223 citations · 409 across the 18 of their papers we have counts for
10 papers · 1 filter
Understanding Overparameterization in Generative Adversarial Networks
Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat +4
A broad class of unsupervised deep learning methods such as Generative Adversarial Networks (GANs) involve training of overparameterized models where the number of parameters of th…
Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation
Yogesh Balaji, Rama Chellappa, Soheil Feizi
Optimal Transport (OT) distances such as Wasserstein have been used in several areas such as GANs and domain adaptation. OT, however, is very sensitive to outliers (samples with la…
The Effectiveness of Memory Replay in Large Scale Continual Learning
Yogesh Balaji, Mehrdad Farajtabar, Dong Yin +2
We study continual learning in the large scale setting where tasks in the input sequence are not limited to classification, and the outputs can be of high dimension. Among multiple…
Winning Lottery Tickets in Deep Generative Models
Neha Mukund Kalibhat, Yogesh Balaji, Soheil Feizi
The lottery ticket hypothesis suggests that sparse, sub-networks of a given neural network, if initialized properly, can be trained to reach comparable or even better performance t…
Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders
Gowthami Somepalli, Yexin Wu, Yogesh Balaji +2
Detecting out of distribution (OOD) samples is of paramount importance in all Machine Learning applications. Deep generative modeling has emerged as a dominant paradigm to model co…
Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference
Wei-An Lin, Yogesh Balaji, Pouya Samangouei +1
Inferring the latent variable generating a given test sample is a challenging problem in Generative Adversarial Networks (GANs). In this paper, we propose InvGAN - a novel framewor…
Adversarial Robustness of Flow-Based Generative Models
Phillip Pope, Yogesh Balaji, Soheil Feizi
Flow-based generative models leverage invertible generator functions to fit a distribution to the training data using maximum likelihood. Despite their use in several application d…
Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets
Yogesh Balaji, Tom Goldstein, Judy Hoffman
Adversarial training is by far the most successful strategy for improving robustness of neural networks to adversarial attacks. Despite its success as a defense mechanism, adversar…
Normalized Wasserstein Distance for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation
Yogesh Balaji, Rama Chellappa, Soheil Feizi
Understanding proper distance measures between distributions is at the core of several learning tasks such as generative models, domain adaptation, clustering, etc. In this work, w…
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
Yogesh Balaji, Hamed Hassani, Rama Chellappa +1
Building on the success of deep learning, two modern approaches to learn a probability model from the data are Generative Adversarial Networks (GANs) and Variational AutoEncoders (…