abs/1807.05380v1
|
http://arxiv.org/pdf/1807.05380v1.pdf
|
https://github.com/masabdi/LSPS
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/a-software-defined-channel-sounder-for
|
A Software-Defined Channel Sounder for Industrial Environments with Fast Time Variance
|
1805.01236
|
http://arxiv.org/abs/1805.01236v1
|
http://arxiv.org/pdf/1805.01236v1.pdf
|
https://github.com/inIT-HF/gr-corrsounder
| true | true | true |
none
|
https://paperswithcode.com/paper/an-empirical-study-on-the-names-of-points-of
|
An empirical study on the names of points of interest and their changes with geographic distance
|
1806.08040
|
http://arxiv.org/abs/1806.08040v1
|
http://arxiv.org/pdf/1806.08040v1.pdf
|
https://github.com/YingjieHu/POI_Name
| true | true | true |
none
|
https://paperswithcode.com/paper/scalable-bayesian-inference-for-excitatory
|
Scalable Bayesian Inference for Excitatory Point Process Networks
|
1507.03228
|
http://arxiv.org/abs/1507.03228v1
|
http://arxiv.org/pdf/1507.03228v1.pdf
|
https://github.com/slinderman/pyhawkes
| true | true | false |
none
|
https://paperswithcode.com/paper/gslicr-slic-superpixels-at-over-250hz
|
gSLICr: SLIC superpixels at over 250Hz
|
1509.04232
|
http://arxiv.org/abs/1509.04232v1
|
http://arxiv.org/pdf/1509.04232v1.pdf
|
https://github.com/carlren/gSLICr
| true | true | true |
none
|
https://paperswithcode.com/paper/diving-deeper-into-mentee-networks
|
Diving deeper into mentee networks
|
1604.08220
|
http://arxiv.org/abs/1604.08220v1
|
http://arxiv.org/pdf/1604.08220v1.pdf
|
https://github.com/ragavvenkatesan/regularizer-network
| true | true | false |
none
|
https://paperswithcode.com/paper/cgmos-certainty-guided-minority-oversampling
|
CGMOS: Certainty Guided Minority OverSampling
|
1607.06525
|
http://arxiv.org/abs/1607.06525v1
|
http://arxiv.org/pdf/1607.06525v1.pdf
|
https://github.com/xzhang311/CGMOS
| true | true | false |
none
|
https://paperswithcode.com/paper/semi-supervised-deep-learning-by-metric
|
Semi-supervised deep learning by metric embedding
|
1611.01449
|
http://arxiv.org/abs/1611.01449v2
|
http://arxiv.org/pdf/1611.01449v2.pdf
|
https://github.com/eladhoffer/SemiSupContrast
| true | true | true |
none
|
https://paperswithcode.com/paper/linear-disentangled-representation-learning
|
Linear Disentangled Representation Learning for Facial Actions
|
1701.03102
|
http://arxiv.org/abs/1701.03102v1
|
http://arxiv.org/pdf/1701.03102v1.pdf
|
https://github.com/eglxiang/FacialAU
| true | true | false |
none
|
https://paperswithcode.com/paper/morphological-inflection-generation-with-hard
|
Morphological Inflection Generation with Hard Monotonic Attention
|
1611.01487
|
http://arxiv.org/abs/1611.01487v3
|
http://arxiv.org/pdf/1611.01487v3.pdf
|
https://github.com/roeeaharoni/morphological-reinflection
| true | true | false |
none
|
https://paperswithcode.com/paper/estimation-of-solar-irradiance-using-ground
|
Estimation of solar irradiance using ground-based whole sky imagers
