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Found 5 papers, 1 papers with code
MissDiff: Training Diffusion Models on Tabular Data with Missing Values
The diffusion model has shown remarkable performance in modeling data distributions and synthesizing data.
Improving Adversarial Robustness by Contrastive Guided Diffusion Process
Among various deep generative models, the diffusion model has been shown to produce high-quality synthetic images and has achieved good performance in improving the adversarial robustness.
Attention Enables Zero Approximation Error
In this work, we show that with suitable adaptations, the single-head self-attention transformer with a fixed number of transformer encoder blocks and free parameters is able to generate any desired polynomial of the input with no error.
Generalizing to Unseen Domains: A Survey on Domain Generalization
Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain.
Robust Learning with Frequency Domain Regularization
We demonstrate the effectiveness of our regularization by (1) defensing to adversarial perturbations; (2) reducing the generalization gap in different architecture; (3) improving the generalization ability in transfer learning scenario without fine-tune.
