Summary of A Survey Of the Self Supervised Learning Mechanisms For Vision Transformers, by Asifullah Khan et al.
A Survey of the Self Supervised Learning Mechanisms for Vision Transformers
by Asifullah Khan, Anabia Sohail, Mustansar Fiaz, Mehdi Hassan, Tariq Habib Afridi, Sibghat Ullah Marwat, Farzeen Munir, Safdar Ali, Hannan Naseem, Muhammad Zaigham Zaheer, Kamran Ali, Tangina Sultana, Ziaurrehman Tanoli, Naeem Akhter
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Deep supervised learning models typically require large amounts of labeled data to achieve satisfactory results. However, acquiring and annotating such vast datasets is costly and laborious. Recently, self-supervised learning (SSL) has gained significant attention in vision tasks by exploiting synchronous relationships within the data as a form of self-supervision. The success of SSL relies on finding ways to utilize the abundant unlabeled data available. This motivates reducing reliance on human supervision and focusing on self-supervision based on inherent data relationships. With ViTs achieving remarkable results in computer vision, it is essential to explore and understand various SSL mechanisms for training these models in scenarios with limited labeled data. This survey develops a comprehensive taxonomy of systematically classifying SSL techniques based on their representations and pre-training tasks applied. It reviews popular pre-training tasks, discusses motivations behind SSL, highlights challenges and advancements, presents comparative analyses of different SSL methods, evaluates strengths and limitations, and identifies potential avenues for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to learn using computers called self-supervised learning (SSL). Usually, we need lots of labeled data to train machines, but that takes a lot of time and money. SSL is different because it can use the unlabeled data we already have to improve our results. This means we might not need as much human supervision in the future. The authors look at some popular computer vision models called ViTs and how they work with SSL. They also create a list of all the ways SSL works and what makes each one good or bad. |
Keywords
» Artificial intelligence » Attention » Self supervised » Supervised
