Summary of Configuring Data Augmentations to Reduce Variance Shift in Positional Embedding Of Vision Transformers, by Bum Jun Kim et al.
Configuring Data Augmentations to Reduce Variance Shift in Positional Embedding of Vision Transformers
by Bum Jun Kim, Sang Woo Kim
First submitted to arxiv on: 23 May 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 A recent study found that vision transformers (ViTs) require large amounts of diverse data to perform well, but even then, their training is heavily dependent on rich data augmentations like Mixup and Cutmix. Now, researchers have identified a vulnerability in this practice: certain augmentations can cause positional embedding variance shifts, degrading ViT performance at test time. To address this issue, the study proposes specific conditions for image configurations to prevent these side effects and provides guidelines for improving ViT performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Vision transformers are super cool AI models that can do many things with images! But did you know they need a lot of data to work well? And even then, they rely on special tricks to learn from all that data. Unfortunately, some of those tricks can actually make the model worse if not used correctly. The study I’m talking about found out what’s going wrong and gave us some tips to fix it and make ViTs better. |
Keywords
» Artificial intelligence » Embedding » Vit
