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 4701–4750 of 8378 papers

TitleStatusHype
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
Topology-preserving augmentation for CNN-based segmentation of congenital heart defects from 3D paediatric CMR—0
Topology-Preserving Scaling in Data Augmentation—0
Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift—0
Toward a Geometrical Understanding of Self-supervised Contrastive Learning—0
Toward Efficient Generalization in 3D Human Pose Estimation via a Canonical Domain Approach—0
Toward Fault Detection in Industrial Welding Processes with Deep Learning and Data Augmentation—0
Toward Generative Data Augmentation for Traffic Classification—0
Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples—0
Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing—0
Towards Accurate and Robust Classification in Continuously Transitioning Industrial Sprays with Mixup—0
Towards Adaptable and Interactive Image Captioning with Data Augmentation and Episodic Memory—0
Towards A Device-Independent Deep Learning Approach for the Automated Segmentation of Sonographic Fetal Brain Structures: A Multi-Center and Multi-Device Validation—0
Towards a Robust WiFi-based Fall Detection with Adversarial Data Augmentation—0
Towards artificially intelligent recycling Improving image processing for waste classification—0
Towards Automated Testing and Robustification by Semantic Adversarial Data Generation—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