Deep Learning Computer Vision Algorithms for Real-World Use
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Updated
Aug 19, 2022 - Python
Deep Learning Computer Vision Algorithms for Real-World Use
PyTorch Blog Post On Image Similarity Search
DocEnTr: An end-to-end document image enhancement transformer - ICPR 2022
Used the Functional API to built custom layers and non-sequential model types in TensorFlow, performed object detection, image segmentation, and interpretation of convolutions. Used generative deep learning including Auto Encoding, VAEs, and GANs to create new content.
Enhanced protein mutational sampling using time-lagged variational autoencoders
Master thesis: Structured Auto-Encoder with application to Music Genre Recognition (code)
This is the code of experiments in paper Cross-Domain Adversarial Auto-Encoder(https://arxiv.org/abs/1804.06078)
Point Cloud Gaussian Mixture Models and Shape Completion with Auto-encoder embeddings
Modified and example codes of GAN in pytorch
Master thesis: Structured Auto-Encoder with application to Music Genre Recognition (results)
Auto encoders based recommendation system
Collaborative and hybrid recommendation systems
simple VAE pytorch implementation
Building Auto-encoders using Deep Learning models in PyTorch
Implementation of some famous machine learning algorithm from scratch
This repository if for creating auto-encoders easily. The main focus of the auto-encoders on this page is for genetic and spectral data analysis but likely could be used for any high dimensional data
A docker environment and notebooks to experiment with the extraction of moore machines from RNN RL policies
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