Neural style transfer as a class in PyTorch

Last update: Jun 27, 2022

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Deep Learning pt-styletransfer

Overview

pt-styletransfer

Neural style transfer as a class in PyTorch

Based on: https://github.com/alexis-jacq/Pytorch-Tutorials

Adds:

StyleTransferNet as a class that can be imported by other scripts

VGG support (this was before pretrained VGG models were available in PyTorch)

Convenience functions for saving intermediate style & content targets for display

Convenience function for examining gram matricies as images

Auto style, content, and product image saving

matplotlib plots of losses over time & record of hyperparameters to keep track of favorable results

Owner

Tyler Kvochick

tweeter: (at)tyler_kvochick

GitHub Repository

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Neural style transfer as a class in PyTorch

Related tags

Overview

pt-styletransfer

Owner

Tyler Kvochick

Transformer model implemented with Pytorch

Fre-GAN: Adversarial Frequency-consistent Audio Synthesis

In this project, we develop a face recognize platform based on MTCNN object-detection netcwork and FaceNet self-supervised network.

Notebook and code to synthesize complex and highly dimensional datasets using Gretel APIs.

Implementation of the paper Scalable Intervention Target Estimation in Linear Models (NeurIPS 2021), and the code to generate simulation results.

Random Walk Graph Neural Networks

R3Det based on mmdet 2.19.0

[CVPRW 21] "BNN - BN = ? Training Binary Neural Networks without Batch Normalization", Tianlong Chen, Zhenyu Zhang, Xu Ouyang, Zechun Liu, Zhiqiang Shen, Zhangyang Wang

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

MatchGAN: A Self-supervised Semi-supervised Conditional Generative Adversarial Network

Official code repository for the work: "The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement"

[AAAI 2022] Separate Contrastive Learning for Organs-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation

On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation (Findings of EMNLP 2021))

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