Dataset condensation with contrastive signals
WebRecent studies on dataset condensation attempt to reduce the dependence on such massive data by synthesizing a compact training dataset. However, the existing … 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
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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 … Web[24]Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, Sungroh Yoon, \Dataset Condensation with Contrastive Signals", International Conference on Machine Learning (ICML), 2024. ... IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024 [7]Sangdoo Yun, Dongyoon Han, Seong Joon Oh, …
WebFeb 7, 2024 · To address this problem, we propose Dataset Condensation with Contrastive signals (DCC) by modifying the loss function to enable the DC methods to … WebDataset Condensation with Contrastive Signals (Saehyung Lee et al., ICML 2024) 📖 Delving into Effective Gradient Matching for Dataset Condensation (Zixuan Jiang et al., 2024) 📖 Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory (Justin Cui et …
WebFeb 6, 2024 · To address this problem, we propose Dataset Condensation with Contrastive signals (DCC) by modifying the loss function to enable the DC methods to … WebSep 28, 2024 · This paper proposes a training set synthesis technique for data-efficient learning, called Dataset Condensation, that learns to condense large dataset into a …
WebSep 12, 2024 · In this work, we analyse the contrastive fine-tuning of pre-trained language models on two fine-grained text classification tasks, emotion classification and sentiment analysis. We adaptively embed class relationships into a contrastive objective function to help differently weigh the positives and negatives, and in particular, weighting ...
WebDataset Condensation with Contrastive Signals Recent studies have demonstrated that gradient matching-based dataset sy... 0 Saehyung Lee, et al. ∙ share research ∙ 2 years ago Removing Undesirable Feature Contributions Using Out-of-Distribution Data Several data augmentation methods deploy unlabeled-in-distribution (UID)... poraclejs githubWebTo 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 ... por 15 where to buyWebFeb 7, 2024 · To 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. sharon schroff kellyWebHa, Hyun Oh Song, "Dataset Condensation via Efficient Synthetic-Data Parameterization", Interna-tional Conference on Machine Learning (ICML 2024), 2024. … sharon schwedaWebDataset Condensation With Contrastive Signals relevant information (e.g., logo, police sign, trailers) while suppressing task-irrelevant information (e.g., wheels, head … porachunek onlineWebTo 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. sharon schultz facebookWebConclusion •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 sharons christmas cake