Dataset condensation with contrastive signals
WebJun 4, 2024 · Dataset Condensation with Contrastive Signals Recent studies have demonstrated that gradient matching-based dataset sy... 0 Saehyung Lee, et al. ∙. share ... WebProceedings of Machine Learning Research
Dataset condensation with contrastive signals
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WebApr 15, 2024 · Condensing Graphs via One-Step Gradient Matching. However, existing approaches have their inherent limitations: (1) they are not directly applicable to … WebFigure 1: Dataset Condensation (left) aims to generate a small set of synthetic images that can match the performance of a network trained on a large image dataset. Our method (right) realizes this goal by learning a synthetic set such that a deep network trained on it and the large set produces similar gradients w.r.t. its weights.
WebSpotlight 08:20 Dataset Condensation via Efficient Synthetic-Data Parameterization. ... Spotlight 08:35 Dataset Condensation with Contrastive Signals. Saehyung Lee · Sanghyuk Chun · Sangwon Jung · Sangdoo Yun · Sungroh Yoon. Poster 15:00 Blurs Behave Like Ensembles: ... WebRecent studies on dataset condensation attempt to reduce the dependence on such massive data by synthesizing a compact training dataset. However, the existing …
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 … 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.
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 …
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 effectively capture the differences between classes. In addition, we analyze the new loss function in terms of training dynamics by tracking the kernel velocity. cistern\\u0027s g6WebFeb 7, 2024 · Dataset Condensation with Contrastive Signals. Recent studies have demonstrated that gradient matching-based dataset synthesis, or dataset condensation … diamond wisemanWebDataset 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)... diamond wire toolWebSep 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 ... cistern\u0027s gdWebTo 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 ... diamond wire wheelWebNon-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 cistern\\u0027s ggWebDataset 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- cistern\\u0027s ge