Dataset condensation with contrastive signals
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 … 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 ...
Dataset condensation with contrastive signals
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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- 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
WebTitle: Dataset Condensation with Contrastive Signals Authors: Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, Sungroh Yoon Abstract summary: gradient … WebJun 4, 2024 · Dataset Condensation with Contrastive Signals Recent studies have demonstrated that gradient matching-based dataset sy... 0 Saehyung Lee, et al. ∙. share ...
http://proceedings.mlr.press/v139/zhao21a/zhao21a.pdf 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.
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 …
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. biscoff caramel cakeWebProceedings of Machine Learning Research biscoff cheesecake pinch of nomWebTo 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 ... dark brown ladies slacksWebTo 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 … dark brown kitchen tileWebSpotlight 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: ... biscoff cereal aldiWebSep 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 … dark brown lady bugsWebNon-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 biscoff celebration cake