Zero-Shot Learning

Description from the archive archive 2025-07-28; Papers-with-Code links inside it are rewritten to this site.

Benchmarks archive 2025-07-28

33 leaderboard tables shown for this task, 33 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 33 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

43 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 43 until expanded.

Subtasks archive 2025-07-28

7 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 787 papers with code (1,864 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.

• 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.
• 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.
• 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.
• 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.
• 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.
• CPM: A Large-scale Generative Chinese Pre-trained Language Model
  1 Dec 2020 10 repositories listed
  However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is primarily English, and the parameters are not publicly available.
• Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly
  3 Jul 2017 10 repositories listed
  Due to the importance of zero-shot learning, i.
• Learning Deep Representations of Fine-grained Visual Descriptions
  17 May 2016 9 repositories listed
  State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information.
• Finetuned Language Models Are Zero-Shot Learners
  3 Sep 2021 8 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  We show that instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks.
• Sampling Matters in Deep Embedding Learning
  23 Jun 2017 6 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions.
• Reproducible scaling laws for contrastive language-image learning
  14 Dec 2022 5 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  To address these limitations, we investigate scaling laws for contrastive language-image pre-training (CLIP) with the public LAION dataset and the open-source OpenCLIP repository.
• LAION-5B: An open large-scale dataset for training next generation image-text models
  16 Oct 2022 5 repositories listed Syntology ran 4 of 18 samples · 14 unverified · 3 pointer-only (licence)
  We show successful replication and fine-tuning of foundational models like CLIP, GLIDE and Stable Diffusion using the dataset, and discuss further experiments enabled with an openly available dataset of this scale.
• 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.
• Improving zero-shot learning by mitigating the hubness problem
  20 Dec 2014 5 repositories listed
  The zero-shot paradigm exploits vector-based word representations extracted from text corpora with unsupervised methods to learn general mapping functions from other feature spaces onto word space, where the words…
• LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
  25 Aug 2024 4 repositories listed Syntology ran 1 of 9 samples · 8 unverified
  Zero-shot graph machine learning, especially with graph neural networks (GNNs), has garnered significant interest due to the challenge of scarce labeled data.
• CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale
  27 May 2024 4 repositories listed Syntology ran 4 of 12 samples · 8 unverified
  Measuring biodiversity is crucial for understanding ecosystem health.
• Your Diffusion Model is Secretly a Zero-Shot Classifier
  28 Mar 2023 4 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 1 pointer-only (licence)
  Our generative approach to classification, which we call Diffusion Classifier, attains strong results on a variety of benchmarks and outperforms alternative methods of extracting knowledge from diffusion models.
• Prompt Injection: Parameterization of Fixed Inputs
  31 May 2022 4 repositories listed Syntology ran 1 of 3 samples · 2 unverified
  Through these explorations, we show that PI can be a promising direction for conditioning language models, especially in scenarios with long and fixed prompts.
• Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm
  11 Oct 2021 4 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Recently, large-scale Contrastive Language-Image Pre-training (CLIP) has attracted unprecedented attention for its impressive zero-shot recognition ability and excellent transferability to downstream tasks.
• Zero-shot User Intent Detection via Capsule Neural Networks
  2 Sep 2018 4 repositories listed
  User intent detection plays a critical role in question-answering and dialog systems.
• Feature Generating Networks for Zero-Shot Learning
  4 Dec 2017 4 repositories listed
  Suffering from the extreme training data imbalance between seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging generalized zero-shot learning…
• Semantic Autoencoder for Zero-Shot Learning
  26 Apr 2017 4 repositories listed
  We show that with this additional reconstruction constraint, the learned projection function from the seen classes is able to generalise better to the new unseen classes.
• Learning a Deep Embedding Model for Zero-Shot Learning
  15 Nov 2016 4 repositories listed
  In this paper we argue that the key to make deep ZSL models succeed is to choose the right embedding space.
• A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges
  4 Jan 2025 3 repositories listed
  Multimodal Vision Language Models (VLMs) have emerged as a transformative topic at the intersection of computer vision and natural language processing, enabling machines to perceive and reason about the world through…
• MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training
  28 Nov 2023 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  We further demonstrate the effectiveness of our multi-modal reinforced training by training a CLIP model based on ViT-B/16 image backbone and achieving +2.
• MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
  28 Nov 2023 3 repositories listed Syntology ran 7 of 10 samples · 3 unverified
  With the rapid development of Multi-modal Large Language Models (MLLMs), a number of diagnostic benchmarks have recently emerged to evaluate the comprehension capabilities of these models.
• AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
  29 Oct 2023 3 repositories listed Syntology ran 14 of 32 samples · 18 unverified
  It is a crucial task when training data is not accessible due to various concerns, eg, data privacy, yet it is challenging since the models need to generalize to anomalies across different domains where the appearance…
• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
  3 Oct 2023 3 repositories listed Syntology ran 7 of 9 samples · 2 unverified
  We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities.

Syntology lines on 21 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.