SOTAVerified

Data Augmentation

Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the original dataset. Data augmentation not only helps to grow the dataset but it also increases the diversity of the dataset. When training machine learning models, data augmentation acts as a regularizer and helps to avoid overfitting.

Data augmentation techniques have been found useful in domains like NLP and computer vision. In computer vision, transformations like cropping, flipping, and rotation are used. In NLP, data augmentation techniques can include swapping, deletion, random insertion, among others.

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Papers

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Showing 2001–2050 of 8378 papers

TitleStatusHype
Data Augmentation by Pairing Samples for Images Classification—0
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes—0
Data Augmentation Can Improve Robustness—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
Contrastive Weighted Learning for Near-Infrared Gaze Estimation—0
Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering—0
A Case Study of Efficacy and Challenges in Practical Human-in-Loop Evaluation of NLP Systems Using Checklist—0
Contrastive Visual Data Augmentation—0
Data Augmentation for Brain-Computer Interfaces: Analysis on Event-Related Potentials Data—0
A Morphologically-Aware Dictionary-based Data Augmentation Technique for Machine Translation of Under-Represented Languages—0
Adding Instructions during Pretraining: Effective Way of Controlling Toxicity in Language Models—0
Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs—0
Contrastive Unsupervised Learning of World Model with Invariant Causal Features—0
Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory—0
AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution—0
2nd Place Solution for ICCV 2021 VIPriors Image Classification Challenge: An Attract-and-Repulse Learning Approach—0
Defect Detection Network In PCB Circuit Devices Based on GAN Enhanced YOLOv11—0
Data augmentation for dealing with low sampling rates in NILM—0
Defending against Model Inversion Attacks via Random Erasing—0
Contrastive Self-supervised Learning for Graph Classification—0
Asymptotically exact data augmentation: models, properties and algorithms—0
Contrastive Representation Learning for Acoustic Parameter Estimation—0
Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection—0
Adaptive Multi-layer Contrastive Graph Neural Networks—0
E-Stitchup: Data Augmentation for Pre-Trained Embeddings—0
Accurate Face Detection for High Performance—0
Data Augmentation for Depression Detection Using Skeleton-Based Gait Information—0
Data Augmentation for Detection of Architectural Distortion in Digital Mammography using Deep Learning Approach—0
Contrastive Psudo-supervised Classification for Intra-Pulse Modulation of Radar Emitter Signals Using data augmentation—0
Data augmentation for efficient learning from parametric experts—0
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding—0
Asymmetric Clean Segments-Guided Self-Supervised Learning for Robust Speaker Verification—0
A Survey on Semantics in Automated Data Science—0
Data Augmentation for End-to-end Code-switching Speech Recognition—0
Data Augmentation for End-to-End Speech Translation: FBK@IWSLT ‘19—0
Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models—0
Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective—0
Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification—0
aiai at the FinSBD-2 Task: Sentence, list and Item Boundary Detection and Items classification of Financial Texts Using Data Augmentation and Attention—0
Adaptive Label Smoothing for Out-of-Distribution Detection—0
Data Augmentation for Histopathological Images Based on Gaussian-Laplacian Pyramid Blending—0
A Unified Framework for Generative Data Augmentation: A Comprehensive Survey—0
Contrastive-mixup learning for improved speaker verification—0
A Unified Gradient Regularization Family for Adversarial Examples—0
A Survey on SAR ship classification using Deep Learning—0
Data Augmentation for Improving the Prediction of Validity and Novelty of Argumentative Conclusions—0
A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification—0
Contrastive Learning with Negative Sampling Correction—0
A Survey on Neural Architecture Search—0
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch—0
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All datasetsImageNetCIFAR-10GA1457

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeiT-B (+MixPro)Accuracy (%)82.9—Unverified
2ResNet-200 (DeepAA)Accuracy (%)81.32—Unverified
3DeiT-S (+MixPro)Accuracy (%)81.3—Unverified
4ResNet-200 (Fast AA)Accuracy (%)80.6—Unverified
5ResNet-200 (UA)Accuracy (%)80.4—Unverified
6ResNet-200 (AA)Accuracy (%)80—Unverified
7ResNet-50 (DeepAA)Accuracy (%)78.3—Unverified
8ResNet-50 (TA wide)Accuracy (%)78.07—Unverified
9ResNet-50 (LoRot-E)Accuracy (%)77.72—Unverified
10ResNet-50 (LoRot-I)Accuracy (%)77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1WideResNet-40-2 (Faster AA)Percentage error3.7—Unverified
2Shake-Shake (26 2×32d) (Faster AA)Percentage error2.7—Unverified
3WideResNet-28-10 (Faster AA)Percentage error2.6—Unverified
4Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×96d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified