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Found 6 papers, 3 papers with code
Learnability and Complexity of Quantum Samples
Given a quantum circuit, a quantum computer can sample the output distribution exponentially faster in the number of bits than classical computers.
Image Augmentations for GAN Training
We provide new state-of-the-art results for conditional generation on CIFAR-10 with both consistency loss and contrastive loss as additional regularizations.
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples
We introduce a simple (one line of code) modification to the Generative Adversarial Network (GAN) training algorithm that materially improves results with no increase in computational cost: When updating the generator parameters, we simply zero out the gradient contributions from the elements of the batch that the critic scores as `least realistic'.
Improved Consistency Regularization for GANs
Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator.
Improving Differentially Private Models with Active Learning
Broad adoption of machine learning techniques has increased privacy concerns for models trained on sensitive data such as medical records.
Generating Natural Adversarial Examples
Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed.
