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 2951–3000 of 8378 papers

TitleStatusHype
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation—0
Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery—0
Chain-of-Thought Prompt Distillation for Multimodal Named Entity Recognition and Multimodal Relation Extraction—0
Enhancing DeepLabV3+ to Fuse Aerial and Satellite Images for Semantic Segmentation—0
Does Incomplete Syntax Influence Korean Language Model? Focusing on Word Order and Case Markers—0
Enhancing DR Classification with Swin Transformer and Shifted Window Attention—0
Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models—0
Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation—0
Does equivariance matter at scale?—0
BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modeling—0
Enhancing Eye Disease Diagnosis with Deep Learning and Synthetic Data Augmentation—0
Enhancing Facial Data Diversity with Style-based Face Aging—0
Learning Test-time Augmentation for Content-based Image Retrieval—0
Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Models—0
Does enhanced shape bias improve neural network robustness to common corruptions?—0
Enhancing Few-shot NER with Prompt Ordering based Data Augmentation—0
Does Data Augmentation Lead to Positive Margin?—0
Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control—0
Enhancing Graph Contrastive Learning with Node Similarity—0
Channel Modeling Aided Dataset Generation for AI-Enabled CSI Feedback: Advances, Challenges, and Solutions—0
Enhancing Image Classification in Small and Unbalanced Datasets through Synthetic Data Augmentation—0
Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments—0
Enhancing Knee Osteoarthritis severity level classification using diffusion augmented images—0
ChapGTP, ILLC's Attempt at Raising a BabyLM: Improving Data Efficiency by Automatic Task Formation—0
A novel method to enhance pneumonia detection via a model-level ensembling of CNN and vision transformer—0
A Comprehensive Study on NLP Data Augmentation for Hate Speech Detection: Legacy Methods, BERT, and LLMs—0
Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap—0
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement—0
Enhancing Mathematical Reasoning in LLMs with Background Operators—0
Enhancing Medical Image Analysis through Geometric and Photometric transformations—0
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving—0
Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation—0
Does Data Augmentation Benefit from Split BatchNorms—0
Breast mass detection in digital mammography based on anchor-free architecture—0
Enhancing Multi-field B2B Cloud Solution Matching via Contrastive Pre-training—0
Enhancing NER Performance in Low-Resource Pakistani Languages using Cross-Lingual Data Augmentation—0
Enhancing Neural Theorem Proving through Data Augmentation and Dynamic Sampling Method—0
Enhancing Nighttime Vehicle Detection with Day-to-Night Style Transfer and Labeling-Free Augmentation—0
Enhancing object detection robustness: A synthetic and natural perturbation approach—0
Enhancing Out-Of-Domain Utterance Detection with Data Augmentation Based on Word Embeddings—0
Adversarial Data Augmentation for Disordered Speech Recognition—0
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning—0
Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directions—0
Chatbot Interaction with Artificial Intelligence: Human Data Augmentation with T5 and Language Transformer Ensemble for Text Classification—0
Enhancing PTSD Outcome Prediction with Ensemble Models in Disaster Contexts—0
Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs—0
Enhancing Robustness in Aspect-based Sentiment Analysis by Better Exploiting Data Augmentation—0
Classification of White Blood Cells Using Machine and Deep Learning Models: A Systematic Review—0
A novel method for data augmentation: Nine Dot Moving Least Square (ND-MLS)—0
Document Image Layout Analysis via Explicit Edge Embedding Network—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