3 accepted papers
While Diffusion Transformers (DiTs) have revolutionized high-fidelity image synthesis, the prohibitive computational costs of training at ultra-high resolutions necessitate robust inference-time extrapolation. Existing extrapolation methods typically operate under a *scale-agnostic* assumption, trea…
Dynamic data pruning accelerates deep learning by selectively omitting less informative samples during training. While per-sample loss is a common importance metric, obtaining it can be challenging or infeasible for complex models or loss functions, often requiring significant implementation effort.
Dynamic data pruning accelerates training by focusing on informative samples. However, comparing importance scores across different model states introduces inconsistency (score context drift), and variable selection rates bias gradient dynamics over time (temporal gradient bias). We introduce RePB (…