Semi-Supervised Image Classification

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

60 leaderboard tables shown for this task, 58 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 60 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

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

Subtasks archive 2025-07-28

2 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 130 papers with code (167 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.

• A Simple Framework for Contrastive Learning of Visual Representations
  13 Feb 2020 96 repositories listed Syntology ran 79 of 137 samples · 58 unverified · 52 pointer-only (licence)
  This paper presents SimCLR: a simple framework for contrastive learning of visual representations.
• 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.
• mixup: Beyond Empirical Risk Minimization
  25 Oct 2017 71 repositories listed Syntology ran 30 of 47 samples · 17 unverified · 15 pointer-only (licence)
  We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks.
• Improved Techniques for Training GANs
  10 Jun 2016 46 repositories listed Syntology ran 1 of 2 samples · 1 unverified
  We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.
• Bootstrap your own latent: A new approach to self-supervised Learning
  13 Jun 2020 31 repositories listed Syntology ran 62 of 79 samples · 17 unverified · 46 pointer-only (licence)
  From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view.
• MixMatch: A Holistic Approach to Semi-Supervised Learning
  6 May 2019 30 repositories listed Syntology ran 39 of 63 samples · 24 unverified · 32 pointer-only (licence)
  Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets.
• Representation Learning with Contrastive Predictive Coding
  10 Jul 2018 28 repositories listed Syntology ran 29 of 45 samples · 16 unverified · 22 pointer-only (licence)
  The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models.
• Improved Regularization of Convolutional Neural Networks with Cutout
  15 Aug 2017 28 repositories listed Syntology ran 21 of 24 samples · 3 unverified · 5 pointer-only (licence)
  Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks.
• FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
  21 Jan 2020 26 repositories listed Syntology ran 50 of 74 samples · 24 unverified · 14 pointer-only (licence)
  Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance.
• Barlow Twins: Self-Supervised Learning via Redundancy Reduction
  4 Mar 2021 24 repositories listed Syntology ran 21 of 26 samples · 5 unverified · 10 pointer-only (licence)
  This causes the embedding vectors of distorted versions of a sample to be similar, while minimizing the redundancy between the components of these vectors.
• Unsupervised Data Augmentation for Consistency Training
  29 Apr 2019 20 repositories listed Syntology ran 15 of 52 samples · 37 unverified · 9 pointer-only (licence)
  In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in…
• Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
  17 Jun 2020 18 repositories listed Syntology ran 13 of 17 samples · 4 unverified · 6 pointer-only (licence)
  In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements…
• Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
  13 Apr 2017 14 repositories listed Syntology ran 11 of 13 samples · 2 unverified · 11 pointer-only (licence)
  In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets.
• Semi-Supervised Learning with Ladder Networks
  9 Jul 2015 10 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  We combine supervised learning with unsupervised learning in deep neural networks.
• Big Self-Supervised Models are Strong Semi-Supervised Learners
  17 Jun 2020 9 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  The proposed semi-supervised learning algorithm can be summarized in three steps: unsupervised pretraining of a big ResNet model using SimCLRv2, supervised fine-tuning on a few labeled examples, and distillation with…
• Meta Pseudo Labels
  23 Mar 2020 9 repositories listed Syntology ran 5 of 14 samples · 9 unverified
  We present Meta Pseudo Labels, a semi-supervised learning method that achieves a new state-of-the-art top-1 accuracy of 90.
• Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
  6 Mar 2017 8 repositories listed Syntology ran 6 of 6 samples · 0 unverified · 6 pointer-only (licence)
  Without changing the network architecture, Mean Teacher achieves an error rate of 4.
• Self-Supervised Learning of Pretext-Invariant Representations
  4 Dec 2019 7 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations for a large training set of images.
• Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
  19 Nov 2016 7 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss.
• Temporal Ensembling for Semi-Supervised Learning
  7 Oct 2016 7 repositories listed Syntology ran 3 of 7 samples · 4 unverified · 2 pointer-only (licence)
  In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled.
• FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
  15 May 2022 6 repositories listed Syntology ran 4 of 14 samples · 10 unverified · 2 pointer-only (licence)
  Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.
• VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
  11 May 2021 6 repositories listed Syntology ran 11 of 17 samples · 6 unverified · 1 pointer-only (licence)
  Recent self-supervised methods for image representation learning are based on maximizing the agreement between embedding vectors from different views of the same image.
• USB: A Unified Semi-supervised Learning Benchmark for Classification
  12 Aug 2022 5 repositories listed Syntology ran 9 of 9 samples · 0 unverified
  We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
• With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
  29 Apr 2021 4 repositories listed Syntology ran 4 of 5 samples · 1 unverified
  On semi-supervised learning benchmarks we improve performance significantly when only 1% ImageNet labels are available, from 53.
• Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples
  28 Apr 2021 4 repositories listed Syntology ran 7 of 21 samples · 14 unverified · 3 pointer-only (licence)
  This paper proposes a novel method of learning by predicting view assignments with support samples (PAWS).
• Large Scale Adversarial Representation Learning
  4 Jul 2019 4 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation…
• Data-Efficient Image Recognition with Contrastive Predictive Coding
  22 May 2019 4 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge.
• Interpolation Consistency Training for Semi-Supervised Learning
  9 Mar 2019 4 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm.
• Unsupervised Feature Learning via Non-Parametric Instance Discrimination
  1 Jun 2018 4 repositories listed
  Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so.
• OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning
  21 Dec 2020 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  With this in mind, we propose a teacher-student scheme to learn representations by training a convolutional net to reconstruct a bag-of-visual-words (BoW) representation of an image, given as input a perturbed version…

Syntology lines on 29 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.