|
1606.02546
|
http://arxiv.org/abs/1606.02546v2
|
http://arxiv.org/pdf/1606.02546v2.pdf
|
https://github.com/Soumyabrata/solar-irradiance-estimation
| true | true | false |
none
|
https://paperswithcode.com/paper/nicp-dense-normal-based-point-cloud
|
NICP: Dense Normal Based Point Cloud Registration
| null |
https://ieeexplore.ieee.org/document/7353455
|
https://www.researchgate.net/profile/Jacopo-Serafin/publication/304251318_NICP_Dense_Normal_Based_Point_Cloud_Registration/links/576a91fb08aef2a864d1dd2f/NICP-Dense-Normal-Based-Point-Cloud-Registration.pdf
|
https://github.com/yorsh87/nicp
| false | false | false |
none
|
https://paperswithcode.com/paper/applying-deep-machine-learning-for-psycho
|
Applying Deep Machine Learning for psycho-demographic profiling of Internet users using O.C.E.A.N. model of personality
|
1703.06914
|
http://arxiv.org/abs/1703.06914v2
|
http://arxiv.org/pdf/1703.06914v2.pdf
|
https://github.com/NewGround-LLC/psistats
| true | true | true |
tf
|
https://paperswithcode.com/paper/lens-distortion-rectification-using
|
Lens Distortion Rectification using Triangulation based Interpolation
|
1611.09559
|
http://arxiv.org/abs/1611.09559v2
|
http://arxiv.org/pdf/1611.09559v2.pdf
|
https://github.com/bbenligiray/lens-distortion-triangulation
| true | true | true |
none
|
https://paperswithcode.com/paper/confident-multiple-choice-learning
|
Confident Multiple Choice Learning
|
1706.03475
|
http://arxiv.org/abs/1706.03475v2
|
http://arxiv.org/pdf/1706.03475v2.pdf
|
https://github.com/chhwang/cmcl
| true | true | false |
tf
|
https://paperswithcode.com/paper/forward-and-reverse-gradient-based
|
Forward and Reverse Gradient-Based Hyperparameter Optimization
|
1703.01785
|
http://arxiv.org/abs/1703.01785v3
|
http://arxiv.org/pdf/1703.01785v3.pdf
|
https://github.com/lucfra/RFHO
| true | true | false |
tf
|
https://paperswithcode.com/paper/cell-r-cnn-v3-a-novel-panoptic-paradigm-for
|
Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm for Biomedical and Biological Images
|
2002.06345
|
https://arxiv.org/abs/2002.06345v2
|
https://arxiv.org/pdf/2002.06345v2.pdf
|
https://github.com/dliu5812/PFFNet
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/neural-ir-explorer-a-content-focused-tool-to
|
Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-Ranking Results
|
1912.04713
|
https://arxiv.org/abs/1912.04713v1
|
https://arxiv.org/pdf/1912.04713v1.pdf
|
https://github.com/sebastian-hofstaetter/neural-ir-explorer
| true | true | true |
none
|
https://paperswithcode.com/paper/pidginunmt-unsupervised-neural-machine
|
PidginUNMT: Unsupervised Neural Machine Translation from West African Pidgin to English
|
1912.03444
|
https://arxiv.org/abs/1912.03444v1
|
https://arxiv.org/pdf/1912.03444v1.pdf
|
https://github.com/keleog/PidginUNMT
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/membership-inference-attacks-on-sequence-to
|
Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?
