Greedy InfoMax for Biologically Plausible Self-Supervised Representation Learning

By Sindy Löwe, Peter O'Connor, Bastiaan S. Veeling. 2019

5 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Dani Yogatama
2. Lingpeng Kong
3. Lei Yu
4. Ngoc-Trung Tran
5. Kwot Sin Lee
6. Wang Ling
7. Ngai-Man Cheung
8. Cyprien de Masson d'Autume

Citations in the corpus

• InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive Learning (2020, background): “Contrastive learning Recently, state-of-the-art works in unsupervised representation learning [3,22,24,29,37,47,57] lies in taking a contrastive approach to maximizing the mutual information between encoded local and global features.”
• A Mutual Information Maximization Perspective of Language Representation Learning (2019, background): “…1988) and has been the main driver of progress in self-supervised representation learning in other domains such as computer vision, audio processing, and reinforcement learning (Belghazi et al., 2018; van den Oord et al., 2019; Hjelm et al., 2019; Bachman et al., 2019; O’Connor & Veeling, 2019).”