Dataset condensation with contrastive signals
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 http://proceedings.mlr.press/v139/zhao21a/zhao21a.pdf
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 … WebTitle: Dataset Condensation with Contrastive Signals Authors: Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, Sungroh Yoon Abstract summary: gradient …
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 … WebRecent studies on dataset condensation attempt to reduce the dependence on such massive data by synthesizing a compact training dataset. However, the existing …
WebFeb 7, 2024 · Algorithm 1 Dataset condensation with contrastive signals. Figure 4 shows the NTK velocity during synthetic dataset optimization using DC and DCC on CIFAR-10. As … 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 …
WebJun 4, 2024 · Dataset Condensation with Contrastive Signals Recent studies have demonstrated that gradient matching-based dataset sy... 0 Saehyung Lee, et al. ∙. share ...
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 … floating pearl vase fillersWebTo 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 ... floating pearl string for water vasesWebFigure 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. floating pearls vase fillersWebProceedings of Machine Learning Research floating pebble poopWebCurrently, 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 ... great izuchi tail monster hunter riseWebJul 24, 2024 · Online Continual Learning with Contrastive Vision Transformer. Online continual learning (online CL) studies the problem of learning sequential tasks from an … floating pearls vase centerpiece picsWeb[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, … floating pendant mounting cables