|
1904.05506
|
https://arxiv.org/abs/1904.05506v2
|
https://arxiv.org/pdf/1904.05506v2.pdf
|
https://github.com/sorami/TACL-Membership
| true | true | true |
none
|
https://paperswithcode.com/paper/improving-question-answering-with-external
|
Improving Question Answering with External Knowledge
|
1902.00993
|
https://arxiv.org/abs/1902.00993v3
|
https://arxiv.org/pdf/1902.00993v3.pdf
|
https://github.com/nlpdata/external
| true | true | true |
none
|
https://paperswithcode.com/paper/convolution-based-spectral-partitioning
|
Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification
|
1906.11981
|
https://arxiv.org/abs/1906.11981v1
|
https://arxiv.org/pdf/1906.11981v1.pdf
|
https://github.com/custom-computing-ic/SpecPatConv3D-Network
| true | true | true |
tf
|
https://paperswithcode.com/paper/xy-network-for-nuclear-segmentation-in-multi
|
HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
|
1812.06499
|
https://arxiv.org/abs/1812.06499v5
|
https://arxiv.org/pdf/1812.06499v5.pdf
|
https://github.com/vqdang/hover_net
| true | true | true |
tf
|
https://paperswithcode.com/paper/a-multiscale-visualization-of-attention-in
|
A Multiscale Visualization of Attention in the Transformer Model
|
1906.05714
|
https://arxiv.org/abs/1906.05714v1
|
https://arxiv.org/pdf/1906.05714v1.pdf
|
https://github.com/jessevig/bertviz
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/a-hitchhikers-guide-to-statistical
|
A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms
|
1904.06979
|
https://arxiv.org/abs/1904.06979v2
|
https://arxiv.org/pdf/1904.06979v2.pdf
|
https://github.com/ccolas/rl_stats
| true | true | false |
none
|
https://paperswithcode.com/paper/bi-directional-lattice-recurrent-neural
|
Bi-Directional Lattice Recurrent Neural Networks for Confidence Estimation
|
1810.13024
|
http://arxiv.org/abs/1810.13024v2
|
http://arxiv.org/pdf/1810.13024v2.pdf
|
https://github.com/qiujiali/lattice_rnn
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/3d-psrnet-part-segmented-3d-point-cloud
|
3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction From a Single Image
|
1810.00461
|
http://arxiv.org/abs/1810.00461v1
|
http://arxiv.org/pdf/1810.00461v1.pdf
|
https://github.com/val-iisc/3d-psrnet
| true | true | true |
tf
|
https://paperswithcode.com/paper/pcl-proposal-cluster-learning-for-weakly
|
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
|
1807.03342
|
http://arxiv.org/abs/1807.03342v2
|
http://arxiv.org/pdf/1807.03342v2.pdf
|
https://github.com/ppengtang/oicr
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/neural-code-comprehension-a-learnable
|
Neural Code Comprehension: A Learnable Representation of Code Semantics
|
1806.07336
|
http://arxiv.org/abs/1806.07336v3
|
http://arxiv.org/pdf/1806.07336v3.pdf
|
https://github.com/spcl/ncc
| true | true | true |
tf
|
https://paperswithcode.com/paper/coupled-generative-adversarial-networks
|
Coupled Generative Adversarial Networks
|
1606.07536
|
http://arxiv.org/abs/1606.07536v2
|
http://arxiv.org/pdf/1606.07536v2.pdf
|
https://github.com/mingyuliutw/CoGAN
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/deep-biaffine-attention-for-neural-dependency
|
Deep Biaffine Attention for Neural Dependency Parsing
|
1611.01734
|
http://arxiv.org/abs/1611.01734v3
|
http://arxiv.org/pdf/1611.01734v3.pdf
|
https://github.com/ShannonAI/mrc-for-dependency-parsing
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/deformable-gans-for-pose-based-human-image
|
Deformable GANs for Pose-based Human Image Generation
|
1801.00055
|
http://arxiv.org/abs/1801.00055v2
|
http://arxiv.org/pdf/1801.00055v2.pdf
|
https://github.com/AliaksandrSiarohin/pose-gan
