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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 ...
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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...
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04/12/2021
Understanding Overparameterization in Generative Adversarial Networks
A broad class of unsupervised deep learning methods such as Generative A...
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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...
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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...
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10/05/2020
Winning Lottery Tickets in Deep Generative Models
The lottery ticket hypothesis suggests that sparse, sub-networks of a gi...
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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...
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07/02/2020
Curriculum Manager for Source Selection in Multi-Source Domain Adaptation
The performance of Multi-Source Unsupervised Domain Adaptation depends s...
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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...
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11/20/2019
Adversarial Robustness of Flow-Based Generative Models
Flow-based generative models leverage invertible generator functions to ...
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10/17/2019
Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets
Adversarial training is by far the most successful strategy for improvin...
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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...
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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...
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11/19/2017
Unsupervised Domain Adaptation for Semantic Segmentation with GANs
Visual Domain Adaptation is a problem of immense importance in computer ...
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04/06/2017