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Methods › Computer Vision › Vision Transformers › DINO

self-DIstillation with NO labels

DINO

208 papers tagged archive 2025-07-28

Introduced by Mathilde Caron et al. in Emerging Properties in Self-Supervised Vision Transformers

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

DINO (self-distillation with no labels) is a self-supervised learning method that directly predicts the output of a teacher network - built with a momentum encoder - using a standard cross-entropy loss.

In the example to the right, DINO is illustrated in the case of one single pair of views (x₁, x₂) for simplicity. The model passes two different random transformations of an input image to the student and teacher networks. Both networks have the same architecture but other parameters. The output of the teacher network is centered with a mean computed over the batch. Each network outputs a K dimensional feature normalized with a temperature softmax over the feature dimension. Their similarity is then measured with a cross-entropy loss. A stop-gradient (sg) operator is applied to the teacher to propagate gradients only through the student. The teacher parameters are updated with the student parameters' exponential moving average (ema).

PaperSourceSee Code · facebookresearch/dino

Papers archive 2025-07-28

30 shown of 208, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 225 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Semantic Segmentation44
Object Detection38
object-detection33
Segmentation29
Self-Supervised Learning28
Object25
Contrastive Learning13
Representation Learning13
Image Classification11
Instance Segmentation11
Image Generation10
image-classification9
Clustering8
Data Augmentation7
Scene Understanding7
Transfer Learning7
Image Segmentation6
Retrieval6
Unsupervised Semantic Segmentation6
Anomaly Detection5

Usage over time archive 2025-07-28

Papers per year tagged with DINO: 2021 to 2025, peak 108 108 0 2021: 1 paper 2021 2022: 5 papers 2022 2023: 44 papers 2023 2024: 108 papers 2024 2025: 50 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (208 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Vision TransformersSelf-Supervised Learning

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