Summary of Calibration Of Ordinal Regression Networks, by Daehwan Kim et al.
Calibration of Ordinal Regression Networks
by Daehwan Kim, Haejun Chung, Ikbeom Jang
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper addresses the issue of over-confident predictions in deep neural networks, particularly in ordinal regression tasks where softmax probabilities do not adhere to a unimodal distribution. The proposed novel loss function, which incorporates soft ordinal encoding and ordinal-aware regularization, ensures that prediction confidence is calibrated according to ordinal relationships between classes. Experimental results on four popular benchmarks demonstrate state-of-the-art calibration without sacrificing classification accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with deep neural networks that makes them predict things they’re not sure about too often. The issue is especially important in tasks where we want the network to give us a sense of how confident it is in its predictions. The researchers propose a new way of training these networks that helps them make more accurate and reliable predictions. This approach works by adding special instructions to the network’s learning process, which helps it understand what makes one thing more or less likely than another. |
Keywords
» Artificial intelligence » Classification » Loss function » Regression » Regularization » Softmax
