Summary of Fair Differentiable Neural Network Architecture Search For Long-tailed Data with Self-supervised Learning, by Jiaming Yan
Fair Differentiable Neural Network Architecture Search for Long-Tailed Data with Self-Supervised Learning
by Jiaming Yan
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Recent advancements in artificial intelligence (AI) have made deep learning (DL) a crucial technology in computer vision, data mining, and natural language processing. Neural architecture search (NAS) is an essential component of DL performance, as it allows for the automatic design of architectures tailored to specific datasets. However, traditional predefined architectures often struggle to adapt to different data distributions, leading to suboptimal performance. To address this issue, researchers have turned to NAS, which can be enhanced by incorporating self-supervised learning and fair differentiable NAS techniques, such as SSF-NAS. This paper explores the application of SSF-NAS on long-tailed datasets, where a few classes have abundant samples, and many have few, leading to biased models. The authors focus on DARTS, FairDARTS, and Barlow Twins, fundamental techniques for SSF-NAS, and conduct experiments on the CIFAR10-LT dataset for performance evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to build a robot that can see and recognize things. You need to design the perfect blueprint for its brain. This is like finding the right neural network architecture in deep learning. Traditionally, these architectures are fixed, but they don’t always work well with different types of data. That’s where Neural Architecture Search (NAS) comes in – it helps find the best architecture for a specific dataset. But sometimes, this approach doesn’t work as well when there are many classes with very few samples. To fix this, researchers have developed techniques like SSF-NAS that combine self-supervised learning and fair differentiable NAS. This paper looks at how to use these techniques on datasets where some classes have many examples while others have few. |
Keywords
* Artificial intelligence * Deep learning * Natural language processing * Neural network * Self supervised
