Summary of Rate-in: Information-driven Adaptive Dropout Rates For Improved Inference-time Uncertainty Estimation, by Tal Zeevi et al.
Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation
by Tal Zeevi, Ravid Shwartz-Ziv, Yann LeCun, Lawrence H. Staib, John A. Onofrey
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper tackles a critical issue in deploying neural networks in high-stakes applications like medical diagnosis: accurate uncertainty estimation. Neural networks are often used for tasks like image classification, and predicting the confidence of their predictions is crucial. Monte Carlo Dropout (MCD) is a popular method that approximates predictive uncertainty by performing multiple forward passes with random noise during inference. However, traditional MCD approaches use static dropout rates across all layers and inputs, which can lead to suboptimal uncertainty estimates as it fails to adapt to varying input characteristics. Existing methods optimize dropout rates during training using labeled data, resulting in fixed inference-time parameters that cannot adjust to new data distributions. This compromises uncertainty estimates in Monte Carlo simulations, making accurate risk assessment challenging. The proposed approach seeks to address this limitation by optimizing dropout rates for individual inputs and network layers, enabling better adaptation to changing input characteristics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure neural networks are reliable when they’re used for important decisions like medical diagnosis. Right now, there’s a way to get an idea of how certain the network is about its predictions called Monte Carlo Dropout (MCD). But the problem is that it uses the same “noise” levels everywhere and doesn’t adapt to different types of data. This can lead to poor estimates. What researchers have been doing is using labeled training data to adjust the noise levels, but this means they’re stuck with those settings forever. This is a big problem because it’s hard to predict how well the network will do on new, unseen data. The goal of this paper is to find a way to make MCD more flexible so that it can adapt to different types of data and give better uncertainty estimates. |
Keywords
» Artificial intelligence » Dropout » Image classification » Inference
