Summary of A Separability-based Approach to Quantifying Generalization: Which Layer Is Best?, by Luciano Dyballa et al.
A separability-based approach to quantifying generalization: which layer is best?
by Luciano Dyballa, Evan Gerritz, Steven W. Zucker
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 The abstract discusses the challenges in evaluating the ability of deep learning models to generalize to unseen data, particularly in open set scenarios. The authors propose a new method for assessing the capacity of networks to represent a sampled domain, even if they haven’t been trained on all classes within that domain. They fine-tune state-of-the-art pre-trained models for visual classification and then evaluate their performance on related but distinct variations of the same domain. The results show that high classification accuracy does not necessarily imply high generalizability, and deeper layers do not always generalize the best. This has implications for pruning and reveals the intrinsic capacity of different layers to generalize. The authors’ code is available at this URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models are great at doing certain tasks, but they often struggle when faced with new or unexpected data. One problem is that we don’t know how well these models will do on new information. The authors came up with a way to test how good these models are at handling new data. They took state-of-the-art pre-trained models and trained them for specific tasks, then tested how well they did on new but related data. They found some surprising things – just because a model is good at one task doesn’t mean it will be good at another. This matters because it means we need to think carefully about how we train our models if we want them to do well in real-world situations. |
Keywords
» Artificial intelligence » Classification » Deep learning » Pruning
