Papers › Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

19 Jun 2020arXiv:2006.11325archive 2025-07-28

Carlos Medina, Arnout Devos, Matthias Grossglauser

Most approaches in few-shot learning rely on costly annotated data related to the goal task domain during (pre-)training. Recently, unsupervised meta-learning methods have exchanged the annotation requirement for a reduction in few-shot classification performance. Simultaneously, in settings with realistic domain shift, common transfer learning has been shown to outperform supervised meta-learning. Building on these insights and on advances in self-supervised learning, we propose a transfer learning approach which constructs a metric embedding that clusters unlabeled prototypical samples and their augmentations closely together. This pre-trained embedding is a starting point for few-shot classification by summarizing class clusters and fine-tuning. We demonstrate that our self-supervised prototypical transfer learning approach ProtoTransfer outperforms state-of-the-art unsupervised meta-learning methods on few-shot tasks from the mini-ImageNet dataset. In few-shot experiments with domain shift, our approach even has comparable performance to supervised methods, but requires orders of magnitude fewer labels.

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indy-lab/ProtoTransfer officialmentioned in papermentioned on GitHubpytorch report
ojss/samptransfer mentioned on GitHubpytorch report

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Protonet indy-lab/ProtoTransfer/omni-mini/prototransfer/protonet.py official repository ran MIT (permissive) · 8b5dd421e59bc6a5 · report
euclidean_distance indy-lab/ProtoTransfer/omni-mini/prototransfer/protonet.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 4cff643601aa1b0c · report
get_prototypes indy-lab/ProtoTransfer/omni-mini/prototransfer/protonet.py official repository ran · our draft was wrong MIT (permissive) · 8fb77482b9d9c2f1 · report
prototypical_loss indy-lab/ProtoTransfer/omni-mini/prototransfer/protonet.py official repository ran · fixture could not drive it MIT (permissive) · df7db1db2fea7a21 · report
get_num_samples indy-lab/ProtoTransfer/omni-mini/prototransfer/protonet.py official repository unverified MIT (permissive) · 1c8b5648679d11f1 · report
cosine_similarity ojss/samptransfer/utils/proto_utils.py community (archive-listed) ran · our draft was wrong fingerprinted no licence file found · pointer only · e0ea79d7ea081390 · report
euclidean_distance ojss/samptransfer/utils/proto_utils.py community (archive-listed) ran · our draft was wrong fingerprinted no licence file found · pointer only · 083427faad799a4d · report
prototypical_loss ojss/samptransfer/utils/proto_utils.py community (archive-listed) ran · fixture could not drive it no licence file found · pointer only · 7afcf427b59e8a5c · report
sns ojss/samptransfer/utils/proto_utils.py community (archive-listed) ran · our draft was wrong fingerprinted no licence file found · pointer only · 07799eb1ce6595d0 · report

Tasks

ClassificationFew-Shot LearningGeneral ClassificationMeta-LearningSelf-Supervised LearningTransfer LearningUnsupervised Few-Shot Image ClassificationUnsupervised Few-Shot Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Unsupervised Few-Shot Image Classification Mini-Imagenet 5-way (1-shot) ProtoTransfer Accuracy 45.67 #17 of 28 Archive leaderboard report
Unsupervised Few-Shot Image Classification Mini-Imagenet 5-way (5-shot) ProtoTransfer Accuracy 62.99 #16 of 28 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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