{"url":"/method/dense-contrastive-learning","slug":"dense-contrastive-learning","name":"Dense Contrastive Learning","full_name":"Dense Contrastive Learning","full_name_withheld":false,"description_markdown":"**Dense Contrastive Learning** is a self-supervised learning method for dense prediction tasks. It implements self-supervised learning by optimizing a pairwise contrastive (dis)similarity loss at the pixel level between two views of input images. Contrasting with regular contrastive loss, the contrastive loss is computed between the single feature vectors outputted by the global projection head, at the level of global feature, while the dense contrastive loss is computed between the dense feature vectors outputted by the dense projection head, at the level of local feature.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Dense Contrastive Learning for Self-Supervised Visual Pre-Training","paper":"/paper/dense-contrastive-learning-for-self","first_author":"Xinlong Wang","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/dense-contrastive-learning-for-self"},"source":{"url":"https://arxiv.org/abs/2011.09157v2","title":"Dense Contrastive Learning for Self-Supervised Visual Pre-Training","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Self-Supervised Learning","url":"/methods/category/self-supervised-learning","pwc_aliases":[]}],"n_papers_tagged":9,"archive_num_papers":9,"papers_newest_first":[{"paper":null,"title":"An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing","date":"2024-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/fine-grained-spatiotemporal-motion-alignment","title":"Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation Learning","date":"2023-09-01","arxiv_id":"2309.00297","n_code_links":1,"syntology":{"ran":2,"of":3,"unverified":1,"pointer_only":0}},{"paper":"/paper/correlation-between-alignment-uniformity-and","title":"Correlation between Alignment-Uniformity and Performance of Dense Contrastive Representations","date":"2022-10-17","arxiv_id":"2210.08819","n_code_links":1,"syntology":null},{"paper":null,"title":"Pixel-level Correspondence for Self-Supervised Learning from Video","date":"2022-07-08","arxiv_id":"2207.03866","n_code_links":0,"syntology":null},{"paper":null,"title":"Contrastive Learning of Features between Images and LiDAR","date":"2022-06-24","arxiv_id":"2206.12071","n_code_links":0,"syntology":null},{"paper":"/paper/cross-patch-dense-contrastive-learning-for","title":"Cross-Patch Dense Contrastive Learning for Semi-Supervised Segmentation of Cellular Nuclei in Histopathologic Images","date":"2022-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"title":"Dense Contrastive Visual-Linguistic Pretraining","date":"2021-09-24","arxiv_id":"2109.11778","n_code_links":0,"syntology":null},{"paper":"/paper/contrastive-language-image-pre-training-for","title":"Contrastive Language-Image Pre-training for the Italian Language","date":"2021-08-19","arxiv_id":"2108.08688","n_code_links":1,"syntology":null},{"paper":"/paper/dense-contrastive-learning-for-self","title":"Dense Contrastive Learning for Self-Supervised Visual Pre-Training","date":"2020-11-18","arxiv_id":"2011.09157","n_code_links":7,"syntology":{"ran":3,"of":3,"unverified":0,"pointer_only":3}}],"papers_shown":9,"tasks":[{"task":"/task/contrastive-learning","name":"Contrastive Learning","papers":8},{"task":"/task/representation-learning","name":"Representation Learning","papers":3},{"task":"/task/self-supervised-learning","name":"Self-Supervised Learning","papers":3},{"task":"/task/image-classification","name":"Image Classification","papers":2},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":2},{"task":"/task/image-classification","name":"image-classification","papers":2},{"task":"/task/data-augmentation","name":"Data Augmentation","papers":1},{"task":"/task/decoder","name":"Decoder","papers":1},{"task":"/task/diversity","name":"Diversity","papers":1},{"task":"/task/image-retrieval","name":"Image Retrieval","papers":1},{"task":"/task/image-segmentation","name":"Image Segmentation","papers":1},{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":1},{"task":"/task/linear-evaluation","name":"Linear evaluation","papers":1},{"task":"/task/multi-label-zero-shot-learning","name":"Multi-label zero-shot learning","papers":1},{"task":"/task/multimodal-deep-learning","name":"Multimodal Deep Learning","papers":1},{"task":"/task/object","name":"Object","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/optical-flow-estimation","name":"Optical Flow Estimation","papers":1},{"task":"/task/pico","name":"PICO","papers":1},{"task":"/task/prediction","name":"Prediction","papers":1}],"tasks_shown":20,"n_tasks":26,"usage_by_year":[{"year":"2020","papers":1},{"year":"2021","papers":2},{"year":"2022","papers":4},{"year":"2023","papers":1},{"year":"2024","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/dense-contrastive-learning"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}