| true | true | true |
tf
|
https://paperswithcode.com/paper/lightweight-and-unobtrusive-privacy
|
Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference
|
1912.09859
|
https://arxiv.org/abs/1912.09859v3
|
https://arxiv.org/pdf/1912.09859v3.pdf
|
https://github.com/ntu-aiot/ObfNet
| true | true | false |
tf
|
https://paperswithcode.com/paper/explaining-and-harnessing-adversarial
|
Explaining and Harnessing Adversarial Examples
|
1412.6572
|
http://arxiv.org/abs/1412.6572v3
|
http://arxiv.org/pdf/1412.6572v3.pdf
|
https://github.com/OwenSec/DeepDetector
| false | false | true |
tf
|
https://paperswithcode.com/paper/towards-evaluating-the-robustness-of-neural
|
Towards Evaluating the Robustness of Neural Networks
|
1608.04644
|
http://arxiv.org/abs/1608.04644v2
|
http://arxiv.org/pdf/1608.04644v2.pdf
|
https://github.com/OwenSec/DeepDetector
| false | false | true |
tf
|
https://paperswithcode.com/paper/a-nice-mc-adversarial-training-for-mcmc
|
A-NICE-MC: Adversarial Training for MCMC
|
1706.07561
|
http://arxiv.org/abs/1706.07561v3
|
http://arxiv.org/pdf/1706.07561v3.pdf
|
https://github.com/jiamings/a-nice-mc
| true | true | true |
tf
|
https://paperswithcode.com/paper/nice-non-linear-independent-components
|
NICE: Non-linear Independent Components Estimation
|
1410.8516
|
http://arxiv.org/abs/1410.8516v6
|
http://arxiv.org/pdf/1410.8516v6.pdf
|
https://github.com/jiamings/a-nice-mc
| false | false | true |
tf
|
https://paperswithcode.com/paper/mcmc-using-hamiltonian-dynamics
|
MCMC using Hamiltonian dynamics
|
1206.1901
|
http://arxiv.org/abs/1206.1901v1
|
http://arxiv.org/pdf/1206.1901v1.pdf
|
https://github.com/jiamings/a-nice-mc
| false | false | true |
tf
|
https://paperswithcode.com/paper/ngraph-he2-a-high-throughput-framework-for
|
nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data
|
1908.04172
|
https://arxiv.org/abs/1908.04172v2
|
https://arxiv.org/pdf/1908.04172v2.pdf
|
https://github.com/NervanaSystems/he-transformer
| false | false | true |
tf
|
https://paperswithcode.com/paper/empathy-and-hope-resource-transfer-to-model
|
Empathy and Hope: Resource Transfer to Model Inter-country Social Media Dynamics
|
2106.12044
|
https://arxiv.org/abs/2106.12044v1
|
https://arxiv.org/pdf/2106.12044v1.pdf
|
https://github.com/anton-sturluson/empathy-and-hope
| true | true | true |
none
|
https://paperswithcode.com/paper/vision-dialog-navigation-by-exploring-cross
|
Vision-Dialog Navigation by Exploring Cross-modal Memory
|
2003.06745
|
https://arxiv.org/abs/2003.06745v1
|
https://arxiv.org/pdf/2003.06745v1.pdf
|
https://github.com/yeezhu/CMN.pytorch
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/neural-generation-of-dialogue-response
|
Neural Generation of Dialogue Response Timings
|
2005.09128
|
https://arxiv.org/abs/2005.09128v1
|
https://arxiv.org/pdf/2005.09128v1.pdf
|
https://github.com/mattroddy/RTNets
| true | true | true |
pytorch
|
https://paperswithcode.com/paper/continuous-control-with-deep-reinforcement
|
Continuous control with deep reinforcement learning
|
1509.02971
|
https://arxiv.org/abs/1509.02971v6
|
https://arxiv.org/pdf/1509.02971v6.pdf
|
https://github.com/FlyienSHaDOw/project_2_continuous_control
| false | false | true |
torch
|
https://paperswithcode.com/paper/time-adaptive-recurrent-neural-network
|
Time Adaptive Recurrent Neural Network
| null |
https://openreview.net/forum?id=VDUovuK0gV
|
https://openreview.net/pdf?id=VDUovuK0gV
|
https://github.com/anilkagak2/TARNN
| true | false | false |
tf
|
https://paperswithcode.com/paper/perceptual-losses-for-real-time-style
|
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
|
1603.08155
|
