Summary of Cbm: Curriculum by Masking, By Andrei Jarca et al.
CBM: Curriculum by Masking
by Andrei Jarca, Florinel-Alin Croitoru, Radu Tudor Ionescu
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes a novel state-of-the-art curriculum learning strategy called Curriculum by Masking (CBM), which creates an easy-to-hard training schedule via patch masking, achieving significant accuracy improvements over conventional training and previous CL methods. CBM leverages gradient magnitudes to prioritize the masking of salient image regions using a novel masking algorithm and block. It operates with two easily configurable parameters: number of patches and curriculum schedule, making it versatile for object recognition and detection. The approach is evaluated on five benchmark datasets (CIFAR-10, CIFAR-100, ImageNet, Food-101, and PASCAL VOC) using various neural architectures, including convolutional networks and vision transformers. The results show the superiority of CBM over state-of-the-art curriculum learning regimes, with improvements in transfer learning contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CBM is a new way to train artificial intelligence models that gets better results by making it harder for them to learn. It does this by hiding parts of images, like faces or objects, and then gradually showing more challenging ones as the model improves. This approach uses two easy-to-control settings: how many patches (small pieces) to hide and what schedule to follow when adding more challenges. The researchers tested CBM on five different datasets with various AI models and found that it outperformed other methods. |
Keywords
* Artificial intelligence * Curriculum learning * Transfer learning
