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 4001–4050 of 8378 papers

TitleStatusHype
Accelerating Molecular Graph Neural Networks via Knowledge Distillation—0
Few-Shot Continual Learning via Flat-to-Wide ApproachesCode0
Pseudo-Trilateral Adversarial Training for Domain Adaptive Traversability Prediction—0
Semi-supervised Object Detection: A Survey on Recent Research and Progress—0
Chain-of-Thought Prompt Distillation for Multimodal Named Entity Recognition and Multimodal Relation Extraction—0
A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial DiversityCode0
Weighted Automata Extraction and Explanation of Recurrent Neural Networks for Natural Language TasksCode0
Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentations—0
Unsupervised Deformable Ultrasound Image Registration and Its Application for Vessel Segmentation—0
AugDMC: Data Augmentation Guided Deep Multiple ClusteringCode0
Harnessing Data Augmentation to Quantify Uncertainty in the Early Estimation of Single-Photon Source QualityCode0
Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization—0
Concurrent ischemic lesion age estimation and segmentation of CT brain using a Transformer-based network—0
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection—0
A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis—0
Deep Learning of Dynamical System Parameters from Return Maps as ImagesCode0
MultiEarth 2023 Deforestation Challenge -- Team FOREVER—0
Recent Advances in Direct Speech-to-text Translation—0
Human Limits in Machine Learning: Prediction of Plant Phenotypes Using Soil Microbiome DataCode0
PAC-Chernoff Bounds: Understanding Generalization in the Interpolation Regime—0
Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Study—0
MTN: Forensic Analysis of MP4 Video Files Using Graph Neural NetworksCode0
Semi-supervised Relation Extraction via Data Augmentation and Consistency-training—0
SLACK: Stable Learning of Augmentations with Cold-start and KL regularization—0
Investigating Masking-based Data Generation in Language Models—0
Low-Resource Text-to-Speech Using Specific Data and Noise Augmentation—0
Cross-Modal Video to Body-joints Augmentation for Rehabilitation Exercise Quality Assessment—0
Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rankCode0
Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language ModelsCode0
Noise Stability Optimization for Finding Flat Minima: A Hessian-based Regularization ApproachCode0
Learning Cross-lingual Mappings for Data Augmentation to Improve Low-Resource Speech Recognition—0
SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection—0
A Simple Data Augmentation for Feature Distribution Skewed Federated Learning—0
VIBR: Learning View-Invariant Value Functions for Robust Visual Control—0
Improving Code-Switching and Named Entity Recognition in ASR with Speech Editing based Data Augmentation—0
Data Augmentation for Seizure Prediction with Generative Diffusion Model—0
Parametric Implicit Face Representation for Audio-Driven Facial Reenactment—0
Revisiting and Advancing Adversarial Training Through A Simple Baseline—0
Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization—0
Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative StudyCode0
Generated Graph DetectionCode0
Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis—0
Underwater Acoustic Target Recognition based on Smoothness-inducing Regularization and Spectrogram-based Data Augmentation—0
Gender-Inclusive Grammatical Error Correction through AugmentationCode0
AROID: Improving Adversarial Robustness Through Online Instance-Wise Data AugmentationCode0
Rotational augmentation techniques: a new perspective on ensemble learning for image classification—0
Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili—0
Graph Mixup with Soft Alignments—0
HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach—0
Medical Data Augmentation via ChatGPT: A Case Study on Medication Identification and Medication Event Classification—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