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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5801–5850 of 8378 papers

TitleStatusHype
AugLoss: A Robust Augmentation-based Fine Tuning Methodology—0
Augment-and-Conquer Negative Binomial Processes—0
Augmentation-Based Deep Learning for Identification of Circulating Tumor Cells—0
Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification—0
Augmentation-induced Consistency Regularization for Classification—0
Augmentation Inside the Network—0
Augmentation Invariant Manifold Learning—0
Augmentation Learning for Semi-Supervised Classification—0
Augmentation of Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models—0
Augmentation of EEG and ECG Time Series for Deep Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates—0
Augmentation Policy Generation for Image Classification Using Large Language Models—0
Augmentation Scheme for Dealing with Imbalanced Network Traffic Classification Using Deep Learning—0
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices—0
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset—0
Augmentation through Laundering Attacks for Audio Spoof Detection—0
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation—0
Augment Before Copy-Paste: Data and Memory Efficiency-Oriented Instance Segmentation Framework for Sport-scenes—0
Augmented Bio-SBERT: Improving Performance for Pairwise Sentence Tasks in Bio-medical Domain—0
Augmented Cyclic Consistency Regularization for Unpaired Image-to-Image Translation—0
Augmented Data as an Auxiliary Plug-in Towards Categorization of Crowdsourced Heritage Data—0
Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design—0
Augmented Parallel-Pyramid Net for Attention Guided Pose-Estimation—0
Learning to Augment: Hallucinating Data for Domain Generalized Segmentation—0
Augmenting Character Designers Creativity Using Generative Adversarial Networks—0
Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context—0
Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation—0
Augmenting Image Question Answering Dataset by Exploiting Image Captions—0
Augmenting Imitation Experience via Equivariant Representations—0
Augmenting learning using symmetry in a biologically-inspired domain—0
Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis—0
Augmenting NLP data to counter Annotation Artifacts for NLI Tasks—0
Augmenting NLP models using Latent Feature Interpolations—0
Augmenting Radio Signals with Wavelet Transform for Deep Learning-Based Modulation Recognition—0
Augmenting Small Data to Classify Contextualized Dialogue Acts for Exploratory Visualization—0
Augmenting transferred representations for stock classification—0
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix—0
Augment on Manifold: Mixup Regularization with UMAP—0
AugmentTRAJ: A framework for point-based trajectory data augmentation—0
AugMixCloak: A Defense against Membership Inference Attacks via Image Transformation—0
AUGVIC: Exploiting BiText Vicinity for Low-Resource NMT—0
A U-Net Based Discriminator for Generative Adversarial Networks—0
The Causal Structure of Domain Invariant Supervised Representation Learning—0
A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection—0
A Unified Framework for Generative Data Augmentation: A Comprehensive Survey—0
A Unified Gradient Regularization Family for Adversarial Examples—0
A Unified Replay-based Continuous Learning Framework for Spatio-Temporal Prediction on Streaming Data—0
A Unified Transformer-based Framework for Duplex Text Normalization—0
A Unified Understanding of Adversarial Vulnerability Regarding Unimodal Models and Vision-Language Pre-training Models—0
AutoCoG: A Unified Data-Modal Co-Search Framework for Graph Neural Networks—0
AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching—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