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Adversarial Self-Training Improves Robustness and Generalization for Gradual Domain Adaptation
Empirically, the gradual self-training method greatly improves the target domain's accuracy compared
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning
Tyler Scott, Karl Ridgeway, Michael C. Mozer
We conduct a systematic comparison of methods in a variety of domains, varying thenumber oflabeled instances available inthetargetdomain (k), as well as the number of target-domain classes.
U-DiTs: Downsample Tokens in U-Shaped Diffusion Transformers
DiTs (U-DiTs) in the paper and conduct extensive experiments to demonstrate the extraordinary performance of U-DiT models.
Online Non-convex Learning in Dynamic Environments
In this research, we examine dynamic environments and choose dynamic regret and adaptive regret to measure the performance.
Inherent Weight Normalization in Stochastic Neural Networks
Georgios Detorakis, Sourav Dutta, Abhishek Khanna, Matthew Jerry, Suman Datta, Emre Neftci
Neural Information Processing Systems http://nips.cc/
The Many Faces of Optimal Weak-to-Strong Learning
's are real-valued weights depending on the accuracy of h
