Summary of Cross Domain Adaptation Using Adversarial Networks with Cyclic Loss, by Manpreet Kaur et al.
Cross Domain Adaptation using Adversarial networks with Cyclic loss
by Manpreet Kaur, Ankur Tomar, Srijan Mishra, Shashwat Verma
First submitted to arxiv on: 2 Dec 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 This research paper investigates techniques for increasing accuracy in generator networks, which perform translation between two domains in an adversarial setting. The study explores three key aspects: activations, encoder-decoder architectures, and a novel loss function called cyclic loss. These methods are designed to constrain the generator network, enabling effective source-target translation. The motivation behind this research is its potential applications, including generating labeled data from synthetic inputs without supervision and generalizing deep learning networks across domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make deep learning work better when it’s trained on one kind of data but used with another kind. Right now, if the training data is a little different from what you’re trying to use it for, the results get really bad. The researchers looked into ways to fix this problem and found some new techniques that help. They tested these methods by translating between two domains and got better results. This could be useful in lots of situations where we need to adapt deep learning models to work with different kinds of data. |
Keywords
» Artificial intelligence » Deep learning » Encoder decoder » Loss function » Translation
