# Conditional Contrastive Learning with Kernel

arXiv: https://arxiv.org/abs/2202.05458 · HTML: https://mlsanity.com/paper/2202.05458/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 11 Feb 2022.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Learning Representations by Maximizing Mutual Information Across Views](https://mlsanity.com/paper/1906.00910/) (2019)
- [Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2010.04592/) (2020)
- [Large Batch Training of Convolutional Networks](https://mlsanity.com/paper/1708.03888/) (2017)
- [A Theoretical Analysis of Contrastive Unsupervised Representation Learning](https://mlsanity.com/paper/1902.09229/) (2019)
- [Hard Negative Mixing for Contrastive Learning](https://mlsanity.com/paper/2010.01028/) (2020)
- [On Mutual Information Maximization for Representation Learning](https://mlsanity.com/paper/1907.13625/) (2019)
- [Swapping Autoencoder for Deep Image Manipulation](https://mlsanity.com/paper/2007.00653/) (2020)
- [Domain Generalization by Marginal Transfer Learning](https://mlsanity.com/paper/1711.07910/) (2017)
- [Predicting What You Already Know Helps: Provable Self-Supervised Learning](https://mlsanity.com/paper/2008.01064/) (2020)
- [A Mutual Information Maximization Perspective of Language Representation Learning](https://mlsanity.com/paper/1910.08350/) (2019)
- [Conditional Negative Sampling for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2010.02037/) (2020)
- [Self-supervised Representation Learning with Relative Predictive Coding](https://mlsanity.com/paper/2103.11275/) (2021)
- [Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings](https://mlsanity.com/paper/1906.02236/) (2019)
- [C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation](https://mlsanity.com/paper/2005.08226/) (2020)
- [Integrating Auxiliary Information in Self-supervised Learning](https://mlsanity.com/paper/2106.02869/) (2021)

## Built on by (most cited first, 22 of 22)

- [Factorized Contrastive Learning: Going Beyond Multi-view Redundancy](https://mlsanity.com/paper/2306.05268/) (2023)
- [Contrastive learning for regression in multi-site brain age prediction](https://mlsanity.com/paper/2211.08326/) (2022)
- [Contrastive Conditional Neural Processes](https://mlsanity.com/paper/2203.03978/) (2022)
- [Conditional Supervised Contrastive Learning for Fair Text Classification](https://mlsanity.com/paper/2205.11485/) (2022)
- [Integrating Prior Knowledge in Contrastive Learning with Kernel](https://mlsanity.com/paper/2206.01646/) (2022)
- [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023)
- [Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based Losses](https://mlsanity.com/paper/2405.18045/) (2024)
- [Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis](https://mlsanity.com/paper/2304.00395/) (2023)
- [Removing Biases from Molecular Representations via Information Maximization](https://mlsanity.com/paper/2312.00718/) (2023)
- [CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization](https://mlsanity.com/paper/2503.24182/) (2025)
- [Homomorphic Self-Supervised Learning](https://mlsanity.com/paper/2211.08282/) (2022)
- [Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play](https://mlsanity.com/paper/2302.05807/) (2023)
- [Decoupled conditional contrastive learning with variable metadata for prostate lesion detection](https://mlsanity.com/paper/2308.09542/) (2023)
- [SynCL: A Synergistic Training Strategy with Instance-Aware Contrastive Learning for End-to-End Multi-Camera 3D Tracking](https://mlsanity.com/paper/2411.06780/) (2024)
- [Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling](https://mlsanity.com/paper/2311.16361/) (2023)
- [Foundations of Multisensory Artificial Intelligence](https://mlsanity.com/paper/2404.18976/) (2024)
- [An Attention-based Framework for Fair Contrastive Learning](https://mlsanity.com/paper/2411.14765/) (2024)
- [Information Subtraction: Learning Representations for Conditional Entropy](https://mlsanity.com/paper/2501.02012/) (2025)
- [Negative-Guided Subject Fidelity Optimization for Zero-Shot Subject-Driven Generation](https://mlsanity.com/paper/2506.03621/) (2025)
- [FairContrast: Enhancing Fairness through Contrastive learning and Customized Augmenting Methods on Tabular Data](https://mlsanity.com/paper/2510.02017/) (2025)
- [MoMo: Conditioned Contrastive Representation Learning for Preference-Modulated Planning](https://mlsanity.com/paper/2605.08512/) (2026)
- [Multi-Objective Exploration and Preference Optimization via Mutual Information](https://mlsanity.com/paper/2607.01392/) (2026)

## Abstract (arXiv)

Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables. Fair contrastive learning constructs negative pairs, for example, from the same gender (conditioning on sensitive information), which in turn reduces undesirable information from the learned representations; weakly supervised contrastive learning constructs positive pairs with similar annotative attributes (conditioning on auxiliary information), which in turn are incorporated into the representations. Although conditional contrastive learning enables many applications, the conditional sampling procedure can be challenging if we cannot obtain sufficient data pairs for some values of the conditioning variable. This paper presents Conditional Contrastive Learning with Kernel (CCL-K) that converts existing conditional contrastive objectives into alternative forms that mitigate the insufficient data problem. Instead of sampling data according to the value of the conditioning variable, CCL-K uses the Kernel Conditional Embedding Operator that samples data from all available data and assigns weights to each sampled data given the kernel similarity between the values of the conditioning variable. We conduct experiments using weakly supervised, fair, and hard negatives contrastive learning, showing CCL-K outperforms state-of-the-art baselines.