Data Augmentation

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

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

63 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 63 until expanded.

Subtasks archive 2025-07-28

2 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 3,225 papers with code (8,378 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.

• YOLOv4: Optimal Speed and Accuracy of Object Detection
  23 Apr 2020 223 repositories listed Syntology ran 24 of 184 samples · 160 unverified · 8 pointer-only (licence)
  There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy.
• Improved Baselines with Momentum Contrastive Learning
  9 Mar 2020 36 repositories listed Syntology ran 8 of 43 samples · 35 unverified · 10 pointer-only (licence)
  Contrastive unsupervised learning has recently shown encouraging progress, e.
• AutoAugment: Learning Augmentation Policies from Data
  24 May 2018 33 repositories listed Syntology ran 6 of 43 samples · 37 unverified · 2 pointer-only (licence)
  In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch.
• SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
  18 Apr 2019 30 repositories listed Syntology ran 1 of 18 samples · 17 unverified
  On LibriSpeech, we achieve 6.
• 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.
• 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
  21 Jun 2016 27 repositories listed Syntology ran 53 of 76 samples · 23 unverified · 20 pointer-only (licence)
  This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images.
• EfficientNetV2: Smaller Models and Faster Training
  1 Apr 2021 26 repositories listed Syntology ran 41 of 79 samples · 38 unverified · 10 pointer-only (licence)
  By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87.
• Supervised Contrastive Learning
  23 Apr 2020 26 repositories listed Syntology ran 6 of 23 samples · 17 unverified
  Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state of the art performance in the unsupervised training of deep image models.
• SimCSE: Simple Contrastive Learning of Sentence Embeddings
  18 Apr 2021 23 repositories listed Syntology ran 17 of 30 samples · 13 unverified · 19 pointer-only (licence)
  This paper presents SimCSE, a simple contrastive learning framework that greatly advances state-of-the-art sentence embeddings.
• 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…
• ResMLP: Feedforward networks for image classification with data-efficient training
  7 May 2021 19 repositories listed Syntology ran 2 of 7 samples · 5 unverified
  We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification.
• RandAugment: Practical automated data augmentation with a reduced search space
  30 Sep 2019 19 repositories listed Syntology ran 58 of 65 samples · 7 unverified · 17 pointer-only (licence)
  Additionally, due to the separate search phase, these approaches are unable to adjust the regularization strength based on model or dataset size.
• 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…
• Random Erasing Data Augmentation
  16 Aug 2017 18 repositories listed
  In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).
• DENSER: Deep Evolutionary Network Structured Representation
  4 Jan 2018 17 repositories listed
  Deep Evolutionary Network Structured Representation (DENSER) is a novel approach to automatically design Artificial Neural Networks (ANNs) using Evolutionary Computation.
• How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
  18 Jun 2021 16 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Vision Transformers (ViT) have been shown to attain highly competitive performance for a wide range of vision applications, such as image classification, object detection and semantic image segmentation.
• EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
  31 Jan 2019 16 repositories listed Syntology ran 2 of 13 samples · 11 unverified · 1 pointer-only (licence)
  We present EDA: easy data augmentation techniques for boosting performance on text classification tasks.
• ResNet strikes back: An improved training procedure in timm
  1 Oct 2021 14 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  We share competitive training settings and pre-trained models in the timm open-source library, with the hope that they will serve as better baselines for future work.
• Self-training with Noisy Student improves ImageNet classification
  11 Nov 2019 13 repositories listed Syntology ran 5 of 24 samples · 19 unverified
  During the learning of the student, we inject noise such as dropout, stochastic depth, and data augmentation via RandAugment to the student so that the student generalizes better than the teacher.
• Sampling Generative Networks
  14 Sep 2016 13 repositories listed Syntology ran 1 of 15 samples · 14 unverified
  We introduce several techniques for sampling and visualizing the latent spaces of generative models.
• DeiT III: Revenge of the ViT
  14 Apr 2022 12 repositories listed
  Our evaluations on Image classification (ImageNet-1k with and without pre-training on ImageNet-21k), transfer learning and semantic segmentation show that our procedure outperforms by a large margin previous fully…
• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
  12 Jun 2017 12 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner.
• Fast AutoAugment
  1 May 2019 11 repositories listed Syntology ran 22 of 40 samples · 18 unverified · 3 pointer-only (licence)
  Data augmentation is an essential technique for improving generalization ability of deep learning models.
• ECG arrhythmia classification using a 2-D convolutional neural network
  18 Apr 2018 11 repositories listed Syntology ran 2 of 9 samples · 7 unverified · 2 pointer-only (licence)
  In this paper, we propose an effective electrocardiogram (ECG) arrhythmia classification method using a deep two-dimensional convolutional neural network (CNN) which recently shows outstanding performance in the field…
• TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up
  14 Feb 2021 10 repositories listed Syntology ran 8 of 8 samples · 0 unverified · 7 pointer-only (licence)
  Our vanilla GAN architecture, dubbed TransGAN, consists of a memory-friendly transformer-based generator that progressively increases feature resolution, and correspondingly a multi-scale discriminator to capture…
• Camera Style Adaptation for Person Re-identification
  28 Nov 2017 10 repositories listed Syntology ran 0 of 3 samples · 3 unverified · 3 pointer-only (licence)
  In this paper, we explicitly consider this challenge by introducing camera style (CamStyle) adaptation.
• NiftyNet: a deep-learning platform for medical imaging
  11 Sep 2017 10 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications.
• Exploring Data Augmentation for Multi-Modality 3D Object Detection
  23 Dec 2020 9 repositories listed
  Due to the fact that multi-modality data augmentation must maintain consistency between point cloud and images, recent methods in this field typically use relatively insufficient data augmentation.
• Graph Random Neural Network for Semi-Supervised Learning on Graphs
  22 May 2020 9 repositories listed Syntology ran 8 of 22 samples · 14 unverified
  We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
• Optimizing Millions of Hyperparameters by Implicit Differentiation
  6 Nov 2019 9 repositories listed Syntology ran 3 of 4 samples · 1 unverified
  We propose an algorithm for inexpensive gradient-based hyperparameter optimization that combines the implicit function theorem (IFT) with efficient inverse Hessian approximations.

Syntology lines on 26 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.