WebCurrently, he works as the head of NAVER AI Lab in NAVER Cloud. He has contributed to the AI research community as Datasets and Benchmarks Co-chair for NeurIPS and Social Co-chair for ICML 2024 and NeurIPS 2024. Also, he has joined a senior technical program committee member, such as, Area chair for NeurIPS 2024 and 2024, Area chair for ICML ... 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 (11%) lower than that achieved using the random selection method (12.2%). We demonstrate that DC cannot effectively utilize the contrastive signals of interclass sam-

Dataset Condensation with Contrastive Signals - icml.cc

WebFeb 7, 2024 · Dataset Condensation with Contrastive Signals. Recent studies have demonstrated that gradient matching-based dataset synthesis, or dataset condensation … 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 ... bonfe\u0027s plumbing heating

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WebHa, Hyun Oh Song, "Dataset Condensation via Efficient Synthetic-Data Parameterization", Interna-tional Conference on Machine Learning (ICML 2024), 2024. … WebDataset Condensation with Contrastive Signals. Contribute to Daankrol/DCC development by creating an account on GitHub. 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 bonfe water heater installation