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 5451–5500 of 8378 papers

TitleStatusHype
The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies—0
The Role of the Input in Natural Language Video Description—0
The RoyalFlush System of Speech Recognition for M2MeT Challenge—0
The RWTH ASR System for TED-LIUM Release 2: Improving Hybrid HMM with SpecAugment—0
The Second Place Solution for ICCV2021 VIPriors Instance Segmentation Challenge—0
The Solution for the CVPR 2023 1st foundation model challenge-Track2—0
The SSL Interplay: Augmentations, Inductive Bias, and Generalization—0
The Tags Are Alright: Robust Large-Scale RFID Clone Detection Through Federated Data-Augmented Radio Fingerprinting—0
The Third Place Solution for CVPR2022 AVA Accessibility Vision and Autonomy Challenge—0
The THUEE System Description for the IARPA OpenASR21 Challenge—0
The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation—0
The University of Sydney's Machine Translation System for WMT19—0
The use of Data Augmentation as a technique for improving neural network accuracy in detecting fake news about COVID-19—0
The USTC-NELSLIP Systems for Simultaneous Speech Translation Task at IWSLT 2021—0
The Vicomtech Audio Deepfake Detection System based on Wav2Vec2 for the 2022 ADD Challenge—0
The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge—0
The Whole Pathological Slide Classification via Weakly Supervised Learning—0
The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 2022—0
The YiTrans End-to-End Speech Translation System for IWSLT 2022 Offline Shared Task—0
The YiTrans Speech Translation System for IWSLT 2022 Offline Shared Task—0
Thinking in Directivity: Speech Large Language Model for Multi-Talker Directional Speech Recognition—0
This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generation—0
Throwing Darts in the Dark? Detecting Bots with Limited Data using Neural Data Augmentation—0
Tibyan Corpus: Balanced and Comprehensive Error Coverage Corpus Using ChatGPT for Arabic Grammatical Error Correction—0
TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systems—0
Tightening Classification Boundaries in Open Set Domain Adaptation through Unknown Exploitation—0
TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation—0
Time-domain speech super-resolution with GAN based modeling for telephony speaker verification—0
TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts—0
TimeLDM: Latent Diffusion Model for Unconditional Time Series Generation—0
Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence—0
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning—0
Time Series Data Augmentation for Deep Learning: A Survey—0
Time Series Generative Learning with Application to Brain Imaging Analysis—0
Time Series Viewmakers for Robust Disruption Prediction—0
Time to Market Reduction for Hydrogen Fuel Cell Stacks using Generative Adversarial Networks—0
TinyClick: Single-Turn Agent for Empowering GUI Automation—0
TipSegNet: Fingertip Segmentation in Contactless Fingerprint Imaging—0
TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention Networks—0
T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation—0
TMU Japanese-English Multimodal Machine Translation System for WAT 2020—0
To augment or not to augment? Data augmentation in user identification based on motion sensors—0
TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue Modeling on Spoken Conversations—0
Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs—0
Tongji University Undergraduate Team for the VoxCeleb Speaker Recognition Challenge2020—0
TopoCL: Topological Contrastive Learning for Time Series—0
TopoLedgerBERT: Topological Learning of Ledger Description Embeddings using Siamese BERT-Networks—0
Topological Regularization for Graph Neural Networks Augmentation—0
Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs—0
Topology-Guided Multi-Class Cell Context Generation for Digital Pathology—0
Show:102550
← PrevPage 110 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