Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial-Based Knowledge Distillation for Multi-Model Ensemble and
  Noisy Data Refinement   Generic Image recognition is a fundamental and fairly important visual
problem in computer vision. One of the major challenges of this task lies in
the fact that single image usually has multiple objects inside while the labels
are still one-hot, another one is noisy and sometimes missing labels when
annotated by humans. In this paper, we focus on tackling these challenges
accompanying with two different image recognition problems: multi-model
ensemble and noisy data recognition with a unified framework. As is well-known,
usually the best performing deep neural models are ensembles of multiple
base-level networks, as it can mitigate the variation or noise containing in
the dataset. Unfortunately, the space required to store these many networks,
and the time required to execute them at runtime, prohibit their use in
applications where test sets are large (e.g., ImageNet). In this paper, we
present a method for compressing large, complex trained ensembles into a single
network, where the knowledge from a variety of trained deep neural networks
(DNNs) is distilled and transferred to a single DNN. In order to distill
diverse knowledge from different trained (teacher) models, we propose to use
adversarial-based learning strategy where we define a block-wise training loss
to guide and optimize the predefined student network to recover the knowledge
in teacher models, and to promote the discriminator network to distinguish
teacher vs. student features simultaneously. Extensive experiments on
CIFAR-10/100, SVHN, ImageNet and iMaterialist Challenge Dataset demonstrate the
effectiveness of our MEAL method. On ImageNet, our ResNet-50 based MEAL
achieves top-1/5 21.79%/5.99% val error, which outperforms the original model
by 2.06%/1.14%. On iMaterialist Challenge Dataset, our MEAL obtains a
remarkable improvement of top-3 1.15% (official evaluation metric) on a strong
baseline model of ResNet-101.

17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc