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 3001–3050 of 8378 papers

TitleStatusHype
Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of Generative AI and State-of-the-Art Neural Networks—0
Enhancing Romanian Offensive Language Detection through Knowledge Distillation, Multi-Task Learning, and Data Augmentation—0
CoCoSoDa: Effective Contrastive Learning for Code Search—0
Checks and Strategies for Enabling Code-Switched Machine Translation—0
Does Data Augmentation Lead to Positive Margin?—0
Enhancing Spoofing Speech Detection Using Rhythm Information—0
Enhancing Synthetic Training Data for Speech Commands: From ASR-Based Filtering to Domain Adaptation in SSL Latent Space—0
Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation—0
A novel method to enhance pneumonia detection via a model-level ensembling of CNN and vision transformer—0
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN—0
A Comprehensive Study on NLP Data Augmentation for Hate Speech Detection: Legacy Methods, BERT, and LLMs—0
Does Data Augmentation Benefit from Split BatchNorms—0
Enhancing Traffic Sign Recognition On The Performance Based On Yolov8—0
Enhancing Traffic Sign Recognition with Tailored Data Augmentation: Addressing Class Imbalance and Instance Scarcity—0
Enhancing Virtual Try-On with Synthetic Pairs and Error-Aware Noise Scheduling—0
Choreographing the Digital Canvas: A Machine Learning Approach to Artistic Performance—0
Enhancing weed detection performance by means of GenAI-based image augmentation—0
Invariance Principle Meets Vicinal Risk Minimization—0
Breast mass detection in digital mammography based on anchor-free architecture—0
Arithmetic Reasoning with LLM: Prolog Generation & Permutation—0
CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge Distillation—0
GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation—0
Adversarial Data Augmentation for Disordered Speech Recognition—0
Enrich the content of the image Using Context-Aware Copy Paste—0
Ensemble of ACCDOA- and EINV2-based Systems with D3Nets and Impulse Response Simulation for Sound Event Localization and Detection—0
Ensemble of pre-trained language models and data augmentation for hate speech detection from Arabic tweets—0
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning—0
Ensemble Self-Training for Low-Resource Languages: Grapheme-to-Phoneme Conversion and Morphological Inflection—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
Do CNNs Encode Data Augmentations?—0
Breast Cancer Diagnosis with Transfer Learning and Global Pooling—0
Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures—0
EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching—0
DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition—0
EntProp: High Entropy Propagation for Improving Accuracy and Robustness—0
A Novel Method for Accurate & Real-time Food Classification: The Synergistic Integration of EfficientNetB7, CBAM, Transfer Learning, and Data Augmentation—0
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation—0
DMix: Distance Constrained Interpolative Mixup—0
Environment Transfer for Distributed Systems—0
DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation—0
EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost—0
DM-CT: Consistency Training with Data and Model Perturbation—0
Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation—0
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency—0
EPYNET: Efficient Pyramidal Network for Clothing Segmentation—0
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces—0
A Novel Framework for Assessment of Learning-based Detectors in Realistic Conditions with Application to Deepfake Detection—0
DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness—0
DJMix: Unsupervised Task-agnostic Augmentation for Improving Robustness—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