http://arxiv.org/abs/1603.08155v1
|
http://arxiv.org/pdf/1603.08155v1.pdf
|
https://github.com/TanguyJeanneau/white-mirror
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/a-neural-algorithm-of-artistic-style
|
A Neural Algorithm of Artistic Style
|
1508.06576
|
http://arxiv.org/abs/1508.06576v2
|
http://arxiv.org/pdf/1508.06576v2.pdf
|
https://github.com/TanguyJeanneau/white-mirror
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/reconfigurable-intelligent-surface-empowered
|
Reconfigurable-Intelligent-Surface Empowered Wireless Communications: Challenges and Opportunities
|
2001.00364
|
https://arxiv.org/abs/2001.00364v3
|
https://arxiv.org/pdf/2001.00364v3.pdf
|
https://github.com/liuhang1994/RIS_Rate_VS_Channel_Error
| false | false | true |
none
|
https://paperswithcode.com/paper/noise-contrastive-estimation-for-multivariate
|
Noise-Contrastive Estimation for Multivariate Point Processes
|
2011.00717
|
https://arxiv.org/abs/2011.00717v1
|
https://arxiv.org/pdf/2011.00717v1.pdf
|
https://github.com/HMEIatJHU/nce-mpp
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/analyzing-and-improving-the-image-quality-of
|
Analyzing and Improving the Image Quality of StyleGAN
|
1912.04958
|
https://arxiv.org/abs/1912.04958v2
|
https://arxiv.org/pdf/1912.04958v2.pdf
|
https://github.com/Xiaomao136/stylegan2-faceswap
| false | false | true |
tf
|
https://paperswithcode.com/paper/revisiting-click-based-interactive-video
|
Revisiting Click-based Interactive Video Object Segmentation
|
2203.01784
|
https://arxiv.org/abs/2203.01784v2
|
https://arxiv.org/pdf/2203.01784v2.pdf
|
https://github.com/Vujas-Eteph/CiVOS
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/semantic-understanding-of-scenes-through-the
|
Semantic Understanding of Scenes through the ADE20K Dataset
|
1608.05442
|
http://arxiv.org/abs/1608.05442v2
|
http://arxiv.org/pdf/1608.05442v2.pdf
|
https://github.com/u7javed/Bidirectional-Image-to-Image-Translator
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/provable-worst-case-guarantees-for-the
|
Certifiably Adversarially Robust Detection of Out-of-Distribution Data
|
2007.08473
|
https://arxiv.org/abs/2007.08473v3
|
https://arxiv.org/pdf/2007.08473v3.pdf
|
https://github.com/j-cb/GOOD
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/unpaired-image-to-image-translation-using
|
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
|
1703.10593
|
https://arxiv.org/abs/1703.10593v7
|
https://arxiv.org/pdf/1703.10593v7.pdf
|
https://github.com/u7javed/Bidirectional-Image-to-Image-Translator
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/nerf-representing-scenes-as-neural-radiance
|
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
|
2003.08934
|
https://arxiv.org/abs/2003.08934v2
|
https://arxiv.org/pdf/2003.08934v2.pdf
|
https://github.com/eitan3/nerf_gluon
| false | false | true |
mxnet
|
https://paperswithcode.com/paper/sketch-guided-image-inpainting-with-partial
|
Sketch-guided Image Inpainting with Partial Discrete Diffusion Process
|
2404.11949
|
https://arxiv.org/abs/2404.11949v1
|
https://arxiv.org/pdf/2404.11949v1.pdf
|
https://github.com/vl2g/sketch-inpainting
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/supervised-learning-with-a-quantum-classifier
|
Supervised learning with a quantum classifier using a multi-level system
|
1908.08385
|
https://arxiv.org/abs/1908.08385v1
|
https://arxiv.org/pdf/1908.08385v1.pdf
|
https://github.com/ard-srp/qml_analysis
| false | false | true |
tf
|
https://paperswithcode.com/paper/conditional-response-generation-using
|
Adversarial Learning on the Latent Space for Diverse Dialog Generation
|
1911.03817
|
