Research map / Label-efficient and robust learning / Recognition and long-tail
A Probabilistic Collaborative Representation Based Approach for Pattern Classification
Check your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ProCRC instead of SVM | handwritten digit classification · MNIST, 50 training samples/class | classification rate: 89.35 -> 91.84 | Helped |
| Use ProCRC instead of SVM | handwritten digit classification · MNIST, 500 training samples/class | classification rate: 95.93 -> 95.88 | Hurt |
| Use ProCRC instead of SVM | handwritten digit classification · USPS, 300 training samples/class | classification rate: 96.30 -> 96.43 | Helped |
| Use robust ProCRC instead of robust SRC | face recognition with random pixel corruption · Extended Yale B, 60% corruption | recognition rate: 76.85 -> 82.42 | Helped |
| Use robust ProCRC instead of robust SRC | face recognition with block occlusion · Extended Yale B, 10% occlusion | recognition rate: 90.42 -> 98.12 | Helped |
| Use R-ProCRC instead of R-SRC | face recognition with disguise · AR, sunglasses disguise | recognition rate: 69.17 -> 70.50 | Helped |
| Use ProCRC instead of Kernel SVM | VGG19 feature classification · Stanford 40 Actions | accuracy: 79.8 -> 80.9 | Helped |
| Use ProCRC instead of Kernel SVM | VGG19 feature classification · CUB200-2011 | accuracy: 76.6 -> 78.3 | Helped |
| Use ProCRC instead of CRC | VGG19 feature classification · Oxford 102 Flowers | accuracy: 93.0 -> 94.8 | Helped |
| Use ProCRC instead of Kernel SVM | VGG19 feature classification · Caltech-256, 30 training images/class | accuracy: 81.3 -> 83.3 | Helped |
| Use ProCRC instead of Softmax | AlexNet features on ImageNet · ImageNet ILSVRC-2012 | top-1 accuracy: 57.4 -> 56.3 | Hurt |
| Use ProCRC instead of Softmax | AlexNet features on ImageNet · ImageNet ILSVRC-2012 | top-5 accuracy: 80.4 -> 80.1 | Hurt |
Closest papers by meaning
- Online Collaborative Learning for Open-Vocabulary Visual Classifiers (CVPR 2016 · similarity 0.85)
- Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization (ICLR 2024 · similarity 0.84)
- Probabilistic Invariant Learning with Randomized Linear Classifiers (NeurIPS 2023 · similarity 0.82)
- Collaborative Learning with Different Labeling Functions (ICML 2024 · similarity 0.82)
- I-Con: A Unifying Framework for Representation Learning (ICLR 2025 · similarity 0.81)
- CoDTS: Enhancing Sparsely Supervised Collaborative Perception with a Dual Teacher-Student Framework (AAAI 2025 · similarity 0.81)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.