activity
20182022
most citedA Hypothesis for the Aesthetic Appreciation in Neural Networks

4 citations · 6 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

6 papers · 1 filter

cs.LG20221 cited

Shared Loss between Generators of GANs

Xin Wang

Generative adversarial networks are generative models that are capable of replicating the implicit probability distribution of the input data with high accuracy. Traditionally, GAN…

cs.LG2021

A Unified Game-Theoretic Interpretation of Adversarial Robustness

Jie Ren, Die Zhang, Yisen Wang +8

This paper provides a unified view to explain different adversarial attacks and defense methods, \emph{i.e.} the view of multi-order interactions between input variables of DNNs. B…

cs.LG2021

Interpreting Attributions and Interactions of Adversarial Attacks

Xin Wang, Shuyun Lin, Hao Zhang +2

This paper aims to explain adversarial attacks in terms of how adversarial perturbations contribute to the attacking task. We estimate attributions of different image regions to th…

cs.LG20214 cited

A Hypothesis for the Aesthetic Appreciation in Neural Networks

Xu Cheng, Xin Wang, Haotian Xue +2

This paper proposes a hypothesis for the aesthetic appreciation that aesthetic images make a neural network strengthen salient concepts and discard inessential concepts. In order t…

cs.LG2021

A Unified Game-Theoretic Interpretation of Adversarial Robustness

Jie Ren, Die Zhang, Yisen Wang +8

This paper provides a unified view to explain different adversarial attacks and defense methods, i.e. the view of multi-order interactions between input variables of DNNs. Based on…

cs.LG20181 cited

Conditional Graph Neural Processes: A Functional Autoencoder Approach

Marcel Nassar, Xin Wang, Evren Tumer

We introduce a novel encoder-decoder architecture to embed functional processes into latent vector spaces. This embedding can then be decoded to sample the encoded functions over a…