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Ayush Jaiswal

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3 papers

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AAAI Conference 2023 Conference Paper

User-Controllable Arbitrary Style Transfer via Entropy Regularization

Jiaxin Cheng
Yue Wu
Ayush Jaiswal
Xu Zhang
Pradeep Natarajan
Prem Natarajan

Ensuring the overall end-user experience is a challenging task in arbitrary style transfer (AST) due to the subjective nature of style transfer quality. A good practice is to provide users many instead of one AST result. However, existing approaches require to run multiple AST models or inference a diversified AST (DAST) solution multiple times, and thus they are either slow in speed or limited in diversity. In this paper, we propose a novel solution ensuring both efficiency and diversity for generating multiple user-controllable AST results by systematically modulating AST behavior at run-time. We begin with reformulating three prominent AST methods into a unified assign-and-mix problem and discover that the entropies of their assignment matrices exhibit a large variance. We then solve the unified problem in an optimal transport framework using the Sinkhorn-Knopp algorithm with a user input ε to control the said entropy and thus modulate stylization. Empirical results demonstrate the superiority of the proposed solution, with speed and stylization quality comparable to or better than existing AST and significantly more diverse than previous DAST works. Code is available at https://github.com/cplusx/eps-Assign-and-Mix.

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AAAI Conference 2020 Conference Paper

Invariant Representations through Adversarial Forgetting

Ayush Jaiswal
Daniel Moyer
Greg Ver Steeg
Wael AbdAlmageed
Premkumar Natarajan

We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechanism. We show that the forgetting mechanism serves as an information-bottleneck, which is manipulated by the adversarial training to learn invariance to unwanted factors. Empirical results show that the proposed framework achieves stateof-the-art performance at learning invariance in both nuisance and bias settings on a diverse collection of datasets and tasks.

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NeurIPS Conference 2018 Conference Paper

Unsupervised Adversarial Invariance

Ayush Jaiswal
Rex Yue Wu
Wael Abd-Almageed
Prem Natarajan

Data representations that contain all the information about target variables but are invariant to nuisance factors benefit supervised learning algorithms by preventing them from learning associations between these factors and the targets, thus reducing overfitting. We present a novel unsupervised invariance induction framework for neural networks that learns a split representation of data through competitive training between the prediction task and a reconstruction task coupled with disentanglement, without needing any labeled information about nuisance factors or domain knowledge. We describe an adversarial instantiation of this framework and provide analysis of its working. Our unsupervised model outperforms state-of-the-art methods, which are supervised, at inducing invariance to inherent nuisance factors, effectively using synthetic data augmentation to learn invariance, and domain adaptation. Our method can be applied to any prediction task, eg. , binary/multi-class classification or regression, without loss of generality.

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