Summary of Enhancing Jepas with Spatial Conditioning: Robust and Efficient Representation Learning, by Etai Littwin et al.
Enhancing JEPAs with Spatial Conditioning: Robust and Efficient Representation Learning
by Etai Littwin, Vimal Thilak, Anand Gopalakrishnan
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 IJEPA, an alternative to Masked Autoencoder (MAE), leverages the Masked Image Modeling framework for representation learning. Unlike MAE, IJEPA predicts in latent space, capturing semantic information by driving representations. While relying on carefully designed context and target windows, IJEPA’s encoder modules lack adaptive modulation of predicted features based on feasibility. To address this, we condition the target and context encoders with positions of context and target windows respectively. This “conditional” approach yields performance gains on several image classification benchmark datasets, enhancing robustness to context window size and sample-efficiency during pretraining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary IJEPA is a new way to learn from images that’s different from Masked Autoencoder (MAE). It uses the same idea as MAE, but does things differently. IJEPA looks at how well it can predict missing parts of an image, which helps it understand what’s important in those images. To make this work better, we gave it information about where the important parts are in each image. This helped it learn even faster and more accurately, especially when looking at small pieces of the image. |
Keywords
» Artificial intelligence » Autoencoder » Context window » Encoder » Image classification » Latent space » Mae » Pretraining » Representation learning
