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 4301–4325 of 8378 papers

TitleStatusHype
Delexicalized Paraphrase Generation—0
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC—0
Denoising Diffusion Medical Models—0
Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers—0
Dense Contrastive Visual-Linguistic Pretraining—0
Dependent Relational Gamma Process Models for Longitudinal Networks—0
Deploying a BERT-based Query-Title Relevance Classifier in a Production System: a View from the Trenches—0
De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks—0
Depression detection in social media posts using transformer-based models and auxiliary features—0
Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation—0
Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis—0
DermGAN: Synthetic Generation of Clinical Skin Images with Pathology—0
Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in Latvian—0
Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditions—0
DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception—0
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection—0
Detecting cities in aerial night-time images by learning structural invariants using single reference augmentation—0
Unmasking Falsehoods in Reviews: An Exploration of NLP Techniques—0
Detecting ESG topics using domain-specific language models and data augmentation approaches—0
Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation—0
Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge—0
Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge—0
Detecting Prefix Bias in LLM-based Reward Models—0
Detection and Classification of Brain tumors Using Deep Convolutional Neural Networks—0
Detection of Active Emergency Vehicles using Per-Frame CNNs and Output Smoothing—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