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 291–300 of 308 papers

TitleStatusHype
Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization—0
INK: Inheritable Natural Backdoor Attack Against Model Distillation—0
Learning More with Less: GAN-based Medical Image Augmentation—0
Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge—0
Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints—0
Leveraging Habitat Information for Fine-grained Bird Identification—0
LMSeg: Language-guided Multi-dataset Segmentation—0
Medical Image Generation using Generative Adversarial Networks—0
MiAMix: Enhancing Image Classification through a Multi-stage Augmented Mixed Sample Data Augmentation Method—0
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pre-training—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified