A Priori Generalizability Estimate for a CNN
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Abstract (arXiv)
We formulate truncated singular value decompositions of entire convolutional neural networks. We demonstrate the computed left and right singular vectors are useful in identifying which images the convolutional neural network is likely to perform poorly on. To create this diagnostic tool, we define two metrics: the Right Projection Ratio and the Left Projection Ratio. The Right (Left) Projection Ratio evaluates the fidelity of the projection of an image (label) onto the computed right (left) singular vectors. We observe that both ratios are able to identify the presence of class imbalance for an image classification problem. Additionally, the Right Projection Ratio, which only requires unlabeled data, is found to be correlated to the model's performance when applied to image segmentation. This suggests the Right Projection Ratio could be a useful metric to estimate how likely the model is to perform well on a sample.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 10 references that are in the map. First posted on arXiv 24 Feb 2025.
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6 of 10 references.
References · 6
- 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
- 2014Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluationcited by 306
- 2014Speeding up Convolutional Neural Networks with Low Rank Expansionscited by 284
- 2018Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learningcited by 215
- 2018Mad Max: Affine Spline Insights into Deep Learningcited by 55
- 2024MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Frameworkcited by 5
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