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A collection of infrastructure and tools for research in neural network interpretability.

physics-informed neural networks parts for ___

Official Pytorch code for "AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer" (AAAI 2024)

Takagi and Nishimoto, CVPR 2023

Official implementation of paper Deep Feature Rotation for Multimodal Image Style Transfer [2021 8th NAFOSTED Conference on Information and Computer Science]

starter from "How to Train a GAN?" at NIPS2016

Retinal blood vessel segmentation using a tiny U-Net model

Image restoration with neural networks but without learning.

This is the official implementation of our publication "Deep learning enables fast and dense single-molecule localization with high accuracy" (Nature Methods)

TensorFlow CNN for fast style transfer ⚡🖥🎨🖼

Official Implementation of 'Fast AutoAugment' in PyTorch.

Microscope control software

Progressive Growing of GANs for Improved Quality, Stability, and Variation (16 bit)

Main source code for Kidney Tumor Segmentation (KiTS) Challenge 2019 in MICCAI 2019

Deep Learning Paper Study in MI2RL@AsanMedicalCenter

The hub of pre-trained models in the medical domain

DeepSTORM3D implemented in Pytorch

Official implementation of Deep-STORM for dense 2D localization microscopy

STORM movie analysis code

Feedforward style transfer

전 세계의 멋진 케라스 문서 및 튜토리얼을 한글화하여 케라스x코리아를 널리널리 이롭게합니다.

침착한 생성모델 학습기

[머신러닝과딥러닝] 수업 정리 / Tutorial of ML and DL

The world's simplest facial recognition api for Python and the command line