mlsanityData to 14 Oct 2026

Adaptive Soft Contrastive Learning

arXiv 2207.11163 ↗First posted 22 Jul 2022cs.CVcited by 13

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Abstract (arXiv)

Self-supervised learning has recently achieved great success in representation learning without human annotations. The dominant method -- that is contrastive learning, is generally based on instance discrimination tasks, i.e., individual samples are treated as independent categories. However, presuming all the samples are different contradicts the natural grouping of similar samples in common visual datasets, e.g., multiple views of the same dog. To bridge the gap, this paper proposes an adaptive method that introduces soft inter-sample relations, namely Adaptive Soft Contrastive Learning (ASCL). More specifically, ASCL transforms the original instance discrimination task into a multi-instance soft discrimination task, and adaptively introduces inter-sample relations. As an effective and concise plug-in module for existing self-supervised learning frameworks, ASCL achieves the best performance on several benchmarks in terms of both performance and efficiency. Code is available at https://github.com/MrChenFeng/ASCL_ICPR2022.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 23 of its 28 references that are in the map. First posted on arXiv 22 Jul 2022.

Builds on

23 of 28 references.

References · 23

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  3. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750
  4. 2020Exploring Simple Siamese Representation Learningcited by 2,866
  5. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,857
  6. 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,845
  7. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,591
  8. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,382
  9. 2016Colorful Image Colorizationcited by 1,114
  10. 2015Unsupervised Visual Representation Learning by Context Predictioncited by 1,066
  11. 2020Prototypical Contrastive Learning of Unsupervised Representationscited by 640
  12. 2019A Theoretical Analysis of Contrastive Unsupervised Representation Learningcited by 530
11 more
  1. 2019Self-labelling via simultaneous clustering and representation learningcited by 528
  2. 2020Debiased Contrastive Learningcited by 356
  3. 2021With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representationscited by 326
  4. 2014Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networkscited by 300
  5. 2019Local Aggregation for Unsupervised Learning of Visual Embeddingscited by 204
  6. 2020Boosting Contrastive Self-Supervised Learning with False Negative Cancellationcited by 119
  7. 2021Contrastive Learning with Stronger Augmentationscited by 100
  8. 2021ReSSL: Relational Self-Supervised Learning with Weak Augmentationcited by 84
  9. 2021Mean Shift for Self-Supervised Learningcited by 65
  10. 2021Incremental False Negative Detection for Contrastive Learningcited by 48
  11. 2020CO2: Consistent Contrast for Unsupervised Visual Representation Learningcited by 38

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 6; 2025: 2; 2026: 1.

13 papers.

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  1. 2023Soft Contrastive Learning for Time Seriescited by 51
  2. 2023MaskCon: Masked Contrastive Learning for Coarse-Labelled Datasetcited by 21
  3. 2024CLIPCleaner: Cleaning Noisy Labels with CLIPcited by 16
  4. 2024SwiMDiff: Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Imagecited by 14
  5. 2024LAFS: Landmark-based Facial Self-supervised Learning for Face Recognitioncited by 13
  6. 2025Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolutioncited by 5
  7. 2023Self-Supervised Representation Learning with Cross-Context Learning between Global and Hypercolumn Featurescited by 4
  8. 2024Open-Vocabulary Mobile Manipulation Based on Double Relaxed Contrastive Learning with Dense Labelingcited by 3
  9. 2024SoftCVI: Contrastive variational inference with self-generated soft labelscited by 1
  10. 2024Explicit Mutual Information Maximization for Self-Supervised Learningcited by 1
  11. 2026Deconstructing the Failure of Ideal Noise Correction: A Three-Pillar Diagnosiscited by 1
  12. 2023MSVQ: Self-Supervised Learning with Multiple Sample Views and Queuescited by 0
1 more
  1. 2025Predicting butterfly species presence from satellite imagery using soft contrastive regularisationcited by 0