Few-Shot Image Classification

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

89 leaderboard tables shown for this task, 89 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. 10 shown of 89 until expanded.

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

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

Subtasks archive 2025-07-28

3 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 220 papers with code (353 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.

• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
  9 Mar 2017 85 repositories listed Syntology ran 86 of 154 samples · 68 unverified · 57 pointer-only (licence)
  We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including…
• Learning Transferable Visual Models From Natural Language Supervision
  26 Feb 2021 82 repositories listed Syntology ran 16 of 20 samples · 4 unverified · 16 pointer-only (licence)
  State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
• Prototypical Networks for Few-shot Learning
  15 Mar 2017 43 repositories listed Syntology ran 49 of 64 samples · 15 unverified · 18 pointer-only (licence)
  We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class.
• Matching Networks for One Shot Learning
  13 Jun 2016 26 repositories listed Syntology ran 6 of 16 samples · 10 unverified · 6 pointer-only (licence)
  Our algorithm improves one-shot accuracy on ImageNet from 87.
• Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
  7 Mar 2019 15 repositories listed Syntology ran 4 of 11 samples · 7 unverified
  Few-shot classification refers to learning a classifier for new classes given only a few examples.
• On First-Order Meta-Learning Algorithms
  8 Mar 2018 13 repositories listed Syntology ran 24 of 42 samples · 18 unverified · 13 pointer-only (licence)
  This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.
• Learning to Compare: Relation Network for Few-Shot Learning
  16 Nov 2017 13 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 1 pointer-only (licence)
  Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network.
• How to train your MAML
  22 Oct 2018 10 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  The field of few-shot learning has recently seen substantial advancements.
• ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
  19 Apr 2022 9 repositories listed Syntology ran 4 of 20 samples · 16 unverified
  In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks.
• Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
  1 Oct 2018 9 repositories listed Syntology ran 5 of 9 samples · 4 unverified · 4 pointer-only (licence)
  Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances.
• Charting the Right Manifold: Manifold Mixup for Few-shot Learning
  28 Jul 2019 8 repositories listed
  A recent regularization technique - Manifold Mixup focuses on learning a general-purpose representation, robust to small changes in the data distribution.
• Meta-Learning with Differentiable Convex Optimization
  7 Apr 2019 7 repositories listed
  We propose to use these predictors as base learners to learn representations for few-shot learning and show they offer better tradeoffs between feature size and performance across a range of few-shot recognition…
• Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
  6 Jun 2020 6 repositories listed Syntology ran 10 of 26 samples · 16 unverified · 11 pointer-only (licence)
  Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples.
• SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
  12 Nov 2019 6 repositories listed Syntology ran 3 of 9 samples · 6 unverified
  Few-shot learners aim to recognize new object classes based on a small number of labeled training examples.
• Meta-Learning with Implicit Gradients
  10 Sep 2019 6 repositories listed Syntology ran 27 of 36 samples · 9 unverified · 10 pointer-only (licence)
  By drawing upon implicit differentiation, we develop the implicit MAML algorithm, which depends only on the solution to the inner level optimization and not the path taken by the inner loop optimizer.
• Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions
  10 Dec 2018 6 repositories listed
  Many few-shot learning methods address this challenge by learning an instance embedding function from seen classes and apply the function to instances from unseen classes with limited labels.
• PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
  26 Apr 2021 5 repositories listed
  To enhance the generalization ability of PanGu-α, we collect 1.
• Universal Representation Learning from Multiple Domains for Few-shot Classification
  25 Mar 2021 5 repositories listed Syntology ran 12 of 26 samples · 14 unverified
  In this paper, we look at the problem of few-shot classification that aims to learn a classifier for previously unseen classes and domains from few labeled samples.
• DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning
  15 Mar 2020 5 repositories listed Syntology ran 2 of 7 samples · 5 unverified
  We employ the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations to determine image relevance.
• Towards a Neural Statistician
  7 Jun 2016 5 repositories listed Syntology ran 2 of 14 samples · 12 unverified
  We refer to our model as a neural statistician, and by this we mean a neural network that can learn to compute summary statistics of datasets without supervision.
• Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
  23 Jun 2023 4 repositories listed
  Despite its simplicity this baseline is competitive with meta-learning methods on a variety of conditions and is able to imitate target policies trained on unseen variations of the original environment.
• Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022
  1 Oct 2022 4 repositories listed
  In this work, we present the winning solution for ORBIT Few-Shot Video Object Recognition Challenge 2022.
• 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.
• Cross-domain Few-shot Learning with Task-specific Adapters
  1 Jul 2021 4 repositories listed
  In this paper, we look at the problem of cross-domain few-shot classification that aims to learn a classifier from previously unseen classes and domains with few labeled samples.
• Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
  3 Oct 2019 4 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 1 pointer-only (licence)
  On VOC07 testbed for few-shot image classification tasks on ImageNet with transfer learning (Goyal et al., 2019), replacing the linear SVM currently used with a Convolutional NTK SVM consistently improves performance.
• Edge-labeling Graph Neural Network for Few-shot Learning
  4 May 2019 4 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning.
• Dynamic Few-Shot Visual Learning without Forgetting
  25 Apr 2018 4 repositories listed Syntology ran 2 of 13 samples · 11 unverified
  In this context, the goal of our work is to devise a few-shot visual learning system that during test time it will be able to efficiently learn novel categories from only a few training data while at the same time it…
• A Simple Neural Attentive Meta-Learner
  11 Jul 2017 4 repositories listed Syntology ran 0 of 3 samples · 3 unverified · 3 pointer-only (licence)
  Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task.
• Low-shot Visual Recognition by Shrinking and Hallucinating Features
  9 Jun 2016 4 repositories listed
  Low-shot visual learning---the ability to recognize novel object categories from very few examples---is a hallmark of human visual intelligence.
• Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification
  16 Feb 2023 3 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks.

Syntology lines on 22 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.