Summary of Contrastive Learning and Cycle Consistency-based Transductive Transfer Learning For Target Annotation, by Shoaib Meraj Sami et al.
Contrastive Learning and Cycle Consistency-based Transductive Transfer Learning for Target Annotation
by Shoaib Meraj Sami, Md Mahedi Hasan, Nasser M. Nasrabadi, Raghuveer Rao
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV); Machine Learning (stat.ML)
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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 proposes a novel approach for automatic target recognition (ATR) annotation using transductive transfer learning (TTL). The current state-of-the-art TTL method, which incorporates a CycleGAN-based unpaired domain translation network, has limitations such as lower annotation performance and higher Fréchet Inception Distance (FID) score. To address these issues, the authors introduce a hybrid contrastive learning base unpaired domain translation (H-CUT) network that employs attention, entropy, noisy feature mixup, and modulated noise contrastive estimation to generate high-variational synthetic negative patches. The proposed C3TTL framework combines two H-CUT networks with two classifiers, optimizing cycle-consistency, MoNCE, and identity losses simultaneously. Experimental results on three ATR datasets demonstrate the effectiveness of the proposed method in annotating civilian and military vehicles as well as ship targets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to help computers recognize targets, like vehicles or ships. Right now, it’s hard for computers to learn from examples because they don’t have enough labeled data. The authors suggest using something called transductive transfer learning, which uses images from one place (the source domain) to teach the computer what things look like in another place (the target domain). They’ve improved this approach by adding some new tricks, like attention and entropy, to help the computer learn better. This method is tested on three different datasets and shows that it can accurately identify targets. |
Keywords
* Artificial intelligence * Attention * Transfer learning * Translation
