SOTAVerified

Image Augmentation

Image Augmentation is a data augmentation method that generates more training data from the existing training samples. Image Augmentation is especially useful in domains where training data is limited or expensive to obtain like in biomedical applications.

Source: Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairing

( Image credit: Kornia )

Papers

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Showing 176–200 of 308 papers

TitleStatusHype
Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions—0
Yelp Food Identification via Image Feature Extraction and Classification—0
Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation—0
Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys—0
3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video—0
A CNN toolbox for skin cancer classification—0
A Comprehensive Survey of Image Augmentation Techniques for Deep Learning—0
A convolutional neural network of low complexity for tumor anomaly detection—0
A Data-Driven Approach to Improve 3D Head-Pose Estimation—0
Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions—0
A framework for river connectivity classification using temporal image processing and attention based neural networks—0
A Methodology to Identify Cognition Gaps in Visual Recognition Applications Based on Convolutional Neural Networks—0
Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics—0
A novel action recognition system for smart monitoring of elderly people using Action Pattern Image and Series CNN with transfer learning—0
A Novel Breast Ultrasound Image Augmentation Method Using Advanced Neural Style Transfer: An Efficient and Explainable Approach—0
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning—0
A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening—0
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch—0
A Tale of Color Variants: Representation and Self-Supervised Learning in Fashion E-Commerce—0
A Technical Report for ICCV 2021 VIPriors Re-identification Challenge—0
A Technical Report for VIPriors Image Classification Challenge—0
Attention-Driven Lightweight Model for Pigmented Skin Lesion Detection—0
Attention W-Net: Improved Skip Connections for better Representations—0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified