Summary of Comparison Of Self-supervised In-domain and Supervised Out-domain Transfer Learning For Bird Species Recognition, by Houtan Ghaffari et al.
Comparison of self-supervised in-domain and supervised out-domain transfer learning for bird species recognition
by Houtan Ghaffari, Paul Devos
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
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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 explores the effectiveness of pre-trained models in assisting tasks outside their original domain. Specifically, it investigates whether in-domain models and datasets are more advantageous than competent out-domain models like convolutional neural networks (CNNs) from ImageNet. The study uses VICReg, a self-supervised method, to fine-tune pre-trained models for bird species recognition. Results will show that in-domain models and datasets can be beneficial, highlighting the importance of domain adaptation in deep learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using old models to help with new tasks. Imagine you’re trying to recognize different types of birds just by listening to their sounds. Researchers are exploring whether they can use pre-trained models from image recognition (like recognizing cats and dogs) to help with this task, even though these models were trained on images, not bird sounds. The study uses a special method called VICReg to test how well these pre-trained models work for bird species recognition. |
Keywords
» Artificial intelligence » Deep learning » Domain adaptation » Self supervised