https://arxiv.org/abs/1911.03817v3
|
https://arxiv.org/pdf/1911.03817v3.pdf
|
https://github.com/vikigenius/conditional_text_generation
| true | true | false |
pytorch
|
https://paperswithcode.com/paper/uncertainty-aware-unsupervised-domain
|
Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection
|
2103.00236
|
https://arxiv.org/abs/2103.00236v2
|
https://arxiv.org/pdf/2103.00236v2.pdf
|
https://github.com/Dayan-Guan/UaDAN
| true | false | true |
pytorch
|
https://paperswithcode.com/paper/real-time-3d-traffic-cone-detection-for
|
Real-time 3D Traffic Cone Detection for Autonomous Driving
|
1902.02394
|
https://arxiv.org/abs/1902.02394v2
|
https://arxiv.org/pdf/1902.02394v2.pdf
|
https://github.com/ayush111111/cone_keypoint_regression
| false | false | true |
none
|
https://paperswithcode.com/paper/automatic-trajectory-recognition-in-active
|
Automatic trajectory recognition in Active Target Time Projection Chambers data by means of hierarchical clustering
|
1807.03513
|
http://arxiv.org/abs/1807.03513v3
|
http://arxiv.org/pdf/1807.03513v3.pdf
|
https://github.com/Rujuta219/Ru219
| false | false | true |
tf
|
https://paperswithcode.com/paper/faster-r-cnn-towards-real-time-object
|
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
|
1506.01497
|
http://arxiv.org/abs/1506.01497v3
|
http://arxiv.org/pdf/1506.01497v3.pdf
|
https://github.com/sorg20/RPN
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/unpaired-image-to-image-translation-using
|
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
|
1703.10593
|
https://arxiv.org/abs/1703.10593v7
|
https://arxiv.org/pdf/1703.10593v7.pdf
|
https://github.com/yxue3357/CycleGAN_tf
| false | false | true |
tf
|
https://paperswithcode.com/paper/investigating-capsule-networks-with-dynamic
|
Investigating Capsule Networks with Dynamic Routing for Text Classification
|
1804.00538
|
http://arxiv.org/abs/1804.00538v4
|
http://arxiv.org/pdf/1804.00538v4.pdf
|
https://github.com/kevindeangeli/capsuleNetwork
| false | false | true |
tf
|
https://paperswithcode.com/paper/towards-scalable-and-reliable-capsule
|
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
|
1906.02829
|
https://arxiv.org/abs/1906.02829v1
|
https://arxiv.org/pdf/1906.02829v1.pdf
|
https://github.com/kevindeangeli/capsuleNetwork
| false | false | true |
tf
|
https://paperswithcode.com/paper/fast-dynamic-routing-based-on-weighted-kernel
|
Fast Dynamic Routing Based on Weighted Kernel Density Estimation
|
1805.10807
|
http://arxiv.org/abs/1805.10807v2
|
http://arxiv.org/pdf/1805.10807v2.pdf
|
https://github.com/kevindeangeli/capsuleNetwork
| false | false | true |
tf
|
https://paperswithcode.com/paper/discriminative-correlation-filter-with
|
Discriminative Correlation Filter with Channel and Spatial Reliability
|
1611.08461
|
http://arxiv.org/abs/1611.08461v3
|
http://arxiv.org/pdf/1611.08461v3.pdf
|
https://github.com/Eladamar/tracking
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/exemplar-based-open-set-panoptic-segmentation
|
Exemplar-Based Open-Set Panoptic Segmentation Network
|
2105.08336
|
https://arxiv.org/abs/2105.08336v2
|
https://arxiv.org/pdf/2105.08336v2.pdf
|
https://github.com/jd730/EOPSN
| true | false | false |
pytorch
|
https://paperswithcode.com/paper/automatic-detection-of-machine-generated-text
|
Automatic Detection of Machine Generated Text: A Critical Survey
|
2011.01314
|
https://arxiv.org/abs/2011.01314v1
|
https://arxiv.org/pdf/2011.01314v1.pdf
|
https://github.com/UBC-NLP/coling2020_machine_generated_text
| true | true | true |
none
|
https://paperswithcode.com/paper/meld-a-multimodal-multi-party-dataset-for
|
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
