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Found 3 papers, 1 papers with code
Augmentation-Aware Self-Supervision for Data-Efficient GAN Training
The invariance may degrade the representation learning ability of the discriminator, thereby affecting the generative modeling performance of the generator.
Fine-tuning Pruned Networks with Linear Over-parameterization
To address this challenge, we propose a novel method that first linearly over-parameterizes the compact layers in pruned networks to enlarge the number of fine-tuning parameters and then re-parameterizes them to the original layers after fine-tuning.
Conditional GANs with Auxiliary Discriminative Classifier
Specifically, the proposed auxiliary discriminative classifier becomes generator-aware by recognizing the class-labels of the real data and the generated data discriminatively.
Ranked #1 on
Conditional Image Generation
on Tiny ImageNet
