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 2451–2500 of 8378 papers

TitleStatusHype
Choreographing the Digital Canvas: A Machine Learning Approach to Artistic Performance—0
AEIOU: A Unified Defense Framework against NSFW Prompts in Text-to-Image Models—0
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning—0
Chinese Grammatical Error Correction Based on Hybrid Models with Data Augmentation—0
ADA: A Game-Theoretic Perspective on Data Augmentation for Object Detection—0
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique—0
AEGR: A simple approach to gradient reversal in autoencoders for network anomaly detection—0
Efficient Out-of-Distribution Detection via CVAE data Generation—0
Efficient Prototype Consistency Learning in Medical Image Segmentation via Joint Uncertainty and Data Augmentation—0
Efficient Semi-supervised Consistency Training for Natural Language Understanding—0
EFSG: Evolutionary Fooling Sentences Generator—0
Check-worthy Claim Detection across Topics for Automated Fact-checking—0
Checks and Strategies for Enabling Code-Switched Machine Translation—0
Are you wearing a mask? Improving mask detection from speech using augmentation by cycle-consistent GANs—0
ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?—0
ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation—0
Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection—0
Classification of White Blood Cells Using Machine and Deep Learning Models: A Systematic Review—0
A Review on Generative Adversarial Networks for Data Augmentation in Person Re-Identification Systems—0
Chatbot Interaction with Artificial Intelligence: Human Data Augmentation with T5 and Language Transformer Ensemble for Text Classification—0
Chat-based Person Retrieval via Dialogue-Refined Cross-Modal Alignment—0
Efficiently Trained Low-Resource Mongolian Text-to-Speech System Based On FullConv-TTS—0
A Review of Image Retrieval Techniques: Data Augmentation and Adversarial Learning Approaches—0
Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation—0
Characters Detection on Namecard with faster RCNN—0
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification—0
Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach—0
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving—0
Character-level Chinese Writer Identification using Path Signature Feature, DropStroke and Deep CNN—0
AAVAE: Augmentation-Augmented Variational Autoencoders—0
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement—0
Character-Based Models for Adversarial Phone Extraction: Preventing Human Sex Trafficking—0
AdvKT: An Adversarial Multi-Step Training Framework for Knowledge Tracing—0
Efficient, Lexicon-Free OCR using Deep Learning—0
ChapGTP, ILLC's Attempt at Raising a BabyLM: Improving Data Efficiency by Automatic Task Formation—0
Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces—0
Efficient data augmentation using graph imputation neural networks—0
Channel Modeling Aided Dataset Generation for AI-Enabled CSI Feedback: Advances, Challenges, and Solutions—0
ChannelAugment: Improving generalization of multi-channel ASR by training with input channel randomization—0
Are NeRFs ready for autonomous driving? Towards closing the real-to-simulation gap—0
Efficient Deep Learning Approaches for Processing Ultra-Widefield Retinal Imaging—0
A Relational Model for One-Shot Classification—0
Efficient Classification of Histopathology Images—0
Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism—0
Active Generation Network of Human Skeleton for Action Recognition—0
Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation—0
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images—0
Challenges and Limitations in the Synthetic Generation of mHealth Sensor Data—0
Chain-of-Thought Prompt Distillation for Multimodal Named Entity Recognition and Multimodal Relation Extraction—0
Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion—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