Generalized Few-Shot Learning

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 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

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

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

9 shown of 9 papers with code (13 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.

• 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.
• Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning
  7 Jun 2019 2 repositories listed Syntology ran 3 of 6 samples · 3 unverified
  In this paper, we investigate the problem of generalized few-shot learning (GFSL) -- a model during the deployment is required to learn about tail categories with few shots and simultaneously classify the head classes.
• Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
  5 Dec 2018 2 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space.
• Better Generalized Few-Shot Learning Even Without Base Data
  29 Nov 2022 1 repository listed
  In this paper, we overcome this limitation by proposing a simple yet effective normalization method that can effectively control both mean and variance of the weight distribution of novel classes without using any base…
• Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection
  14 Dec 2021 1 repository listed
  Our method achieves state-of-the-art results on 1-shot and 5-shot intent detection task with gains ranging from 2-8\% points in F1 score on four benchmark datasets.
• Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection
  6 Oct 2020 1 repository listed
  Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen training classes to novel testing classes, they rely on a static similarity measure and…
• Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation
  9 Jul 2020 1 repository listed
  Few-shot learning aims to recognize novel classes from a few examples.
• From Generalized zero-shot learning to long-tail with class descriptors
  5 Apr 2020 1 repository listed
  Real-world data is predominantly unbalanced and long-tailed, but deep models struggle to recognize rare classes in the presence of frequent classes.
• Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
  1 Jun 2019 1 repository listed
  Many approaches in generalized zero-shot learning rely on cross-modal mapping between the image feature space and the class embedding space.

Syntology lines on 2 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.