davidtellez

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@AiosynThe Netherlands

Pinned Repositories

adda_mnist64
Adversarial Discriminative Domain Adaptation with MNIST 64x64 in Lasagne-Theano
Language:Python32 4 115
began
Boundary Equibilibrium Generative Adversarial Networks (BEGAN) in Lasagne-Theano
Language:Python5 3 00
contrastive-predictive-coding
Keras implementation of Representation Learning with Contrastive Predictive Coding
Language:Python529 16 18120
contrastive-predictive-coding-images
Keras implementation of Representation Learning with Contrastive Predictive Coding for images
Language:Python34 3 38
davidtellez.github.io
Personal website
Language:SCSS1 2 00
dcgan_mnist64
Implementation of a Deep Convolutional Generative Adversarial Network to generate realistic MNIST digits at 64x64 resolution in Tensorflow.
Language:Jupyter Notebook0 2 00
neural-image-compression
Code accompanying the paper Neural Image Compression for Gigapixel Histopathology Image Analysis
Language:Python50 3 815
segmentation_models
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
Language:Python1 1 00

davidtellez/contrastive-predictive-coding
Keras implementation of Representation Learning with Contrastive Predictive Coding
Language:Python529 16 18120
davidtellez/neural-image-compression
Code accompanying the paper Neural Image Compression for Gigapixel Histopathology Image Analysis
Language:Python50 3 815
davidtellez/contrastive-predictive-coding-images
Keras implementation of Representation Learning with Contrastive Predictive Coding for images
Language:Python34 3 38
davidtellez/adda_mnist64
Adversarial Discriminative Domain Adaptation with MNIST 64x64 in Lasagne-Theano
Language:Python32 4 115
davidtellez/began
Boundary Equibilibrium Generative Adversarial Networks (BEGAN) in Lasagne-Theano
Language:Python5 3 00
davidtellez/davidtellez.github.io
Personal website
Language:SCSS1 2 00
davidtellez/segmentation_models
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
Language:Python1 1 00
davidtellez/dcgan_mnist64
Implementation of a Deep Convolutional Generative Adversarial Network to generate realistic MNIST digits at 64x64 resolution in Tensorflow.
Language:Jupyter Notebook0 2 00