Dataset condensation with contrastive signals
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 … 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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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)... 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.
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. Furthermore, we introduce a bi-level ...
WebVenues OpenReview WebFeb 7, 2024 · This study proposes Dataset Condensation with Contrastive signals (DCC) by modifying the loss function to enable the DC methods to effectively capture the …
WebProceedings of Machine Learning Research
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 ipod touch nano 7th generationipod touch nano caseWebFeb 7, 2024 · Dataset Condensation with Contrastive Signals. Recent studies have demonstrated that gradient matching-based dataset synthesis, or dataset condensation … ipod touch newest modelWebFeb 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 … orbit lounger coverWebApr 15, 2024 · Condensing Graphs via One-Step Gradient Matching. However, existing approaches have their inherent limitations: (1) they are not directly applicable to … ipod touch online kopenWebSep 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 ... orbit lounger cushion replacementWeboverlooking contrastive signals. •To address this issue, we propose the Dataset Condensation with Contrastive signals (DCC) method. •In our experiments, we … orbit machinery movers