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 5751–5800 of 8378 papers

TitleStatusHype
A Survey on Face Data Augmentation—0
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch—0
A Survey on Neural Architecture Search—0
A Survey on SAR ship classification using Deep Learning—0
A Survey on Semantics in Automated Data Science—0
Asymmetric Clean Segments-Guided Self-Supervised Learning for Robust Speaker Verification—0
Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection—0
Asymptotically exact data augmentation: models, properties and algorithms—0
A Syntax-Guided Grammatical Error Correction Model with Dependency Tree Correction—0
A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages—0
A Systematic Study on Quantifying Bias in GAN-Augmented Data—0
A tailored Handwritten-Text-Recognition System for Medieval Latin—0
Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis—0
A Target-Aware Analysis of Data Augmentation for Hate Speech Detection—0
A Theory of PAC Learnability under Transformation Invariances—0
Atherosclerotic carotid plaques on panoramic imaging: an automatic detection using deep learning with small dataset—0
A Three Step Training Approach with Data Augmentation for Morphological Inflection—0
A Time-Series Data Augmentation Model through Diffusion and Transformer Integration—0
ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories—0
A Transformer Based Pitch Sequence Autoencoder with MIDI Augmentation—0
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy—0
Attacking Voice Anonymization Systems with Augmented Feature and Speaker Identity Difference—0
Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints—0
Exploring Limits of Diffusion-Synthetic Training with Weakly Supervised Semantic Segmentation—0
Attention based on-device streaming speech recognition with large speech corpus—0
Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans—0
Attention, Filling in The Gaps for Generalization in Routing Problems—0
Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management—0
Attention-Guided Erasing: A Novel Augmentation Method for Enhancing Downstream Breast Density Classification—0
Attention Is All You Need For Blind Room Volume Estimation—0
AttentionMix: Data augmentation method that relies on BERT attention mechanism—0
Attention Speaks Volumes: Localizing and Mitigating Bias in Language Models—0
Attention-stacked Generative Adversarial Network (AS-GAN)-empowered Sensor Data Augmentation for Online Monitoring of Manufacturing System—0
Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification—0
Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition—0
A Twitter BERT Approach for Offensive Language Detection in Marathi—0
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU—0
AUC-mixup: Deep AUC Maximization with Mixup—0
Auctus: A Dataset Search Engine for Data Augmentation—0
Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs—0
Audio Defect Detection in Music with Deep Networks—0
Audio Denoising for Robust Audio Fingerprinting—0
AudioSpa: Spatializing Sound Events with Text—0
Audio-to-Audio Emotion Conversion With Pitch And Duration Style Transfer—0
Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions—0
Auditory-Based Data Augmentation for End-to-End Automatic Speech Recognition—0
Aug2Search: Enhancing Facebook Marketplace Search with LLM-Generated Synthetic Data Augmentation—0
Towards Practical Few-shot Federated NLP—0
AugGAN: Cross Domain Adaptation with GAN-based Data Augmentation—0
AugInsert: Learning Robust Visual-Force Policies via Data Augmentation for Object Assembly Tasks—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