Summary of Efficienttrain++: Generalized Curriculum Learning For Efficient Visual Backbone Training, by Yulin Wang et al.
EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone Training
by Yulin Wang, Yang Yue, Rui Lu, Yizeng Han, Shiji Song, Gao Huang
First submitted to arxiv on: 14 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed EfficientTrain++ method is designed to reduce the training time of visual backbones while maintaining their performance. By generalizing curriculum learning, it reformulates the training procedure as a soft-selection function that uncovers progressively more difficult patterns within each example during training. This approach leverages all training data at every stage, but initiates exposure to easier-to-learn patterns before introducing harder ones. To achieve this, the method introduces a cropping operation in the Fourier spectrum of inputs and modulates data augmentation intensity. The result is a simple yet effective method that reduces training time by 1.5-3.0x for various models on ImageNet-1K/22K without sacrificing accuracy, making it suitable for self-supervised learning (e.g., MAE). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to train visual backbones faster and better. Instead of selecting easier-to-harder samples, the method uncovers patterns within each image during training. This makes it efficient and works well with different models on big datasets without sacrificing accuracy. |
Keywords
» Artificial intelligence » Curriculum learning » Data augmentation » Mae » Self supervised
