Summary of No Train, All Gain: Self-supervised Gradients Improve Deep Frozen Representations, by Walter Simoncini et al.
No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations
by Walter Simoncini, Spyros Gidaris, Andrei Bursuc, Yuki M. Asano
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 paper introduces FUNGI, a method to enhance transformer encoder features using self-supervised gradients. The approach is simple: compute gradients from various self-supervised objectives for each input, project them to a lower dimension, and concatenate with the model’s output embedding. The resulting features are evaluated on 11 datasets from vision, NLP, and audio, showing consistent performance improvements over embeddings across various backbones and pretraining strategies. FUNGI also benefits linear classification, clustering, image retrieval, and improves in-context scene understanding abilities of pretrained models, such as DINO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a way to make transformers better by using things they’re already good at. They take the model’s output and add some extra information from other tasks that don’t require labels. This helps the model do better on various tasks like image recognition, language processing, and audio analysis. The new features also improve how well models can understand scenes and objects. |
Keywords
* Artificial intelligence * Classification * Clustering * Embedding * Encoder * Nlp * Pretraining * Scene understanding * Self supervised * Transformer
