Summary of How Many Views Does Your Deep Neural Network Use For Prediction?, by Keisuke Kawano and Takuro Kutsuna and Keisuke Sano
How many views does your deep neural network use for prediction?
by Keisuke Kawano, Takuro Kutsuna, Keisuke Sano
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 In this research paper, the authors investigate the generalization abilities of Deep Neural Networks (DNNs). Despite previous studies, the understanding of DNNs’ generalization is still incomplete. The authors propose Minimal Sufficient Views (MSVs), a novel approach to explain DNNs’ generalization. MSVs are distinct features in an input that preserve a model’s prediction for that input. Empirical results show a clear relationship between the number of MSVs and prediction accuracy, suggesting that a multi-view perspective is crucial for understanding non-ensemble or non-distilled DNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores how well Deep Neural Networks (DNNs) work on new data they haven’t seen before. Despite lots of studies, we still don’t fully understand why DNNs are good at this. The authors introduce a new idea called Minimal Sufficient Views (MSVs), which helps explain why DNNs work the way they do. MSVs are like special filters that help us understand how a DNN makes predictions about a picture. The results show that the more MSVs we have, the better the DNN is at making accurate predictions. |
Keywords
* Artificial intelligence * Generalization
