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 4651–4700 of 8378 papers

TitleStatusHype
Selecting task with optimal transport self-supervised learning for few-shot classification—0
ObjectMix: Data Augmentation by Copy-Pasting Objects in Videos for Action Recognition—0
SingAug: Data Augmentation for Singing Voice Synthesis with Cycle-consistent Training Strategy—0
A Survey of Robust 3D Object Detection Methods in Point Clouds—0
SimVQA: Exploring Simulated Environments for Visual Question Answering—0
Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword SpottingCode1
Robust Disentangled Variational Speech Representation Learning for Zero-shot Voice ConversionCode1
Clozer: Adaptable Data Augmentation for Cloze-style Reading Comprehension—0
Federated Domain Adaptation for ASR with Full Self-Supervision—0
Counterfactual Cycle-Consistent Learning for Instruction Following and Generation in Vision-Language NavigationCode1
Overcoming challenges in leveraging GANs for few-shot data augmentationCode1
Learning Instance-Specific Adaptation for Cross-Domain Segmentation—0
On Uncertainty, Tempering, and Data Augmentation in Bayesian ClassificationCode1
Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels—0
ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversionCode1
VGGIN-Net: Deep Transfer Network for Imbalanced Breast Cancer DatasetCode1
Supervised Graph Contrastive Learning for Few-shot Node Classification—0
EnvEdit: Environment Editing for Vision-and-Language NavigationCode1
Decomposed Temporal Dynamic CNN: Efficient Time-Adaptive Network for Text-Independent Speaker Verification Explained with Speaker Activation MapCode0
Neural representation of a time optimal, constant acceleration rendezvous—0
Improving Generalization of Deep Neural Network Acoustic Models with Length Perturbation and N-best Based Label Smoothing—0
Physics-informed deep-learning applications to experimental fluid mechanics—0
Improving Persian Relation Extraction Models by Data Augmentation—0
Robust Speaker Recognition with Transformers Using wav2vec 2.0—0
Improved singing voice separation with chromagram-based pitch-aware remixing—0
Leveraging Clinically Relevant Biometric Constraints To Supervise A Deep Learning Model For The Accurate Caliper Placement To Obtain Sonographic Measurements Of The Fetal Brain—0
Hierarchical Transformer Model for Scientific Named Entity RecognitionCode0
Investigating Active-learning-based Training Data Selection for Speech Spoofing Countermeasure—0
bitsa_nlp@LT-EDI-ACL2022: Leveraging Pretrained Language Models for Detecting Homophobia and Transphobia in Social Media CommentsCode0
Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry ConstraintsCode1
Towards physiology-informed data augmentation for EEG-based BCIs—0
A Spiking Neural Network based on Neural Manifold for Augmenting Intracortical Brain-Computer Interface Data—0
How Do We Fail? Stress Testing Perception in Autonomous VehiclesCode0
Data Augmentation Strategies for Improving Sequential Recommender Systems—0
Metropolis-Hastings Data Augmentation for Graph Neural Networks—0
Reverse Engineering of Imperceptible Adversarial Image PerturbationsCode1
Improving Contrastive Learning with Model AugmentationCode1
Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalizationCode0
Impact of Dataset on Acoustic Models for Automatic Speech Recognition—0
Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality ReductionCode1
SMARAGD: Learning SMatch for Accurate and Rapid Approximate Graph DistanceCode0
Prompt-based System for Personality and Interpersonal Reactivity Prediction—0
Transformer-based Multimodal Information Fusion for Facial Expression Analysis—0
A Method of Data Augmentation to Train a Small Area Fingerprint Recognition Deep Neural Network with a Normal Fingerprint Database—0
Improving Neural Predictivity in the Visual Cortex with Gated Recurrent Connections—0
A New Approach to Improve Learning-based Deepfake Detection in Realistic Conditions—0
Mask Usage Recognition using Vision Transformer with Transfer Learning and Data Augmentation—0
Generative Modeling Helps Weak Supervision (and Vice Versa)Code0
A Novel Framework for Assessment of Learning-based Detectors in Realistic Conditions with Application to Deepfake Detection—0
Conditional Generative Data Augmentation for Clinical Audio Datasets—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