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Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation

The implementation of Reducing Infromation Bottleneck for Weakly Supervised Semantic Segmentation, Jungbeom Lee, Jooyoung Choi, Jisoo Mok, and Sungroh Yoon, NeurIPS 2021. [[paper]]

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Abstract

Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus on a small discriminative region of the target object. We interpret this phenomenon using the information bottleneck principle: the final layer of a deep neural network, activated by the sigmoid or softmax activation functions, causes an information bottleneck, and as a result, only a subset of the task-relevant information is passed on to the output. We first support this argument through a

 

 

 

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