Machine learning, in numpy
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Updated
Dec 5, 2022 - Python
Machine learning, in numpy
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
DeepNude's algorithm and general image generation theory and practice research, including pix2pix, CycleGAN, UGATIT, DCGAN, SinGAN, ALAE, mGANprior, StarGAN-v2 and VAE models (TensorFlow2 implementation). DeepNude的算法以及通用生成对抗网络(GAN,Generative Adversarial Network)图像生成的理论与实践研究。
A Collection of Variational Autoencoders (VAE) in PyTorch.
Collection of generative models in Tensorflow
Advanced Deep Learning with Keras, published by Packt
Diffusion model papers, survey, and taxonomy
Unifying Generative Autoencoder implementations in Python
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
Experiments for understanding disentanglement in VAE latent representations
A collection of generative methods implemented with TensorFlow (Deep Convolutional Generative Adversarial Networks (DCGAN), Variational Autoencoder (VAE) and DRAW: A Recurrent Neural Network For Image Generation).
Vector Quantized VAEs - PyTorch Implementation
PyTorch Re-Implementation of "Generating Sentences from a Continuous Space" by Bowman et al 2015 https://arxiv.org/abs/1511.06349
This is the repository of our article published in RecSys 2020 "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" and of several follow-up studies.
Tensorflow implementation of variational auto-encoder for MNIST
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations
Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation
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