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 3401–3450 of 8378 papers

TitleStatusHype
AlignMix: Improving representations by interpolating aligned features—0
Cross-Lingual Approaches to Reference Resolution in Dialogue Systems—0
Cross-language Sentence Selection via Data Augmentation and Rationale Training—0
AugLoss: A Robust Augmentation-based Fine Tuning Methodology—0
Image retrieval outperforms diffusion models on data augmentation—0
Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration—0
AugInsert: Learning Robust Visual-Force Policies via Data Augmentation for Object Assembly Tasks—0
CrossFuse: Learning Infrared and Visible Image Fusion by Cross-Sensor Top-K Vision Alignment and Beyond—0
Cross Encoding as Augmentation: Towards Effective Educational Text Classification—0
AugGAN: Cross Domain Adaptation with GAN-based Data Augmentation—0
Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback—0
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models—0
Cross-Domain Few-Shot Learning with Meta Fine-Tuning—0
Towards Practical Few-shot Federated NLP—0
A data augmentation methodology for training machine/deep learning gait recognition algorithms—0
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation—0
Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery—0
CrossCount: A Deep Learning System for Device-free Human Counting using WiFi—0
Cross-Corpus Data Augmentation for Acoustic Addressee Detection—0
Aug2Search: Enhancing Facebook Marketplace Search with LLM-Generated Synthetic Data Augmentation—0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
Cross-Corpora Spoken Language Identification with Domain Diversification and Generalization—0
Auditory-Based Data Augmentation for End-to-End Automatic Speech Recognition—0
CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals—0
Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions—0
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator—0
CRNNTL: convolutional recurrent neural network and transfer learning for QSAR modelling—0
Audio-to-Audio Emotion Conversion With Pitch And Duration Style Transfer—0
AudioSpa: Spatializing Sound Events with Text—0
A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation—0
Data augmentation by morphological mixup for solving Raven's Progressive Matrices—0
Accelerating Representation Learning with View-Consistent Dynamics in Data-Efficient Reinforcement Learning—0
3D Brain MRI Classification for Alzheimer Diagnosis Using CNN with Data Augmentation—0
Trainable Pointwise Decoder Module for Point Cloud Segmentation—0
Credit Risk Identification in Supply Chains Using Generative Adversarial Networks—0
Creation of Novel Soft Robot Designs using Generative AI—0
Creating Spoken Dialog Systems in Ultra-Low Resourced Settings—0
CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency—0
Audio Denoising for Robust Audio Fingerprinting—0
A Lightweight Multi Aspect Controlled Text Generation Solution For Large Language Models—0
CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis—0
Crash Data Augmentation Using Conditional Generative Adversarial Networks (CGAN) for Improving Safety Performance Functions—0
Audio Defect Detection in Music with Deep Networks—0
CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose—0
Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs—0
A Data Augmentation Method and the Embedding Mechanism for Detection and Classification of Pulmonary Nodules on Small Samples—0
CoViews: Adaptive Augmentation Using Cooperative Views for Enhanced Contrastive Learning—0
Auctus: A Dataset Search Engine for Data Augmentation—0
CoVid-19 Detection leveraging Vision Transformers and Explainable AI—0
AUC-mixup: Deep AUC Maximization with Mixup—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