WebDataset Condensation With Contrastive Signals relevant information (e.g., logo, police sign, trailers) while suppressing task-irrelevant information (e.g., wheels, head … WebTo address this problem, we propose Dataset Condensation with Contrastive signals (DCC) by modifying the loss function to enable the DC methods to effectively capture the differences between classes. In addition, we analyze the new loss function in terms of training dynamics by tracking the kernel velocity. Furthermore, we introduce a bi-level ...

Dataset Condensation with Contrastive Signals - icml.cc

WebNon-Contrastive Unsupervised Learning of Physiological Signals from Video Jeremy Speth · Nathan Vance · Patrick Flynn · Adam Czajka High-resolution image reconstruction with latent diffusion models from human brain activity Yu Takagi · Shinji Nishimoto RIFormer: Keep Your Vision Backbone Effective But Removing Token Mixer WebConclusion •We show that DC primarily focuses on the class-wise gradient while overlooking contrastive signals. •To address this issue, we propose the Dataset Condensation with Contrastive signals (DCC) method. •In our experiments, we demonstrate that the proposed DCC outperforms DC in fine-grained classification tasks and general benchmark datasets dark walls with dark furniture

CVPR2024_玖138的博客-CSDN博客

WebDataset Condensation With Contrastive Signals lights, roads, trees). In our experiments on the fine-grained Automobile dataset, DC results in a classifier with a test accuracy … WebRecent studies on dataset condensation attempt to reduce the dependence on such massive data by synthesizing a compact training dataset. However, the existing … WebJan 29, 2024 · Photo by AJ Jean on Unsplash. The topic of data-efficient learning an important topic in Data Science and is an active area of research. Training large models … bishop watterson football 2021