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 2076–2100 of 8378 papers

TitleStatusHype
Contrastive Fine-tuning Improves Robustness for Neural Rankers—0
Agriculture-Vision Challenge 2024 -- The Runner-Up Solution for Agricultural Pattern Recognition via Class Balancing and Model Ensemble—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
Developing the Reliable Shallow Supervised Learning for Thermal Comfort using ASHRAE RP-884 and ASHRAE Global Thermal Comfort Database II—0
Agriculture-Vision Challenge 2022 -- The Runner-Up Solution for Agricultural Pattern Recognition via Transformer-based Models—0
A Survey on Data Augmentation for Text Classification—0
Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations—0
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection—0
A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells—0
Continuous Control Reinforcement Learning: Distributed Distributional DrQ Algorithms—0
A Grey-box Text Attack Framework using Explainable AI—0
Detecting cities in aerial night-time images by learning structural invariants using single reference augmentation—0
Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks—0
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification—0
A survey of underwater acoustic data classification methods using deep learning for shoreline surveillance—0
Continual Unsupervised Domain Adaptation for Semantic Segmentation using a Class-Specific Transfer—0
A Survey of Uncertainty in Deep Neural Networks—0
Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation—0
A survey of synthetic data augmentation methods in computer vision—0
Contextual Scene Augmentation and Synthesis via GSACNet—0
A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data—0
A Graph Data Augmentation Strategy with Entropy Preservation—0
Academic Case Reports Lack Diversity: Assessing the Presence and Diversity of Sociodemographic and Behavioral Factors related to Post COVID-19 Condition—0
Unmasking Falsehoods in Reviews: An Exploration of NLP Techniques—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