cross-domain few-shot learning

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 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

1 dataset 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

30 shown of 37 papers with code (74 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.
• 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.
• StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning
  18 Feb 2023 2 repositories listed
  Thus, inspired by vanilla adversarial learning, a novel model-agnostic meta Style Adversarial training (StyleAdv) method together with a novel style adversarial attack method is proposed for CD-FSL.
• Universal Representations: A Unified Look at Multiple Task and Domain Learning
  6 Apr 2022 2 repositories listed
  We propose a unified look at jointly learning multiple vision tasks and visual domains through universal representations, a single deep neural network.
• Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty
  1 Feb 2022 2 repositories listed Syntology ran 5 of 7 samples · 2 unverified
  This data enables self-supervised pre-training on the target domain, in addition to supervised pre-training on the source domain.
• Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
  8 Jan 2021 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Current state-of-the-art few-shot learners focus on developing effective training procedures for feature representations, before using simple, e.
• Cross-Domain Few-Shot Learning by Representation Fusion
  13 Oct 2020 2 repositories listed Syntology ran 1 of 8 samples · 7 unverified
  On the few-shot datasets miniImagenet and tieredImagenet with small domain shifts, CHEF is competitive with state-of-the-art methods.
• A Broader Study of Cross-Domain Few-Shot Learning
  16 Dec 2019 2 repositories listed Syntology ran 4 of 7 samples · 3 unverified
  Extensive experiments on the proposed benchmark are performed to evaluate state-of-art meta-learning approaches, transfer learning approaches, and newer methods for cross-domain few-shot learning.
• 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.
• Enhancing Masked Time-Series Modeling via Dropping Patches
  19 Dec 2024 1 repository listed
  This paper explores how to enhance existing masked time-series modeling by randomly dropping sub-sequence level patches of time series.
• Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot Learning
  15 Nov 2024 1 repository listed
  This paper addresses the source-free CDFSL (SF-CDFSL) problem, tackling few-shot learning (FSL) in the target domain using only pre-trained models and a few target samples without source data or strategies.
• Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting
  24 Jun 2024 1 repository listed Syntology ran 5 of 9 samples · 4 unverified · 9 pointer-only (licence)
  However, most existing methods pay more attention to learning domain-adaptive inductive bias (meta-knowledge) through feature-wise manipulation or task diversity improvement while neglecting the phenomenon that deep…
• Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot Learning
  21 Jun 2024 1 repository listed
  Inspired by the success of vanilla adversarial learning in Cross-Domain Few-Shot Learning, we take a step further and devise a model-agnostic and Primitive-Based Adversarial training (PBadv) method to deal with this…
• Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning
  7 Mar 2024 1 repository listed Syntology ran 6 of 7 samples · 1 unverified · 1 pointer-only (licence)
  In this paper, we look at cross-domain few-shot classification which presents the challenging task of learning new classes in previously unseen domains with few labelled examples.
• Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning
  4 Mar 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  For this reason, this paper explores a Source-Free CDFSL (SF-CDFSL) problem, in which CDFSL is addressed through the use of existing pretrained models instead of training a model with source data, avoiding accessing…
• Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning
  1 Mar 2024 1 repository listed Syntology ran 8 of 11 samples · 3 unverified · 11 pointer-only (licence)
  To enhance the transferability and facilitate fine-tuning, we introduce a simple yet effective approach to achieve long-range flattening of the minima in the loss landscape.
• Cross-Domain Few-Shot Learning via Adaptive Transformer Networks
  25 Jan 2024 1 repository listed
  Most few-shot learning works rely on the same domain assumption between the base and the target tasks, hindering their practical applications.
• Leveraging Normalization Layer in Adapters With Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot Learning
  18 Dec 2023 1 repository listed
  Second, to address the pitfalls of noisy statistics, we deploy two strategies: a progressive training of the two adapters and an adaptive distillation technique derived from features determined by the model solely with…
• Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification
  2 Nov 2023 1 repository listed
  In addition, it adopts a transformer based cross-attention learning module to learn the set-level sample relations and acquire the attention from query samples to support samples.
• Domain Adaptive Few-Shot Open-Set Learning
  22 Sep 2023 1 repository listed
  Few-shot learning has made impressive strides in addressing the crucial challenges of recognizing unknown samples from novel classes in target query sets and managing visual shifts between domains.
• CDFSL-V: Cross-Domain Few-Shot Learning for Videos
  7 Sep 2023 1 repository listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  To address this issue, in this work, we propose a novel cross-domain few-shot video action recognition method that leverages self-supervised learning and curriculum learning to balance the information from the source…
• Dual Adaptive Representation Alignment for Cross-domain Few-shot Learning
  18 Jun 2023 1 repository listed
  Recent progress in this setting assumes that the base knowledge and novel query samples are distributed in the same domains, which are usually infeasible for realistic applications.
• Revisiting Prototypical Network for Cross Domain Few-Shot Learning
  1 Jan 2023 1 repository listed
  Prototypical Network is a popular few-shot solver that aims at establishing a feature metric generalizable to novel few-shot classification (FSC) tasks using deep neural networks.
• TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning
  11 Oct 2022 1 repository listed
  The proposed TGDM framework contains a Mixup-3T network for learning classifiers and a dynamic ratio generation network (DRGN) for learning the optimal mix ratio.
• ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning
  11 Oct 2022 1 repository listed
  Concretely, to solve the data imbalance problem between the source data with sufficient examples and the auxiliary target data with limited examples, we build our model under the umbrella of multi-expert learning.
• Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition
  16 Jul 2022 1 repository listed
  Extensive experiments on both in-the-lab and in-the-wild compound expression datasets demonstrate the superiority of our proposed CDNet against several state-of-the-art FSL methods.
• Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification
  30 Jun 2022 1 repository listed
  The IDE-block is used to characterize and aggregate the intradomain nonlocal relationships and the interdomain feature and distribution similarities are captured in the CSA-block.
• Feature Extractor Stacking for Cross-domain Few-shot Learning
  12 May 2022 1 repository listed
  Recently published CDFSL methods generally construct a universal model that combines knowledge of multiple source domains into one feature extractor.
• Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning
  15 Mar 2022 1 repository listed
  The key challenge of CD-FSL lies in the huge data shift between source and target domains, which is typically in the form of totally different visual styles.
• Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder
  11 Aug 2021 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  State of the art (SOTA) few-shot learning (FSL) methods suffer significant performance drop in the presence of domain differences between source and target datasets.

Syntology lines on 12 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.