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 2651–2700 of 8378 papers

TitleStatusHype
A Computer Vision Application for Assessing Facial Acne Severity from Selfie Images—0
CAAP: Class-Dependent Automatic Data Augmentation Based On Adaptive Policies For Time Series—0
1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization for the ICPHM 2023 Data Challenge—0
Enhancing Traffic Sign Recognition On The Performance Based On Yolov8—0
C3-SemiSeg: Contrastive Semi-Supervised Segmentation via Cross-Set Learning and Dynamic Class-Balancing—0
APAC: Augmented PAttern Classification with Neural Networks—0
Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training—0
Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models—0
AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images—0
Adversarial Examples Generation for Reducing Implicit Gender Bias in Pre-trained Models—0
BUT Opensat 2019 Speech Recognition System—0
Anything in Any Scene: Photorealistic Video Object Insertion—0
Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation—0
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN—0
Enhancing Traffic Sign Recognition with Tailored Data Augmentation: Addressing Class Imbalance and Instance Scarcity—0
Bulk Production Augmentation Towards Explainable Melanoma Diagnosis—0
Built-in Elastic Transformations for Improved Robustness—0
ANVITA Machine Translation System for WAT 2021 MultiIndicMT Shared Task—0
Building robust prediction models for defective sensor data using Artificial Neural Networks—0
Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation—0
An Unsupervised Domain Adaptive Approach for Multimodal 2D Object Detection in Adverse Weather Conditions—0
A Compromise Principle in Deep Monocular Depth Estimation—0
Building Korean Sign Language Augmentation (KoSLA) Corpus with Data Augmentation Technique—0
Building Goal-oriented Document-grounded Dialogue Systems—0
Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection—0
An Unsupervised Domain Adaptation Method for Locating Manipulated Region in partially fake Audio—0
A Comprehensive Survey on Data Augmentation—0
Enhancing Spoofing Speech Detection Using Rhythm Information—0
Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection—0
An Unpaired Cross-modality Segmentation Framework Using Data Augmentation and Hybrid Convolutional Networks for Segmenting Vestibular Schwannoma and Cochlea—0
Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization—0
Building A Proof-Oriented Programmer That Is 64% Better Than GPT-4o Under Data Scarcity—0
Building a Noisy Audio Dataset to Evaluate Machine Learning Approaches for Automatic Speech Recognition Systems—0
An Ultra-Fast Method for Simulation of Realistic Ultrasound Images—0
CoCoSoDa: Effective Contrastive Learning for Code Search—0
Don't overfit the history -- Recursive time series data augmentation—0
Building a Functional Machine Translation Corpus for Kpelle—0
Anti-Inpainting: A Proactive Defense against Malicious Diffusion-based Inpainters under Unknown Conditions—0
Dominant Shuffle: A Simple Yet Powerful Data Augmentation for Time-series Prediction—0
Domain Transfer based Data Augmentation for Neural Query Translation—0
Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud—0
Adversarial Diversity and Hard Positive Generation—0
Domain specificity and data efficiency in typo tolerant spell checkers: the case of search in online marketplaces—0
Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture—0
Domain Specific Fine-tuning of Denoising Sequence-to-Sequence Models for Natural Language Summarization—0
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN—0
Domain-specific augmentations with resolution agnostic self-attention mechanism improves choroid segmentation in optical coherence tomography images—0
Anti-Confusing: Region-Aware Network for Human Pose Estimation—0
Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario—0
Generative Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges—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