Search Results for author: Yogesh Balaji

Found 19 papers, 8 papers with code

Understanding Overparameterization in Generative Adversarial Networks

no code implementations12 Apr 2021 Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi

We also empirically study the role of model overparameterization in GANs using several large-scale experiments on CIFAR-10 and Celeb-A datasets.

Analyzing Attention Mechanisms through Lens of Sample Complexity and Loss Landscape

no code implementations1 Jan 2021 Bingyuan Liu, Yogesh Balaji, Lingzhou Xue, Martin Renqiang Min

Attention mechanisms have advanced state-of-the-art deep learning models in many machine learning tasks.

Understanding Over-parameterization in Generative Adversarial Networks

no code implementations ICLR 2021 Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi

In this work, we present a comprehensive analysis of the importance of model over-parameterization in GANs both theoretically and empirically.

Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation

1 code implementation NeurIPS 2020 Yogesh Balaji, Rama Chellappa, Soheil Feizi

To remedy this issue, robust formulations of OT with unbalanced marginal constraints have previously been proposed.

Domain Adaptation

Winning Lottery Tickets in Deep Generative Models

1 code implementation5 Oct 2020 Neha Mukund Kalibhat, Yogesh Balaji, Soheil Feizi

In this paper, we confirm the existence of winning tickets in deep generative models such as GANs and VAEs.

Learning to Balance Specificity and Invariance for In and Out of Domain Generalization

1 code implementation ECCV 2020 Prithvijit Chattopadhyay, Yogesh Balaji, Judy Hoffman

For domain generalization, the goal is to learn from a set of source domains to produce a single model that will best generalize to an unseen target domain.

Domain Generalization

Curriculum Manager for Source Selection in Multi-Source Domain Adaptation

no code implementations ECCV 2020 Luyu Yang, Yogesh Balaji, Ser-Nam Lim, Abhinav Shrivastava

In this paper, we proposed an adversarial agent that learns a dynamic curriculum for source samples, called Curriculum Manager for Source Selection (CMSS).

Multi-Source Unsupervised Domain Adaptation Unsupervised Domain Adaptation

Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference

no code implementations23 Nov 2019 Wei-An Lin, Yogesh Balaji, Pouya Samangouei, Rama Chellappa

Additionally, we show how InvGAN can be used to implement reparameterization white-box attacks on projection-based defense mechanisms.

Adversarial Robustness of Flow-Based Generative Models

no code implementations20 Nov 2019 Phillip Pope, Yogesh Balaji, Soheil Feizi

Finally, using a hybrid adversarial training procedure, we significantly boost the robustness of these generative models.

Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets

1 code implementation17 Oct 2019 Yogesh Balaji, Tom Goldstein, Judy Hoffman

Adversarial training is by far the most successful strategy for improving robustness of neural networks to adversarial attacks.

Normalized Wasserstein for Mixture Distributions With Applications in Adversarial Learning and Domain Adaptation

no code implementations ICCV 2019 Yogesh Balaji, Rama Chellappa, Soheil Feizi

Using the proposed normalized Wasserstein measure leads to significant performance gains for mixture distributions with imbalanced mixture proportions compared to the vanilla Wasserstein distance.

Domain Adaptation

Normalized Wasserstein Distance for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation

1 code implementation1 Feb 2019 Yogesh Balaji, Rama Chellappa, Soheil Feizi

Using the proposed normalized Wasserstein measure leads to significant performance gains for mixture distributions with imbalanced mixture proportions compared to the vanilla Wasserstein distance.

Domain Adaptation

Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs

1 code implementation ICLR 2019 Yogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi

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 (VAEs).

Unrolling the Shutter: CNN to Correct Motion Distortions

no code implementations CVPR 2017 Vijay Rengarajan, Yogesh Balaji, A. N. Rajagopalan

Our single-image correction method fares well even operating in a frame-by-frame manner against video-based methods and performs better than scene-specific correction schemes even under challenging situations.

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