Unsupervised Few-Shot Image Classification

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

4 leaderboard tables shown for this task, 4 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

15 shown of 15 papers with code (28 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Self-Supervision Can Be a Good Few-Shot Learner
  19 Jul 2022 4 repositories listed Syntology ran 3 of 10 samples · 7 unverified
  Specifically, we maximize the mutual information (MI) of instances and their representations with a low-bias MI estimator to perform self-supervised pre-training.
• Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
  19 Jun 2020 2 repositories listed Syntology ran 8 of 9 samples · 1 unverified · 4 pointer-only (licence)
  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…
• Self-Supervised Learning For Few-Shot Image Classification
  14 Nov 2019 2 repositories listed Syntology ran 2 of 3 samples · 1 unverified
  In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself.
• BECLR: Batch Enhanced Contrastive Few-Shot Learning
  4 Feb 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Learning quickly from very few labeled samples is a fundamental attribute that separates machines and humans in the era of deep representation learning.
• CMVAE: Causal Meta VAE for Unsupervised Meta-Learning
  20 Feb 2023 1 repository listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Unsupervised meta-learning aims to learn the meta knowledge from unlabeled data and rapidly adapt to novel tasks.
• Contrastive Prototypical Network with Wasserstein Confidence Penalty
  21 Oct 2022 1 repository listed
  This results in our CPN (Contrastive Prototypical Network) model, which combines the prototypical loss with pairwise contrast and outperforms the existing models from this paradigm with modestly large batch size.
• Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space
  21 Oct 2022 1 repository listed
  Most few-shot image classification methods are trained based on tasks.
• Self-Attention Message Passing for Contrastive Few-Shot Learning
  12 Oct 2022 1 repository listed
  Humans have a unique ability to learn new representations from just a handful of examples with little to no supervision.
• Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
  27 Sep 2022 1 repository listed
  In this work, we prove that the core reason for this is lack of a clustering-friendly property in the embedding space.
• Self-Supervised Class-Cognizant Few-Shot Classification
  15 Feb 2022 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  Unsupervised learning is argued to be the dark matter of human intelligence.
• Multi-level Second-order Few-shot Learning
  15 Jan 2022 1 repository listed
  The goal of multi-level feature design is to extract feature representations at different layer-wise levels of CNN, realizing several levels of visual abstraction to achieve robust few-shot learning.
• Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning
  1 Jan 2021 1 repository listed
  Then, the learned model can be used for downstream few-shot classification tasks, where we obtain task-specific parameters by performing semi-supervised EM on the latent representations of the support and query set, and…
• Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks
  30 Nov 2020 1 repository listed
  Meta-learning has become a practical approach towards few-shot image classification, where "a strategy to learn a classifier" is meta-learned on labeled base classes and can be applied to tasks with novel classes.
• Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation
  13 Apr 2020 1 repository listed Syntology ran 0 of 13 samples · 13 unverified
  Importantly, we highlight the value and importance of the distribution diversity in the augmentation-based pretext few-shot tasks, which can effectively alleviate the overfitting problem and make the few-shot model…
• Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning
  12 Jan 2020 1 repository listed
  The majority of existing few-shot learning methods describe image relations with binary labels.

Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.