Summary of Visualizing the Loss Landscape Of Self-supervised Vision Transformer, by Youngwan Lee et al.
Visualizing the loss landscape of Self-supervised Vision Transformer
by Youngwan Lee, Jeffrey Ryan Willette, Jonghee Kim, Sung Ju Hwang
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 paper explores the masked autoencoder (MAE) approach for self-supervised image modeling with vision transformers. MAE-ViT shows better generalization capability than fully supervised training from scratch, but the reason is unknown. The authors propose Reconstruction Consistent Masked Auto Encoder (RC-MAE), which incorporates an exponential moving average (EMA) teacher to perform a conditional gradient correction during optimization. To investigate the effectiveness of RC-MAE and MAE-ViT, the authors visualize the loss landscapes of self-supervised vision transformer models by both MAE and RC-MAE, comparing them with supervised ViT (Sup-ViT). The results show that MAE-ViT has a smoother and wider overall loss curvature than Sup-ViT, and the EMA-teacher allows MAE to widen the region of convexity in both pre-training and linear probing. This work is the first to investigate self-supervised ViT through the lens of loss landscapes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at a way to train computer vision models using just images, without labels. They find that this approach works better than traditional training methods, but they don’t know why it’s more effective. To understand what’s going on, they use a new method called RC-MAE, which helps the model learn by correcting its mistakes during training. By looking at how well the model performs, they discover that this new approach makes the model converge faster and do better on tests. This research is important because it shows a new way to train computer vision models without needing lots of labeled data. |
Keywords
» Artificial intelligence » Autoencoder » Encoder » Generalization » Mae » Optimization » Self supervised » Supervised » Vision transformer » Vit
