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.

Further readings:

( Image credit: Albumentations )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5251–5300 of 8378 papers

TitleStatusHype
Network Augmentation for Tiny Deep Learning—0
Can Synthetic Translations Improve Bitext Quality?—0
Curriculum Data Augmentation for Low-Resource Slides Summarization—0
Can Language Models Take A Hint? Prompting for Controllable Contextualized Commonsense Inference—0
Towards Robust Waveform-Based Acoustic Models—0
Virtual Augmentation Supported Contrastive Learning of Sentence RepresentationsCode1
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language ModelsCode1
Unifying Cross-lingual Summarization and Machine Translation with Compression RateCode0
Reappraising Domain Generalization in Neural Networks—0
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without RetrainingCode1
Towards Identity Preserving Normal to Dysarthric Voice Conversion—0
From Start to Finish: Latency Reduction Strategies for Incremental Speech Synthesis in Simultaneous Speech-to-Speech Translation—0
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation—0
Interpreting the Robustness of Neural NLP Models to Textual Perturbations—0
Augmenting Imitation Experience via Equivariant Representations—0
Semantically Distributed Robust Optimization for Vision-and-Language InferenceCode0
Retrieval-guided Counterfactual Generation for QA—0
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking—0
Deep learning models for predicting RNA degradation via dual crowdsourcingCode0
DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models—0
Domain generalization in deep learning for contrast-enhanced imaging—0
IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance—0
Nuisance-Label Supervision: Robustness Improvement by Free Labels—0
Context-gloss Augmentation for Improving Word Sense Disambiguation—0
Style-based quantum generative adversarial networks for Monte Carlo eventsCode1
Fast Hand Detection in Collaborative Learning Environments—0
False Negative Distillation and Contrastive Learning for Personalized Outfit Recommendation—0
2D Multi-Class Model for Gray and White Matter Segmentation of the Cervical Spinal Cord at 7TCode0
LENS: Localization enhanced by NeRF synthesis—0
SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue SystemsCode1
Doubly-Trained Adversarial Data Augmentation for Neural Machine TranslationCode0
Investigation on Data Adaptation Techniques for Neural Named Entity Recognition—0
UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-TrainingCode1
Spatial mixup: Directional loudness modification as data augmentation for sound event localization and detectionCode0
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition—0
Semi-Supervised Semantic Segmentation via Adaptive Equalization LearningCode1
Point Cloud Augmentation with Weighted Local TransformationsCode1
Label-Occurrence-Balanced Mixup for Long-tailed Recognition—0
Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery—0
An evaluation of data augmentation methods for sound scene geotagging—0
Data Augmentation with Locally-time Reversed Speech for Automatic Speech Recognition—0
Learning 3D Representations of Molecular Chirality with Invariance to Bond RotationsCode1
Distinguishing rule- and exemplar-based generalization in learning systemsCode0
Combining Image Features and Patient Metadata to Enhance Transfer Learning—0
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts—0
StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech SynthesisCode1
Towards Accurate Cross-Domain In-Bed Human Pose EstimationCode1
FilterAugment: An Acoustic Environmental Data Augmentation MethodCode1
Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class BoundariesCode1
Spectral Bias in Practice: The Role of Function Frequency in Generalization—0
Show:102550
← PrevPage 106 of 168Next →
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