angelaAH

U2PL

[CVPR'22] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

Adv-ALSTM

Code for paper "Enhancing Stock Movement Prediction with Adversarial Training" IJCAI 2019

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

FCN-DenseNet

FCN implement with densenet for VOC dataset

Pytorch-UNet

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

FPN-Semantic-segmentation

use FPN to FPN-Semantic-segmentation。pytorch version

pytorch-deeplab-xception

DeepLab v3+ model in PyTorch. Support different backbones.

fdu-web