Summary of Flexible Variational Information Bottleneck: Achieving Diverse Compression with a Single Training, by Sota Kudo et al.
Flexible Variational Information Bottleneck: Achieving Diverse Compression with a Single Training
by Sota Kudo, Naoaki Ono, Shigehiko Kanaya, Ming Huang
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
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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 Information Bottleneck (IB) framework is a widely used method for extracting information related to a target random variable from a source random variable. IB controls the trade-off between data compression and predictiveness through the Lagrange multiplier β. Traditionally, finding the optimal value of β requires computationally expensive search processes across multiple training cycles. In this study, we introduce Flexible Variational Information Bottleneck (FVIB), a framework for classification tasks that can learn optimal models for all values of β in a single, efficient training process. We theoretically demonstrate that FVIB can simultaneously maximize an approximation of the VIB objective function, outperforming traditional IB methods. Empirically, we show that FVIB achieves similar performance to VIB while offering improved calibration performance and continuous optimization of β. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about a new way to use a tool called Information Bottleneck (IB) for machine learning tasks like classification. Traditionally, finding the right balance between compressing data and making good predictions with IB takes a lot of computational effort. The researchers introduce a new approach called Flexible Variational Information Bottleneck (FVIB), which can do this balancing act in just one training process, making it more efficient. They show that FVIB works as well or better than the traditional way, and even improves on calibration performance. |
Keywords
* Artificial intelligence * Classification * Machine learning * Objective function * Optimization
