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 2301–2350 of 8378 papers

TitleStatusHype
A Fourier Perspective on Model Robustness in Computer Vision—0
A Simple Feature Augmentation for Domain Generalization—0
Adapting Coreference Resolution for Processing Violent Death Narratives—0
Code Needs Comments: Enhancing Code LLMs with Comment Augmentation—0
A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image—0
DRDr: Automatic Masking of Exudates and Microaneurysms Caused By Diabetic Retinopathy Using Mask R-CNN and Transfer Learning—0
CodeFort: Robust Training for Code Generation Models—0
A Simple Data Augmentation for Feature Distribution Skewed Federated Learning—0
Code Execution with Pre-trained Language Models—0
Codec Data Augmentation for Time-domain Heart Sound Classification—0
A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection—0
DreamActor-H1: High-Fidelity Human-Product Demonstration Video Generation via Motion-designed Diffusion Transformers—0
DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images—0
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding—0
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking—0
COCO-Counterfactuals: Automatically Constructed Counterfactual Examples for Image-Text Pairs—0
Coarse-to-fine Task-driven Inpainting for Geoscience Images—0
A Flat Minima Perspective on Understanding Augmentations and Model Robustness—0
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite—0
CNNs Avoid Curse of Dimensionality by Learning on Patches—0
Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?—0
CNN-powered micro- to macro-scale flow modeling in deformable porous media—0
CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation—0
A Simple Background Augmentation Method for Object Detection with Diffusion Model—0
Draft, Command, and Edit: Controllable Text Editing in E-Commerce—0
CNN-BiLSTM model for English Handwriting Recognition: Comprehensive Evaluation on the IAM Dataset—0
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness—0
CNN-based approach for glaucoma diagnosis using transfer learning and LBP-based data augmentation—0
Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients—0
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning—0
A First Attempt at Unreliable News Detection in Swedish—0
Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images—0
CMMC-BDRC Solution to the NLP-TEA-2018 Chinese Grammatical Error Diagnosis Task—0
AS-ES Learning: Towards Efficient CoT Learning in Small Models—0
cMIM: A Contrastive Mutual Information Framework for Unified Generative and Discriminative Representation Learning—0
Clutter Detection and Removal in 3D Scenes with View-Consistent Inpainting—0
Adapting Abstract Meaning Representation Parsing to the Clinical Narrative -- the SPRING THYME parser—0
A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy—0
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error—0
Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models—0
Clustering and Data Augmentation to Improve Accuracy of Sleep Assessment and Sleep Individuality Analysis—0
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
Affinity and Diversity: Quantifying Mechanisms of Data Augmentation—0
ADAPT at SR’20: How Preprocessing and Data Augmentation Help to Improve Surface Realization—0
A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation—0
Do We Need to Differentiate Negative Candidates Before Training a Neural Ranker?—0
Clozer”:" Adaptable Data Augmentation for Cloze-style Reading Comprehension—0
Clozer: Adaptable Data Augmentation for Cloze-style Reading Comprehension—0
A Semantic Feature-Wise Transformation Relation Network for Automatic Short Answer Grading—0
Affine transformation estimation improves visual self-supervised learning—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