# Refining Self-Supervised Learning in Imaging: Beyond Linear Metric

arXiv: https://arxiv.org/abs/2202.12921 · HTML: https://mlsanity.com/paper/2202.12921/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Debiased Contrastive Learning](https://mlsanity.com/paper/2007.00224/) (2020)
- [Understanding self-supervised Learning Dynamics without Contrastive Pairs](https://mlsanity.com/paper/2102.06810/) (2021)
- [Learning Image Representations by Completing Damaged Jigsaw Puzzles](https://mlsanity.com/paper/1802.01880/) (2018)
- [Self-Supervised Feature Learning by Learning to Spot Artifacts](https://mlsanity.com/paper/1806.05024/) (2018)
- [Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks](https://mlsanity.com/paper/2002.07898/) (2020)
- [Structured Analysis Dictionary Learning for Image Classification](https://mlsanity.com/paper/1805.00597/) (2018)
- [Deep Transform and Metric Learning Networks](https://mlsanity.com/paper/2104.10329/) (2021)
- [Dynamic Graph Learning based on Graph Laplacian](https://mlsanity.com/paper/2002.07044/) (2020)

## Built on by (most cited first, 1 of 1)

- [Implicit Bayes Adaptation: A Collaborative Transport Approach](https://mlsanity.com/paper/2304.08298/) (2023)

## Abstract (arXiv)

We introduce in this paper a new statistical perspective, exploiting the Jaccard similarity metric, as a measure-based metric to effectively invoke non-linear features in the loss of self-supervised contrastive learning. Specifically, our proposed metric may be interpreted as a dependence measure between two adapted projections learned from the so-called latent representations. This is in contrast to the cosine similarity measure in the conventional contrastive learning model, which accounts for correlation information. To the best of our knowledge, this effectively non-linearly fused information embedded in the Jaccard similarity, is novel to self-supervision learning with promising results. The proposed approach is compared to two state-of-the-art self-supervised contrastive learning methods on three image datasets. We not only demonstrate its amenable applicability in current ML problems, but also its improved performance and training efficiency.