Summary of Interpreting Neurons in Deep Vision Networks with Language Models, by Nicholas Bai et al.
Interpreting Neurons in Deep Vision Networks with Language Models
by Nicholas Bai, Rahul A. Iyer, Tuomas Oikarinen, Akshay Kulkarni, Tsui-Wei Weng
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel method called Describe-and-Dissect (DnD) is proposed to analyze the roles of hidden neurons in vision networks. DnD leverages multimodal deep learning advancements to generate complex natural language descriptions without requiring labeled training data or predefined concepts. The approach is also training-free, allowing for easy integration with more capable general-purpose models. Qualitative and quantitative evaluations demonstrate that DnD outperforms prior work by providing higher-quality neuron descriptions. On average, the method provides the highest quality labels and is over 2x more likely to be selected as the best explanation compared to the best baseline. A use case illustrates the potential of DnD in land cover prediction models for sustainability applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand what a tiny part of a computer’s brain does, without knowing how it works or what it means. That’s kind of like what scientists do when they want to figure out what hidden “neurons” are doing in artificial intelligence systems. In this research, a new way is proposed to describe these neurons using natural language, like words. This method doesn’t require special training data and can use powerful computer models that already exist. The results show that this approach works better than previous methods at explaining neuron behaviors. An example of how this could be useful is in predicting land cover types for sustainability projects. |
Keywords
* Artificial intelligence * Deep learning
