neural image generation

Last update: Dec 17, 2022

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Overview

pixray

Pixray is an image generation system. It combines previous ideas including:

Perception Engines which uses image augmentation and iteratively optimises images against an ensemble of classifiers
CLIP guided GAN imagery from Ryan Murdoch and Katherine Crowson as well as modifictions such as CLIPDraw from Kevin Frans
Useful ways of navigating latent space from Sampling Generative Networks
(more to come)

pixray it itself a python library and command line utility, but is also friendly to running on line in Google Colab notebooks.

The system is currently lacking documentation. Instead plese checkout THE DEMO NOTEBOOKS - especially the super simple "Start Here" colab.

Owner

dribnet

Lecturer at University of Wellington School of Design teaching creative coding and researching neural design.

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neural image generation

Related tags

Overview

pixray

Owner

dribnet

A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes

Dynamic vae - Dynamic VAE algorithm is used for anomaly detection of battery data

TANL: Structured Prediction as Translation between Augmented Natural Languages

Library to enable Bayesian active learning in your research or labeling work.

Codebase for the solution that won first place and was awarded the most human-like agent in the 2021 NeurIPS Competition MineRL BASALT Challenge.

Bayesian Meta-Learning Through Variational Gaussian Processes

Efficient Two-Step Networks for Temporal Action Segmentation (Neurocomputing 2021)

Lab Materials for MIT 6.S191: Introduction to Deep Learning

Semi-supevised Semantic Segmentation with High- and Low-level Consistency

Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

Official PyTorch(Geometric) implementation of DPGNN(DPGCN) in "Distance-wise Prototypical Graph Neural Network for Node Imbalance Classification"

This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.

Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark

Efficient Sharpness-aware Minimization for Improved Training of Neural Networks

Principled Detection of Out-of-Distribution Examples in Neural Networks

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PyTorch implementation of the paper The Lottery Ticket Hypothesis for Object Recognition

Unofficial implementation of the paper: PonderNet: Learning to Ponder in TensorFlow

Improving Query Representations for DenseRetrieval with Pseudo Relevance Feedback:A Reproducibility Study.

PyTorch implementation of Decoupling Value and Policy for Generalization in Reinforcement Learning