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 3651–3700 of 8378 papers

TitleStatusHype
Contrastive Visual Data Augmentation—0
Contrastive Unsupervised Learning of World Model with Invariant Causal Features—0
AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution—0
Adaptive Neural Networks for Intelligent Data-Driven Development—0
A Case Study of Efficacy and Challenges in Practical Human-in-Loop Evaluation of NLP Systems Using Checklist—0
Generative Image Translation for Data Augmentation of Bone Lesion Pathology—0
GAMMA: Generative Augmentation for Attentive Marine Debris Detection—0
GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation—0
Contrastive Self-supervised Learning for Graph Classification—0
Generative Model-Based Fusion for Improved Few-Shot Semantic Segmentation of Infrared Images—0
Asymptotically exact data augmentation: models, properties and algorithms—0
GaitMorph: Transforming Gait by Optimally Transporting Discrete Codes—0
Pedestrian Attribute Editing for Gait Recognition and Anonymization—0
Gait Data Augmentation using Physics-Based Biomechanical Simulation—0
Generative Models for Multi-Illumination Color Constancy—0
Generative Networks for Precision Enthusiasts—0
Contrastive Representation Learning for Acoustic Parameter Estimation—0
Generative Robust Classification—0
Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection—0
Generative Technology for Human Emotion Recognition: A Scope Review—0
Gain from Neighbors: Boosting Model Robustness in the Wild via Adversarial Perturbations Toward Neighboring Classes—0
GADT: Enhancing Transferable Adversarial Attacks through Gradient-guided Adversarial Data Transformation—0
Contrastive Psudo-supervised Classification for Intra-Pulse Modulation of Radar Emitter Signals Using data augmentation—0
GABO: Graph Augmentations with Bi-level Optimization—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
GenLabel: Mixup Relabeling using Generative Models—0
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning—0
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification—0
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing—0
FUSSL: Fuzzy Uncertain Self Supervised Learning—0
A Survey on Semantics in Automated Data Science—0
GenX: Mastering Code and Test Generation with Execution Feedback—0
GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction—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
Geometric and Physical Constraints Synergistically Enhance Neural PDE Surrogates—0
Adaptive Multi-layer Contrastive Graph Neural Networks—0
FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots—0
Contrastive-mixup learning for improved speaker verification—0
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification—0
A Survey on SAR ship classification using Deep Learning—0
FUSED-Net: Detecting Traffic Signs with Limited Data—0
Further advantages of data augmentation on convolutional neural networks—0
Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition—0
Contrastive Learning with Negative Sampling Correction—0
A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification—0
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation—0
Functional Space Analysis of Local GAN Convergence—0
Fully Test-time Adaptation for Tabular Data—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