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Overcoming challenges in leveraging GANs for few-shot data augmentation
Overcoming challenges in leveraging GANs for few-shot data augmentation
In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance. …
Workshop at the Conference on Lifelong Learning Agents (CoLLAs),  2022.
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A Closer Look at the Optimization Landscapes of Generative Adversarial Networks
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks
Generative adversarial networks have been very successful in generative modeling, however they remain relatively challenging to train …
International Conference on Learning Representations (ICLR),  2020.
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Reducing Noise in GAN Training with Variance Reduced Extragradient
Reducing Noise in GAN Training with Variance Reduced Extragradient
We study the effect of the stochastic gradient noise on the training of generative adversarial networks (GANs) and show that it can …
Conference on Neural Information Processing Systems (NeurIPS),  2019.
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