latent-space

Latent web browser

Autoencoders in Keras

Official implementation of the MM'21 paper "Constrained Graphic Layout Generation via Latent Optimization" (LayoutGAN++, CLG-LO, and Layout evaluation)

Buckle up, adventure in the styleGAN2-ada-pytorch network latent space awaits

Code accompanying ISMIR'19 paper titled "Learning to Traverse Latent Spaces for Musical Score Inpaintning"

Learning and controlling the source-filter representation of speech with a variational autoencoder

CVPR 2021, Smoothing the Disentangled Latent Style Space for Unsupervised I2I Translation

Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"

A deep learning architecture for robust inference and accurate prediction of cellular dynamics

Interacting with Latent Space of AutoEncoder

Controllable Face Generation via pretrained Conditional Adversarial Latent Autoencoder (ALAE)

Code implementation of the detection network capable of dealing with many overlapping spline bodies.

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'.

Code for the paper "C3VQG: Category Consistent Cyclic Visual Question Generation".

GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face Synthesis

AI that generates human faces which have never been seen before. The future is now 😁

Resources for the paper titled "Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts". Accepted at NeurIPS 2022.

LE-PDE accelerates PDEs' forward simulation and inverse optimization via latent global evolution, achieving significant speedup with SOTA accuracy

Keras implementation of Variation Autoencoder for face generation. Analysis of the distribution of the latent space of the VAE. Vector arithemtic in the latent space. Morphing between the faces. The model was trained on CelebA dataset

Count{down, up} with MNIST using Latent Interpolation