CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision NetworksDownload PDF

Published: 01 Feb 2023, 19:21, Last Modified: 02 Mar 2023, 11:16ICLR 2023 notable top 25%Readers: Everyone
Keywords: Interpretability, Explainability, Network Dissection
TL;DR: We propose an automated method for generating descriptions of the representation learned by hidden layer neurons, leveraging the multimodal CLIP-model.
Abstract: In this paper, we propose CLIP-Dissect, a new technique to automatically describe the function of individual hidden neurons inside vision networks. CLIP-Dissect leverages recent advances in multimodal vision/language models to label internal neurons with open-ended concepts without the need for any labeled data or human examples. We show that CLIP-Dissect provides more accurate descriptions than existing methods for last layer neurons where the ground-truth is available as well as qualitatively good descriptions for hidden layer neurons. In addition, our method is very flexible: it is model agnostic, can easily handle new concepts and can be extended to take advantage of better multimodal models in the future. Finally CLIP-Dissect is computationally efficient and can label all neurons from five layers of ResNet-50 in just 4 minutes, which is more than 10$\times$ faster than existing methods. Our code is available at
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