Summary of Image-feature Weak-to-strong Consistency: An Enhanced Paradigm For Semi-supervised Learning, by Zhiyu Wu et al.
Image-Feature Weak-to-Strong Consistency: An Enhanced Paradigm for Semi-Supervised Learning
by Zhiyu Wu, Jinshi Cui
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper introduces a novel semi-supervised learning (SSL) approach that combines feature-level perturbations with varying intensities and forms to expand the augmentation space, establishing an image-feature weak-to-strong consistency paradigm. This paradigm develops a triple-branch structure that facilitates interactions between both types of perturbations within one branch, boosting their synergy. Additionally, the authors propose a confidence-based identification strategy to distinguish between naive and challenging samples, introducing additional challenges exclusively for naive samples. The proposed approach can seamlessly integrate with existing SSL methods and is applied to several representative algorithms on multiple benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way of learning from partially labeled data that makes it easier to learn by making the fake data more like real data. It uses a combination of image-level and feature-level perturbations, which helps to improve the performance of existing machine learning models. This approach can be used with many different types of models and datasets, and it’s tested on several benchmarks to show that it works well. |
Keywords
* Artificial intelligence * Boosting * Machine learning * Semi supervised
