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Unsupervised object category discovery via information bottleneck method
Proceedings of the 18th ACM international conference on Multimedia, 2010We present a novel approach to automatically discover object categories from a collection of unlabeled images. This is achieved by the Information Bottleneck method, which finds the optimal partitioning of the image collection by maximally preserving the relevant information with respect to the latent semantic residing in the image contents.
Zhengzheng Lou, Yangdong Ye, Dong Liu
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The information bottleneck method in communications
2020Die Information Bottleneck Methode ist ein informationstheoretisches Verfahren, welches auf die Kompression einer beobachteten Zufallsvariable abzielt. Das wichtigste Ziel dieser Kompression ist die Erhaltung sogenannter relevanter Information. Die Methode hat ihren Ursprung im Maschinenlernen und bisher existieren nur wenige Anwendungen der Methode in
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Determine the Optimal Parameter for Information Bottleneck Method
2006A natural question in Information Bottleneck method is how many "groups" are appropriate. The dependency on prior knowledge restricts the applications of many Information Bottleneck algorithms. In this paper we aim to remove this dependency by formulating the parameter choosing as a model selection problem, and solve it using the minimum message length
Gang Li, Dong Liu, Yangdong Ye, Jia Rong
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IEEE Transactions on Information Forensics and Security
Automatic Modulation Classification (AMC) is crucial for monitoring the legitimacy of user frequency behavior and identifying potential sources of interference in spectrum monitoring.
Sicheng Zhang +4 more
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Automatic Modulation Classification (AMC) is crucial for monitoring the legitimacy of user frequency behavior and identifying potential sources of interference in spectrum monitoring.
Sicheng Zhang +4 more
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Towards Universal AI-Generated Image Detection by Variational Information Bottleneck Network
Computer Vision and Pattern RecognitionThe rapid advancement of generative models has significantly improved the quality of generated images. Mean-while, it challenges information authenticity and credibility.
Haifeng Zhang +5 more
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Revisiting LLM Reasoning via Information Bottleneck
arXiv.orgLarge language models (LLMs) have recently demonstrated remarkable progress in reasoning capabilities through reinforcement learning with verifiable rewards (RLVR).
Shiye Lei +3 more
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IEEE Transactions on Networking
Collaborative perception systems leverage multiple edge devices, such as surveillance cameras or autonomous cars, to enhance sensing quality and eliminate blind spots.
Zhengru Fang +5 more
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Collaborative perception systems leverage multiple edge devices, such as surveillance cameras or autonomous cars, to enhance sensing quality and eliminate blind spots.
Zhengru Fang +5 more
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Finding the Optimal Cardinality Value for Information Bottleneck Method
2006Information Bottleneck method can be used as a dimensionality reduction approach by grouping “similar” features together [1]. In application, a natural question is how many “features groups” will be appropriate. The dependency on prior knowledge restricts the applications of many Information Bottleneck algorithms.
Gang Li +3 more
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Information Fusion
eliminate the heterogeneity among modalities. Thirdly, we introduce graph contrastive representation learning to capture intra-modal and inter-modal complementary semantic information and learn intra-class and inter-class boundary information of emotion ...
Yuntao Shou +5 more
semanticscholar +1 more source
eliminate the heterogeneity among modalities. Thirdly, we introduce graph contrastive representation learning to capture intra-modal and inter-modal complementary semantic information and learn intra-class and inter-class boundary information of emotion ...
Yuntao Shou +5 more
semanticscholar +1 more source
Transformer Fault Diagnosis Algorithm Based on Entropy-Weighting Information Bottleneck Method
2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 2015The paper presents an algorithm for DGA (Dissolved Gas Analysis) fault diagnosis based on the Information Bottleneck (IB) method by introducing the train data supervision after IB clustering. The classified label of the test data is marked by voting among all these train data which exist in the same cluster with the certain test data.
Hong Xing Lu, Yang Dong Ye, Gang Chen
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