Autoencoders in Keras
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Updated
Mar 6, 2018 - Python
Autoencoders in Keras
Count{down, up} with MNIST using Latent Interpolation
Witchcraft is a toolkit capable of encoding documents of various content types and structure (flat, hierarchical, or series) into searchable latent vector space.
A project for understanding latent spaces in different neural networks (joint work with interns 2018)
Visualize the Latent Space of an Autoencoder using matplotlib
Variational Autoencoders implementation in Keras.
Code accompanying ISMIR'19 paper titled "Learning to Traverse Latent Spaces for Musical Score Inpaintning"
Replication of the research paper titled Auto-Encoding Variational Bayes.
Toy example for a Conditional Variational Autoencoder in Keras. Regresses features from automatically generated images. Useful for learning about the concept.
Extension of GANspace: https://github.com/harskish/ganspace
AI that generates human faces which have never been seen before. The future is now 😁
Diverse Facial Edit with StyleGAN, StyleGAN2, StyleClip with ViT, and Other Features like Background Removal and Face Swap
TensorFlow implementation of a simple Variational Autoencoder. Includes a simple GUI for exploring the learned latent space.
Uniform Vectorized AutoEncoder : latent vectors distribution is attacked by adversarial model
This repository contains the code, data and scripts used to write the Bachelor Thesis "Latent representations for traditional music analysis and generation".
VecDB is a tool to help you to store, manage and compare embeddings vectors for machine learning applications.
Interacting with Latent Space of AutoEncoder
Pipeline Consisting of LSTM + Variational and Transformer Based Autoencoders + PCA/UMAP (Parameterized and Non-Parameterized) For Generating Low-Dim Manifold Representation of V1 Neural Activity
This repository contains the implementation of SimplEx, a method to explain the latent representations of black-box models with the help of a corpus of examples. For more details, please read our NeurIPS 2021 paper: 'Explaining Latent Representations with a Corpus of Examples'.
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