stl-10

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sjenni / LearningToSpotArtifacts

Self-Supervised Feature Learning by Learning to Spot Artifacts. In CVPR, 2018.

machine-learning deep-learning tensorflow classification alexnet unsupervised-learning feature-learning compter-vision self-supervised-learning stl-10

Updated Mar 24, 2023
Python

wgcban / mix-bt

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Official PyTorch Implementation of Guarding Barlow Twins Against Overfitting with Mixed Samples

imagenet representation-learning knn-classification cifar-10 linear-probing mixup cifar-100 self-supervised-learning stl-10 byol tiny-imagenet simclr contrastive-learning barlow-twins

Updated Jan 19, 2024
Python

boyla950 / conditional-wgan-gp

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Coursework solutions for a 3rd year Computer Science module on Deep Learning @ Durham University. Uses a conditional WGAN-GP implementation to generate images from the CIFAR-10 and STL-10 datasets.

deep-learning neural-networks image-generation wgan-gp cifar-10 stl-10 ac-gan

Updated Oct 13, 2022
Python

shadowbourne / pegasus-lightweight-gan

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3rd Year: 1st - 104/100. Generative Modelling: Applying GANs to generate out-of-sample inter-class images - "The Mythical Pegasus: A Mysterious Journey".

pytorch gan pegasus data-augmentation cifar-10 stl-10 generative-modelling lightweight-gan inter-class-generative-modelling dual-discriminator-gan