Summary of Awareness Of Uncertainty in Classification Using a Multivariate Model and Multi-views, by Alexey Kornaev et al.
Awareness of uncertainty in classification using a multivariate model and multi-views
by Alexey Kornaev, Elena Kornaeva, Oleg Ivanov, Ilya Pershin, Danis Alukaev
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 A novel approach to making AI more natural is presented in this paper, focusing on giving models room for doubt. Two primary challenges are addressed: training a model to estimate its own prediction uncertainties and handling uncertain predictions. To tackle the first challenge, an uncertainty-aware negative log-likelihood loss function is proposed for N-dimensional multivariate normal distributions with spherical variance matrices in N-classes classification tasks. This loss function regularizes uncertain predictions and trains models to calculate both predictions and uncertainty estimations. The label smoothing technique fits well with this approach. For the second challenge, data augmentation limits are expanded at training and test stages, generating multiple predictions for each test sample. By combining multi-view predictions, uncertainties, and confidences, several methods are proposed for calculating final predictions, including mode values and bin counts with soft and hard weights. The model tuning task is formalized as a multimodal optimization problem with non-differentiable criteria of maximum accuracy, solved using particle swarm optimization. The proposed methodology demonstrates good results on the CIFAR-10 dataset with clean and noisy labels compared to other uncertainty estimation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI models can be more natural by allowing them to have doubts. This paper solves two big problems: making a model estimate how sure it is of its own predictions, and handling those uncertain predictions. To do this, the authors propose a new loss function that helps models learn to predict and also estimate their uncertainty. They also expand the way data is used during training and testing, which helps generate multiple predictions for each test sample. This allows for different ways to combine these predictions and calculate a final answer. The paper shows how well this approach works on a popular dataset compared to other methods. |
Keywords
» Artificial intelligence » Classification » Data augmentation » Log likelihood » Loss function » Optimization
