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 2201–2250 of 8378 papers

TitleStatusHype
On the notion of Hallucinations from the lens of Bias and Validity in Synthetic CXR Images—0
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis—0
SqueezeSAM: User friendly mobile interactive segmentation—0
Improving the Robustness of 3D Human Pose Estimation: A Benchmark and Learning from Noisy Input—0
Creating Spoken Dialog Systems in Ultra-Low Resourced Settings—0
Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations—0
Semantic Image Synthesis for Abdominal CT—0
BoschAI @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data AugmentationCode0
Progressive Multi-Modality Learning for Inverse Protein FoldingCode1
Speech and Text-Based Emotion Recognizer—0
Singular Value Penalization and Semantic Data Augmentation for Fully Test-Time Adaptation—0
SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data AugmentationCode1
Temporal Supervised Contrastive Learning for Modeling Patient Risk ProgressionCode0
D3A-TS: Denoising-Driven Data Augmentation in Time SeriesCode1
Data Scarcity in Recommendation Systems: A Survey—0
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D DetectionCode1
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models—0
HandDiffuse: Generative Controllers for Two-Hand Interactions via Diffusion Models—0
Image Synthesis-based Late Stage Cancer Augmentation and Semi-Supervised Segmentation for MRI Rectal Cancer Staging—0
Enhancing Recipe Retrieval with Foundation Models: A Data Augmentation PerspectiveCode1
Understanding Community Bias Amplification in Graph Representation Learning—0
Synthesizing Traffic Datasets using Graph Neural NetworksCode0
SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation—0
A Review On Table Recognition Based On Deep LearningCode0
Stable Diffusion for Data Augmentation in COCO and Weed Datasets—0
A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems—0
Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic SegmentationCode1
OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization—0
Residual Graph Convolutional Network for Bird's-Eye-View Semantic Segmentation—0
Series2Vec: Similarity-based Self-supervised Representation Learning for Time Series ClassificationCode1
Automatic Transcription of Handwritten Old Occitan LanguageCode0
SurfaceAug: Closing the Gap in Multimodal Ground Truth Sampling—0
FAGC:Feature Augmentation on Geodesic Curve in the Pre-Shape Space—0
XAIQA: Explainer-Based Data Augmentation for Extractive Question Answering—0
Indirect Gradient Matching for Adversarial Robust Distillation—0
Text Intimacy Analysis using Ensembles of Multilingual Transformers—0
Leveraging Domain Adaptation and Data Augmentation to Improve Qur'anic IR in English and Arabic—0
GeNIe: Generative Hard Negative Images Through DiffusionCode1
Simplifying Neural Network Training Under Class ImbalanceCode0
FaultFormer: Pretraining Transformers for Adaptable Bearing Fault ClassificationCode0
Re-Nerfing: Improving Novel View Synthesis through Novel View Synthesis—0
TextAug: Test time Text Augmentation for Multimodal Person Re-identification—0
Learning Polynomial Problems with SL(2,R) Equivariance—0
Developing Linguistic Patterns to Mitigate Inherent Human Bias in Offensive Language DetectionCode0
Steerers: A framework for rotation equivariant keypoint descriptorsCode1
A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing—0
Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shifts—0
Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation TechniqueCode0
Toward Improving Robustness of Object Detectors Against Domain ShiftCode1
Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations—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