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.

Further readings:

( Image credit: Albumentations )

Papers

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Showing 4001–4050 of 8378 papers

TitleStatusHype
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges—0
Aggrotech: Leveraging Deep Learning for Sustainable Tomato Disease Management—0
Image compositing is all you need for data augmentation—0
Image Data Augmentation for Deep Learning: A Survey—0
Image Data Augmentation for the TAIGA-IACT Experiment with Conditional Generative Adversarial Networks—0
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations—0
Adaptive Few-Shot Learning (AFSL): Tackling Data Scarcity with Stability, Robustness, and Versatility—0
Context-Aware Data Augmentation for LIDAR 3D Object Detection—0
FKIMNet: A Finger Dorsal Image Matching Network Comparing Component (Major, Minor and Nail) Matching with Holistic (Finger Dorsal) Matching—0
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning—0
Context-Aware Attention-Based Data Augmentation for POI Recommendation—0
Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition—0
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4—0
Image to Pseudo-Episode: Boosting Few-Shot Segmentation by Unlabeled Data—0
Fish-TViT: A novel fish species classification method in multi water areas based on transfer learning and vision transformer—0
Fish Detection Using Deep Learning—0
First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI—0
Imbalance-Aware Culvert-Sewer Defect Segmentation Using an Enhanced Feature Pyramid Network—0
First Place Solution to the ECCV 2024 ROAD++ Challenge @ ROAD++ Spatiotemporal Agent Detection 2024—0
Imbalanced Sentiment Classification Enhanced with Discourse Marker—0
Content-Conditioned Generation of Stylized Free hand Sketches—0
First Place Solution to the ECCV 2024 ROAD++ Challenge @ ROAD++ Atomic Activity Recognition 2024—0
First Order Ambisonics Domain Spatial Augmentation for DNN-based Direction of Arrival Estimation—0
Imitation Learning for End to End Vehicle Longitudinal Control with Forward Camera—0
FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment—0
CONTEMPLATING REAL-WORLDOBJECT RECOGNITION—0
Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling—0
Impact of Aliasing on Generalization in Deep Convolutional Networks—0
Adaptive Feature Selection for End-to-End Speech Translation—0
Impact of Dataset on Acoustic Models for Automatic Speech Recognition—0
Academic Case Reports Lack Diversity: Assessing the Presence and Diversity of Sociodemographic and Behavioral Factors related to Post COVID-19 Condition—0
Fingerprint Feature Extraction by Combining Texture, Minutiae, and Frequency Spectrum Using Multi-Task CNN—0
Real-Time Helmet Violation Detection in AI City Challenge 2023 with Genetic Algorithm-Enhanced YOLOv5—0
Impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning—0
Consistent Text Categorization using Data Augmentation in e-Commerce—0
Implanting Synthetic Lesions for Improving Liver Lesion Segmentation in CT Exams—0
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks—0
Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning—0
Fine-tuning of Convolutional Neural Networks for the Recognition of Facial Expressions in Sign Language Video Samples—0
Finetuning Is a Surprisingly Effective Domain Adaptation Baseline in Handwriting Recognition—0
A supervised generative optimization approach for tabular data—0
Age Range Estimation using MTCNN and VGG-Face Model—0
Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis—0
Fine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset—0
Importance of Data Loading Pipeline in Training Deep Neural Networks—0
Consistency and Monotonicity Regularization for Neural Knowledge Tracing—0
Fine-Grained Few Shot Learning with Foreground Object Transformation—0
Consensus Clustering With Unsupervised Representation Learning—0
Fine-grained building roof instance segmentation based on domain adapted pretraining and composite dual-backbone—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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified