Dataset condensation with contrastive signals

Author: avlm

August undefined, 2024

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 ... 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)...

ICML 2024

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 ... WebProceedings of Machine Learning Research citi human research basic course

ICML 2024

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 ... 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 … 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 citihub hotel malang

Dataset Condensation with Contrastive Signals

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: ... 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, … diashow tiereWebConclusion •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 diashow tierbilder

"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 … " - Dataset condensation with contrastive signals

Dataset condensation with contrastive signals

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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 … WebTitle: Dataset Condensation with Contrastive Signals Authors: Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, Sungroh Yoon Abstract summary: gradient …

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WebDataset Condensation with Contrastive Signals. Contribute to Daankrol/DCC development by creating an account on GitHub. WebDataset Condensation With Contrastive Signals relevant information (e.g., logo, police sign, trailers) while suppressing task-irrelevant information (e.g., wheels, head …

WebVenues OpenReview 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 ...

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 … 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.

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-

WebApr 15, 2024 · Condensing Graphs via One-Step Gradient Matching. However, existing approaches have their inherent limitations: (1) they are not directly applicable to … diashow testsiegerWebDataset 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 … citi human research protections trainingWebJun 4, 2024 · Dataset Condensation with Contrastive Signals Recent studies have demonstrated that gradient matching-based dataset sy... 0 Saehyung Lee, et al. ∙. share ... diashow toolWebFeb 7, 2024 · Dataset Condensation with Contrastive Signals. Recent studies have demonstrated that gradient matching-based dataset synthesis, or dataset condensation … citi human research loginWebDataset Condensation With Contrastive Signals relevant information (e.g., logo, police sign, trailers) while suppressing task-irrelevant information (e.g., wheels, head … citi how to change last nameWebFeb 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 … diashow ultimateWebTo 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. citi human research course