|
1810.02508
|
https://arxiv.org/abs/1810.02508v6
|
https://arxiv.org/pdf/1810.02508v6.pdf
|
https://github.com/AnuragBalsaraf/ERC
| false | false | true |
none
|
https://paperswithcode.com/paper/yolov4-optimal-speed-and-accuracy-of-object
|
YOLOv4: Optimal Speed and Accuracy of Object Detection
|
2004.10934
|
https://arxiv.org/abs/2004.10934v1
|
https://arxiv.org/pdf/2004.10934v1.pdf
|
https://github.com/meerkatai/Yolo-v4-Object-Detection-Classification
| false | false | true |
tf
|
https://paperswithcode.com/paper/yolov3-an-incremental-improvement
|
YOLOv3: An Incremental Improvement
|
1804.02767
|
http://arxiv.org/abs/1804.02767v1
|
http://arxiv.org/pdf/1804.02767v1.pdf
|
https://github.com/meerkatai/Yolo-v4-Object-Detection-Classification
| false | false | true |
tf
|
https://paperswithcode.com/paper/cspnet-a-new-backbone-that-can-enhance
|
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
|
1911.11929
|
https://arxiv.org/abs/1911.11929v1
|
https://arxiv.org/pdf/1911.11929v1.pdf
|
https://github.com/meerkatai/Yolo-v4-Object-Detection-Classification
| false | false | true |
tf
|
https://paperswithcode.com/paper/a-gaussian-process-upsampling-model-for
|
A Gaussian Process Upsampling Model for Improvements in Optical Character Recognition
|
2005.03780
|
https://arxiv.org/abs/2005.03780v1
|
https://arxiv.org/pdf/2005.03780v1.pdf
|
https://github.com/stevenireeves/GP_upsamp
| false | false | true |
none
|
https://paperswithcode.com/paper/a-fast-estimator-for-the-bispectrum-and
|
A fast estimator for the bispectrum and beyond - A practical method for measuring non-Gaussianity in 21-cm maps
|
1705.06284
|
https://arxiv.org/abs/1705.06284v2
|
https://arxiv.org/pdf/1705.06284v2.pdf
|
https://bitbucket.org/caw11/bifft
| true | false | false |
none
|
https://paperswithcode.com/paper/going-deeper-with-convolutions
|
Going Deeper with Convolutions
|
1409.4842
|
http://arxiv.org/abs/1409.4842v1
|
http://arxiv.org/pdf/1409.4842v1.pdf
|
https://github.com/bhuyanamit986/FlowerClassification
| false | false | true |
none
|
https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact
|
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
|
1602.07261
|
http://arxiv.org/abs/1602.07261v2
|
http://arxiv.org/pdf/1602.07261v2.pdf
|
https://github.com/bhuyanamit986/FlowerClassification
| false | false | true |
none
|
https://paperswithcode.com/paper/rethinking-the-inception-architecture-for
|
Rethinking the Inception Architecture for Computer Vision
|
1512.00567
|
http://arxiv.org/abs/1512.00567v3
|
http://arxiv.org/pdf/1512.00567v3.pdf
|
https://github.com/bhuyanamit986/FlowerClassification
| false | false | true |
none
|
https://paperswithcode.com/paper/rewriting-history-with-inverse-rl-hindsight
|
Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement
|
2002.11089
|
https://arxiv.org/abs/2002.11089v1
|
https://arxiv.org/pdf/2002.11089v1.pdf
|
https://github.com/Banmahhhh/HIPI-RL
| false | false | true |
pytorch
|
https://paperswithcode.com/paper/thompson-sampling-algorithms-for-mean
|
Thompson Sampling Algorithms for Mean-Variance Bandits
|
2002.00232
|
https://arxiv.org/abs/2002.00232v3
|
https://arxiv.org/pdf/2002.00232v3.pdf
|
https://github.com/maxqyzhu/TS_for_mean_variance_bandit
| false | false | true |
none
|
https://paperswithcode.com/paper/interpretable-selection-and-visualization-of
|
Interpretable Selection and Visualization of Features and Interactions Using Bayesian Forests
|
1506.02371
|
http://arxiv.org/abs/1506.02371v4
|
http://arxiv.org/pdf/1506.02371v4.pdf
|
https://github.com/vkrakovna/sbfc
| false | false | true |
none
|
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