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 3201–3250 of 8378 papers

TitleStatusHype
GAN based Data Augmentation to Resolve Class Imbalance—0
Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis—0
A Novel Data Augmentation Tool for Enhancing Machine Learning Classification: A New Application of the Higher Order Dynamic Mode Decomposition for Improved Cardiac Disease Identification—0
Adversarial Bone Length Attack on Action Recognition—0
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks—0
Exploring the Power of Pure Attention Mechanisms in Blind Room Parameter Estimation—0
Distilling Transformers for Neural Cross-Domain Search—0
ColorUNet: A convolutional classification approach to colorization—0
Color Variants Identification in Fashion e-commerce via Contrastive Self-Supervised Representation Learning—0
Exploring the Utility of Self-Supervised Pretraining Strategies for the Detection of Absent Lung Sliding in M-Mode Lung Ultrasound—0
GAN-based Data Augmentation for Chest X-ray Classification—0
ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation—0
Exploring Variational Autoencoders for Medical Image Generation: A Comprehensive Study—0
Exploring WavLM Back-ends for Speech Spoofing and Deepfake Detection—0
Exploring Zero and Few-shot Techniques for Intent Classification—0
Combination of multiple neural networks using transfer learning and extensive geometric data augmentation for assessing cellularity scores in histopathology images—0
Extended Labeled Faces in-the-Wild (ELFW): Augmenting Classes for Face Segmentation—0
Extending Temporal Data Augmentation for Video Action Recognition—0
Extensive Studies of the Neutron Star Equation of State from the Deep Learning Inference with the Observational Data Augmentation—0
Distilling Large Language Models into Tiny and Effective Students using pQRNN—0
External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation—0
Extracting knowledge from features with multilevel abstraction—0
Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset—0
Extracting Targeted Training Data from ASR Models, and How to Mitigate It—0
Combining High-Level Features of Raw Audio Waves and Mel-Spectrograms for Audio Tagging—0
Extraction of Medication Names from Twitter Using Augmentation and an Ensemble of Language Models—0
ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization—0
Extreme Augmentation : Can deep learning based medical image segmentation be trained using a single manually delineated scan?—0
A Comprehensive Framework for Semantic Similarity Analysis of Human and AI-Generated Text Using Transformer Architectures and Ensemble Techniques—0
Augmenting Offline Reinforcement Learning with State-only Interactions—0
Distilling Calibrated Student from an Uncalibrated Teacher—0
EyeBAG: Accurate Control of Eye Blink and Gaze Based on Data Augmentation Leveraging Style Mixing—0
Facebook AI's WMT20 News Translation Task Submission—0
Facebook AI’s WMT20 News Translation Task Submission—0
Face Emotion Recognization Using Dataset Augmentation Based on Neural Network—0
FaceMixup: Enhancing Facial Expression Recognition through Mixed Face Regularization—0
Face morphing detection in the presence of printing/scanning and heterogeneous image sources—0
FaceSaliencyAug: Mitigating Geographic, Gender and Stereotypical Biases via Saliency-Based Data Augmentation—0
Combining Weakly Supervised ML Techniques for Low-Resource NLU—0
Facial Recognition Leveraging Generative Adversarial Networks—0
Facial Surgery Preview Based on the Orthognathic Treatment Prediction—0
Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement—0
Bora: Biomedical Generalist Video Generation Model—0
GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation—0
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing—0
Factual Dialogue Summarization via Learning from Large Language Models—0
Distillation Using Oracle Queries for Transformer-Based Human-Object Interaction Detection—0
FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis—0
ComOM at VLSP 2023: A Dual-Stage Framework with BERTology and Unified Multi-Task Instruction Tuning Model for Vietnamese Comparative Opinion Mining—0
Distillation of Diffusion Features for Semantic Correspondence—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