Summary of Rec-ttt: Contrastive Feature Reconstruction For Test-time Training, by Marco Colussi et al.
ReC-TTT: Contrastive Feature Reconstruction for Test-Time Training
by Marco Colussi, Sergio Mascetti, Jose Dolz, Christian Desrosiers
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 proposed ReC-TTT technique enhances Test-Time Training (TTT) for deep learning models by introducing cross-reconstruction as an auxiliary task during training. This approach generates discriminative views of input data, enabling better adaptation to unseen domains at test time. By using a shared decoder and freezing the encoder on the source domain, ReC-TTT achieves state-of-the-art results in most domain shift classification challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ReC-TTT is a new way to make deep learning models more adaptable. It’s like teaching a model to look at things from different angles, so it can better understand new and unseen situations. This helps the model be more accurate when faced with unexpected changes in data. The approach uses something called cross-reconstruction, which is an auxiliary task that trains the model to generate multiple views of the same input. This allows the model to learn how to extract important features from the data, making it better at adapting to new situations. |
Keywords
» Artificial intelligence » Classification » Decoder » Deep learning » Encoder
