Summary of Cross-domain Few-shot Learning Via Adaptive Transformer Networks, by Naeem Paeedeh et al.
Cross-Domain Few-Shot Learning via Adaptive Transformer Networks
by Naeem Paeedeh, Mahardhika Pratama, Muhammad Anwar Ma’sum, Wolfgang Mayer, Zehong Cao, Ryszard Kowlczyk
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers propose an adaptive transformer network (ADAPTER) for cross-domain few-shot learning, where there are large domain shifts between tasks. ADAPTER uses bidirectional cross-attention to learn transferable features between domains and is trained with DINO to produce diverse, less biased features. The architecture also incorporates label smoothing to improve prediction consistency. The authors evaluate the performance of ADAPTER in BSCD-FSL benchmarks and show significant improvements over prior arts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn in new ways by making it easier to transfer skills from one task to another, even when there’s a big difference between them. They create a special kind of AI model called ADAPTER that can do this. It’s like having a superpower that lets you pick up new skills quickly! The researchers also found a way to make the predictions more reliable and consistent. This is an important step forward in making AI models work better in real-life situations. |
Keywords
* Artificial intelligence * Cross attention * Few shot * Transformer
