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 2701–2750 of 8378 papers

TitleStatusHype
Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection—0
Alleviating Mode Collapse in GAN via Diversity Penalty Module—0
Building a Functional Machine Translation Corpus for Kpelle—0
DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images—0
Draft, Command, and Edit: Controllable Text Editing in E-Commerce—0
Draft, Command, and Edit: Controllable Text Editing in E-Commerce—0
Anti-Inpainting: A Proactive Defense against Malicious Diffusion-based Inpainters under Unknown Conditions—0
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error—0
DRDr: Automatic Masking of Exudates and Microaneurysms Caused By Diabetic Retinopathy Using Mask R-CNN and Transfer Learning—0
DreamActor-H1: High-Fidelity Human-Product Demonstration Video Generation via Motion-designed Diffusion Transformers—0
Dominant Shuffle: A Simple Yet Powerful Data Augmentation for Time-series Prediction—0
Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction—0
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation—0
DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images—0
Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models—0
Driving through the Lens: Improving Generalization of Learning-based Steering using Simulated Adversarial Examples—0
Improving Small Language Models on PubMedQA via Generative Data Augmentation—0
DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation—0
Domain Transfer based Data Augmentation for Neural Query Translation—0
Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud—0
DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks—0
The Effects of Mixed Sample Data Augmentation are Class Dependent—0
Adversarial Diversity and Hard Positive Generation—0
Dropout Training for Support Vector Machines—0
Dropout Training for SVMs with Data Augmentation—0
Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations—0
Domain specificity and data efficiency in typo tolerant spell checkers: the case of search in online marketplaces—0
Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture—0
Domain Specific Fine-tuning of Denoising Sequence-to-Sequence Models for Natural Language Summarization—0
DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization—0
Domain-specific augmentations with resolution agnostic self-attention mechanism improves choroid segmentation in optical coherence tomography images—0
C3-SemiSeg: Contrastive Semi-Supervised Segmentation via Cross-Set Learning and Dynamic Class-Balancing—0
Anti-Confusing: Region-Aware Network for Human Pose Estimation—0
Generative Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges—0
Equivariant Neural Tangent Kernels—0
CAAP: Class-Dependent Automatic Data Augmentation Based On Adaptive Policies For Time Series—0
eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers—0
Dual Attention Driven Lumbar Magnetic Resonance Image Feature Enhancement and Automatic Diagnosis of Herniation—0
Evaluating Convolutional Neural Networks for COVID-19 classification in chest X-ray images—0
Evaluating the Utility of Model Configurations and Data Augmentation on Clinical Semantic Textual Similarity—0
Domain Similarity-Perceived Label Assignment for Domain Generalized Underwater Object Detection—0
Domain-guided data augmentation for deep learning on medical imaging—0
BTDNet: a Multi-Modal Approach for Brain Tumor Radiogenomic Classification—0
Domain Generalized Recaptured Screen Image Identification Using SWIN Transformer—0
Anticipating the Unseen Discrepancy for Vision and Language Navigation—0
Dual-Granularity Contrastive Learning for Session-based Recommendation—0
EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost—0
Dual-Level Boost Network for Long-Tail Prohibited Items Detection in X-ray Security Inspection—0
Domain Generalization via Frequency-domain-based Feature Disentanglement and Interaction—0
Domain Generalization via Balancing Training Difficulty and Model Capability—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