There are 0 repository under latent-representations topic.
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
Implementation of "Disentangled Representation Learning for Non-Parallel Text Style Transfer(ACL 2019)" in Pytorch
Graph Representation Analysis for Connected Embeddings
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 our paper -- Hyperprior Induced Unsupervised Disentanglement of Latent Representations (AAAI 2019)
ICCV23 "Householder Projector for Unsupervised Latent Semantics Discovery"
ACM CHIL 2020: "Survival Cluster Analysis"
Code associated with the paper "Prior Image-Constrained Reconstruction using Style-Based Generative Models" accepted to ICML 2021.
Official repository for the "Multiple wavefield solutions in physics-informed neural networks using latent representation" paper.
Simple Pytorch Implementation of BYOL: Bootstrap Your Own Latent(https://arxiv.org/abs/2006.07733) [Colab Version Available]
A study on the effect of normalization in predictions by CNN models
Latent-Explorer is the Python implementation of the framework proposed in the paper "Unveiling LLMs: The Evolution of Latent Representations in a Dynamic Knowledge Graph".
Working towards deliverable 5.3
TensorFlow code and LaTex for Bachelor Thesis: Understanding Variational Autoencoders' Latent Representations of Remote Sensing Images :earth_africa:
Investigate mapping of articulations from the image space to the latent space using neural networks.
TensorFlow Deep Feature Consistent VAE Implementation on the Kaggle Fashion Dataset
Latent Representation and Exploration of Images Using Variational AutoEncoders
This algorithm exploits the relationships between variables to improve the reconstruction performance of the variational autoencoder (VAE). A correlation score was used as the metric to group the features via a distance-based clustering method. The resulting clusters served as inputs for the Attention-Based VAE.
📜 [MIDL 2022] "Sensor to Image Heterogeneous Domain Adaptation Network", Ishikaa Lunawat, Vignesh S, S P Sharan