EvaFlower

EvaFlower

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EvaFlower's repositories

CVProject_celldetection

I achieved to detect hemopoietic cell by using multi-radius ring filter. The hough transformation is very sensitive to the selected radius and needs a good edge detection so it did bad job for detection of hemopoietic cell. I track cells by detecting frame-by-frame and regard circle at most closest position as the same cell.

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Differential-Critic-GAN

Code for "Differential-Critic GAN: Generating What You Want by a Cue of Preferences (TNNLS 2022)"

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awesome-python-cn

Python资源大全中文版,包括:Web框架、网络爬虫、模板引擎、数据库、数据可视化、图片处理等,由伯乐在线持续更新。

FaceX-Zoo

A PyTorch Toolbox for Face Recognition

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Inception-Score

CPU/GPU/TPU implementation of the Inception Score

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pytorch-CycleGAN-and-pix2pix

Image-to-Image Translation in PyTorch

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stylegan2-ada

StyleGAN2 with adaptive discriminator augmentation (ADA) - Official TensorFlow implementation

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coco-labels

The labels for object categories in COCO dataset

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DAGAN

DAGAN: Data Augmentation Generative Adversarial Networks

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Interpretable_Kernel_Dim_Reduction

Interpretable Kernel Dimensionality Reduction

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kernel_dimension_reduction_clustering

Kernel Dimension Reduction clustering via HSIC

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lsun

LSUN Dataset Documentation and Demo Code

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ML_NOTE

Building ML knowledge background

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MMD_Loss.Pytorch

A pytorch implementation of Maximum Mean Discrepancies(MMD) loss

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PyTorch-GAN

PyTorch implementations of Generative Adversarial Networks.

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pytorch-MNIST-CelebA-GAN-DCGAN

Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets

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pytorch-msssim

PyTorch differentiable Multi-Scale Structural Similarity (MS-SSIM) loss

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RelGAN

Official Keras implementation of RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes

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Switchable_Normalization_TF

tensorflow implementation of Switch Norm - https://arxiv.org/abs/1806.10779

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UTSseminar

This is for recording our seminar. Just for own review.

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wgan-gp

A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

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