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cs.CV2026
Beyond Loss Values: Robust Dynamic Pruning via Loss Trajectory Alignment
Huaiyuan Qin, Muli Yang, Gabriel James Goenawan +5
Existing dynamic data pruning methods often fail under noisy-label settings, as they typically rely on per-sample loss as the ranking criterion. This could mistakenly lead to prese…
cs.CV2025
Conditional Representation Learning for Customized Tasks
Honglin Liu, Chao Sun, Peng Hu +2
Conventional representation learning methods learn a universal representation that primarily captures dominant semantics, which may not always align with customized downstream task…
cs.CV2025
DUDE: Diffusion-Based Unsupervised Cross-Domain Image Retrieval
Ruohong Yang, Peng Hu, Yunfan Li +1
Unsupervised cross-domain image retrieval (UCIR) aims to retrieve images of the same category across diverse domains without relying on annotations. Existing UCIR methods, which al…