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:

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Papers

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Showing 5351–5400 of 8378 papers

TitleStatusHype
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset—0
Simple Semantic-based Data Augmentation for Named Entity Recognition in Biomedical Texts—0
DE-ABUSE@TamilNLP-ACL 2022: Transliteration as Data Augmentation for Abuse Detection in Tamil—0
De-Bias for Generative Extraction in Unified NER Task—0
The use of Data Augmentation as a technique for improving neural network accuracy in detecting fake news about COVID-19—0
Measuring and Mitigating Name Biases in Neural Machine Translation—0
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—0
Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue—0
Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem SolversCode0
COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19 Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex Ultrasound Augmentation LearningCode0
Human-in-the-loop online multi-agent approach to increase trustworthiness in ML models through trust scores and data augmentation—0
Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI—0
Improving robustness of language models from a geometry-aware perspective—0
A DOMAIN TRANSFER BASED DATA AUGMENTATION METHOD FOR AUTOMATED RESPIRATORY CLASSIFICATION—0
Deeper Insights into the Robustness of ViTs towards Common Corruptions—0
Disambiguation of morpho-syntactic features of African American English -- the case of habitual be—0
Label Anchored Contrastive Learning for Language Understanding—0
Reprint: a randomized extrapolation based on principal components for data augmentationCode0
VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization—0
Universum-inspired Supervised Contrastive LearningCode0
The 2021 NIST Speaker Recognition Evaluation—0
TEAM-Atreides at SemEval-2022 Task 11: On leveraging data augmentation and ensemble to recognize complex Named Entities in Bangla—0
SoftEdge: Regularizing Graph Classification with Random Soft Edges—0
The NIST CTS Speaker Recognition Challenge—0
SelfD: Self-Learning Large-Scale Driving Policies From the Web—0
Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation—0
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation—0
A Mobile Food Recognition System for Dietary Assessment—0
G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-TrainingCode0
Image Data Augmentation for Deep Learning: A Survey—0
Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies—0
Augmentation of Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models—0
Adaptive Noisy Data Augmentation for Regularized Estimation and Inference in Generalized Linear Models—0
Gated Multimodal Fusion with Contrastive Learning for Turn-taking Prediction in Human-robot Dialogue—0
Extracting Targeted Training Data from ASR Models, and How to Mitigate It—0
Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology—0
UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language—0
AFSC: Adaptive Fourier Space Compression for Anomaly Detection—0
STRATA: Word Boundaries & Phoneme Recognition From Continuous Urdu Speech using Transfer Learning, Attention, & Data Augmentation—0
A Robust and Scalable Attention Guided Deep Learning Framework for Movement Quality Assessment—0
UFRC: A Unified Framework for Reliable COVID-19 Detection on Crowdsourced Cough Audio—0
DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling—0
CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge Distillation—0
OmniPD: One-Step Person Detection in Top-View Omnidirectional Indoor Scenes—0
Robotic and Generative Adversarial Attacks in Offline Writer-independent Signature Verification—0
Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task—0
Call-sign recognition and understanding for noisy air-traffic transcripts using surveillance information—0
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma—0
Impossible Triangle: What's Next for Pre-trained Language Models?—0
Overlapping Word Removal is All You Need: Revisiting Data Imbalance in Hope Speech Detection—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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified