Papers › Understanding Contrastive Representation Learning through Alignment and Uniformity on...

Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

20 May 2020arXiv:2005.10242archive 2025-07-28

Tongzhou Wang, Phillip Isola

Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Remarkably, directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning. Project Page: https://ssnl.github.io/hypersphere Code: https://github.com/SsnL/align_uniform , https://github.com/SsnL/moco_align_uniform

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2005.10242")

Code

Syntology Ran 2 of 11 code samples harvested from 2 repositories linked to this paper; 9 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · fixture could not drive it.

By repository: community (archive-listed): 9 samples from 1 repository, 2 ran; community: 1 sample from 1 repository, 0 ran; 1 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

SsnL/align_uniform officialmentioned in papermentioned on GitHubpytorchMIT report
pangzss/pytorch-ctvsum mentioned on GitHubpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

11 samples harvested; 2 ran; 1 honoured the contract we drafted; 9 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · honoured contract
1ran · fixture could not drive it
9unverified

Licence: 2 of the 11 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. Some samples are identical code Syntology first harvested from another repository; for those, this paper's copy is not located and its licence is not recorded. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

cosine_scheduler pangzss/pytorch-ctvsum/utils.py community (archive-listed) ran · honoured contract fingerprinted MIT (permissive) · 361a6b24f11fc50a · report
trunc_normal_ pangzss/pytorch-ctvsum/utils.py community (archive-listed) ran · fixture could not drive it MIT (permissive) · d8578a1e5c318cf9 · report
evaluate_summary pangzss/pytorch-ctvsum/vsum_tools.py community (archive-listed) unverified MIT (permissive) · 4926d212a1822095 · report
evaluate_user_summaries pangzss/pytorch-ctvsum/vsum_tools.py community (archive-listed) unverified MIT (permissive) · 94b106ddf66bb46e · report
get_rc_func pangzss/pytorch-ctvsum/evaluation.py community (archive-listed) unverified MIT (permissive) · 7a401efe934df82d · report
knapsack pangzss/pytorch-ctvsum/knapsack.py community (archive-listed) unverified MIT (permissive) · 26afde22fce432ac · report
knapsack_dp pangzss/pytorch-ctvsum/knapsack.py community (archive-listed) unverified MIT (permissive) · 96b2828743f88070 · report
nondefault_trainer_args pangzss/pytorch-ctvsum/main_ablations.py community (archive-listed) unverified MIT (permissive) · 0998ae7833d6d509 · report
test_collate pangzss/pytorch-ctvsum/utils.py community (archive-listed) unverified MIT (permissive) · 641956a2906f089f · report
validate ssnl/moco_align_uniform/main_lincls.py community unverified licence not identified · pointer only · 0358792e34e9cbb2 · report
accuracy identical code first harvested elsewhere unverified licence of this copy not recorded · 9b8289076669fe4f · report

Tasks

Contrastive LearningRepresentation Learning

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections