Few-Shot Learning

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

27 leaderboard tables shown for this task, 27 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 27 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

45 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 45 until expanded.

Subtasks archive 2025-07-28

12 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 1,297 papers with code (2,964 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…
• Language Models are Few-Shot Learners
  28 May 2020 67 repositories listed Syntology ran 15 of 65 samples · 50 unverified · 4 pointer-only (licence)
  By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
• LLaMA: Open and Efficient Foundation Language Models
  27 Feb 2023 57 repositories listed Syntology ran 26 of 58 samples · 32 unverified · 4 pointer-only (licence)
  We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters.
• 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.
• BioBERT: a pre-trained biomedical language representation model for biomedical text mining
  25 Jan 2019 19 repositories listed Syntology ran 4 of 25 samples · 21 unverified · 1 pointer-only (licence)
  Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
• A Closer Look at Few-shot Classification
  8 Apr 2019 13 repositories listed Syntology ran 7 of 8 samples · 1 unverified · 7 pointer-only (licence)
  Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled 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.
• The Power of Scale for Parameter-Efficient Prompt Tuning
  18 Apr 2021 12 repositories listed Syntology ran 0 of 15 samples · 15 unverified
  More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method "closes the gap" and matches the strong…
• GPT-4 Technical Report
  15 Mar 2023 11 repositories listed Syntology ran 2 of 5 samples · 3 unverified · 1 pointer-only (licence)
  We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
• Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
  9 Mar 2020 10 repositories listed Syntology ran 8 of 12 samples · 4 unverified · 1 pointer-only (licence)
  The edge between these two lines of works has yet been underexplored, and the effectiveness of meta-learning in few-shot learning remains unclear.
• 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.
• Making Pre-trained Language Models Better Few-shot Learners
  31 Dec 2020 9 repositories listed Syntology ran 2 of 9 samples · 7 unverified · 7 pointer-only (licence)
  We present LM-BFF--better few-shot fine-tuning of language models--a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples.
• Big Transfer (BiT): General Visual Representation Learning
  24 Dec 2019 9 repositories listed Syntology ran 3 of 10 samples · 7 unverified
  We conduct detailed analysis of the main components that lead to high transfer performance.
• Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
  31 Jul 2017 9 repositories listed
  In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial.
• 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.
• PaLM: Scaling Language Modeling with Pathways
  5 Apr 2022 7 repositories listed Syntology ran 30 of 37 samples · 7 unverified
  To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM.
• 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…
• Data Augmentation Generative Adversarial Networks
  12 Nov 2017 7 repositories listed Syntology ran 1 of 5 samples · 4 unverified
  The model, based on image conditional Generative Adversarial Networks, takes data from a source domain and learns to take any data item and generalise it to generate other within-class data items.
• Compact Bilinear Pooling
  19 Nov 2015 7 repositories listed
  Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition.
• Free Lunch for Few-shot Learning: Distribution Calibration
  16 Jan 2021 6 repositories listed Syntology ran 4 of 6 samples · 2 unverified · 5 pointer-only (licence)
  In this paper, we calibrate the distribution of these few-sample classes by transferring statistics from the classes with sufficient examples, then an adequate number of examples can be sampled from the calibrated…
• 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.
• Few-Shot Learning with Graph Neural Networks
  10 Nov 2017 6 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not.
• Large Language Models Are Human-Level Prompt Engineers
  3 Nov 2022 5 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  By conditioning on natural language instructions, large language models (LLMs) have displayed impressive capabilities as general-purpose computers.
• Flamingo: a Visual Language Model for Few-Shot Learning
  29 Apr 2022 5 repositories listed Syntology ran 18 of 24 samples · 6 unverified · 7 pointer-only (licence)
  Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research.
• Calibrate Before Use: Improving Few-Shot Performance of Language Models
  19 Feb 2021 5 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  We show that this type of few-shot learning can be unstable: the choice of prompt format, training examples, and even the order of the training examples can cause accuracy to vary from near chance to near…

Syntology lines on 25 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.