Summary of Efficienttrain: Exploring Generalized Curriculum Learning For Training Visual Backbones, by Yulin Wang et al.
EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbones
by Yulin Wang, Yang Yue, Rui Lu, Tianjiao Liu, Zhao Zhong, Shiji Song, Gao Huang
First submitted to arxiv on: 17 Nov 2022
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 presents a new curriculum learning approach for efficient training of visual backbones, such as vision Transformers. It’s inspired by the way deep networks learn early on, recognizing easier-to-learn patterns within each example. The proposed curriculum starts with exposing these easy patterns and gradually introduces more difficult ones. To implement this, the authors introduce a cropping operation in the Fourier spectrum to efficiently learn from lower-frequency components, demonstrate that original image features are equivalent to weaker data augmentation, and design a learning schedule using a greedy-search algorithm. This approach, called EfficientTrain, reduces training costs by >1.5x for various popular models on ImageNet-1K/22K without sacrificing accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us train deep networks more efficiently. It’s like a roadmap that guides the model to learn in stages. First, it looks at easy things and then moves on to harder ones. This makes training faster and uses less energy. The approach is simple but works well for many different models. It even helps with self-supervised learning. |
Keywords
* Artificial intelligence * Curriculum learning * Data augmentation * Self supervised
