Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variational Resampling Based Assessment of Deep Neural Networks under
  Distribution Shift   A novel variational inference based resampling framework is proposed to
evaluate the robustness and generalization capability of deep learning models
with respect to distribution shift. We use Auto Encoding Variational Bayes to
find a latent representation of the data, on which a Variational Gaussian
Mixture Model is applied to deliberately create distribution shift by dividing
the dataset into different clusters. Wasserstein distance is used to
characterize the extent of distribution shift between the generated data
splits. We compare several popular Convolutional Neural Network (CNN)
architectures and Bayesian CNN models for image classification on the
Fashion-MNIST dataset, to assess their robustness and generalization behavior
under the deliberately created distribution shift, as well as under random
Cross Validation. Our method of creating artificial domain splits of a single
dataset can also be used to establish novel model selection criteria and
assessment tools in machine learning, as well as benchmark methods for domain
adaptation and domain generalization approaches.

30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat