Yogesh Balaji | DeepAI

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∙ 11/02/2022

eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

Large-scale diffusion-based generative models have led to breakthroughs ...

0 Yogesh Balaji, et al. ∙

∙ 01/26/2022

A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes

While datasets with single-label supervision have propelled rapid advanc...

3 Mazda Moayeri, et al. ∙

∙ 04/12/2021

Understanding Overparameterization in Generative Adversarial Networks

A broad class of unsupervised deep learning methods such as Generative A...

0 Yogesh Balaji, et al. ∙

∙ 10/12/2020

Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation

Optimal Transport (OT) distances such as Wasserstein have been used in s...

0 Yogesh Balaji, et al. ∙

∙ 10/06/2020

The Effectiveness of Memory Replay in Large Scale Continual Learning

We study continual learning in the large scale setting where tasks in th...

0 Yogesh Balaji, et al. ∙

∙ 10/05/2020

Winning Lottery Tickets in Deep Generative Models

The lottery ticket hypothesis suggests that sparse, sub-networks of a gi...

0 Neha Mukund Kalibhat, et al. ∙

∙ 08/28/2020

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

We introduce Domain-specific Masks for Generalization, a model for impro...

0 Prithvijit Chattopadhyay, et al. ∙

∙ 07/02/2020

Curriculum Manager for Source Selection in Multi-Source Domain Adaptation

The performance of Multi-Source Unsupervised Domain Adaptation depends s...

0 Luyu Yang, et al. ∙

∙ 11/23/2019

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

Inferring the latent variable generating a given test sample is a challe...

33 Wei-An Lin, et al. ∙

∙ 11/20/2019

Adversarial Robustness of Flow-Based Generative Models

Flow-based generative models leverage invertible generator functions to ...

0 Phillip Pope, et al. ∙

∙ 10/17/2019

Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets

Adversarial training is by far the most successful strategy for improvin...

13 Yogesh Balaji, et al. ∙

∙ 02/01/2019

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

Understanding proper distance measures between distributions is at the c...

0 Yogesh Balaji, et al. ∙

∙ 10/09/2018

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

Building on the success of deep learning, two modern approaches to learn...

10 Yogesh Balaji, et al. ∙

∙ 11/19/2017

Unsupervised Domain Adaptation for Semantic Segmentation with GANs

Visual Domain Adaptation is a problem of immense importance in computer ...

0 Swami Sankaranarayanan, et al. ∙

∙ 04/06/2017

Generate To Adapt: Aligning Domains using Generative Adversarial Networks

Domain Adaptation is an actively researched problem in Computer Vision. ...

0 Swami Sankaranarayanan, et al. ∙

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