Summary of On Diversity in Discriminative Neural Networks, by Brahim Oubaha et al.
On Diversity in Discriminative Neural Networks
by Brahim Oubaha, Claude Berrou, Xueyao Ji, Yehya Nasser, Raphaël Le Bidan
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed neural network architecture incorporates various diversity principles to achieve remarkable results in self-supervised and semi-supervised learning tasks. The model leverages spatial, temporal, and frequency diversities, as well as redundant coding, to design extremely efficient systems. Specifically, the paper reports a record self-supervised learning accuracy of 99.57% on MNIST and a top-tier semi-supervised learning accuracy of 94.21% on CIFAR-10 using only 25 labels per class. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The neural network architecture builds upon various diversity principles to achieve impressive results in machine learning tasks. The model combines different diversities, such as spatial, temporal, and frequency, with redundant coding to create an efficient system. The paper reports remarkable self-supervised and semi-supervised learning accuracy on MNIST and CIFAR-10 datasets. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Self supervised » Semi supervised
