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amirunpri2018's Projects

ssgan-tensorflow icon ssgan-tensorflow

A Tensorflow implementation of Semi-supervised Learning Generative Adversarial Networks (NIPS 2016: Improved Techniques for Training GANs).

ssh icon ssh

SSH: Single Stage Headless Face Detector

ssh_tensorflow icon ssh_tensorflow

This is a tensorflow re-implementation of SSH: Single Stage Headless Face Detector.

ssr-net icon ssr-net

[IJCAI18] SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation

sst icon sst

Single Shot Tracker

ssta-captioning icon ssta-captioning

Repository for paper: Saliency-Based Spatio-Temporal Attention for Video Captioning

sstd icon sstd

Single Shot Text Detector with Regional Attention

st-gcn icon st-gcn

Spatial Temporal Graph Convolutional Networks (ST-GCN) for Skeleton-Based Action Recognition in PyTorch

stacknet icon stacknet

StackNet is a computational, scalable and analytical Meta modelling framework

staramr icon staramr

Scans genome contigs against the ResFinder and PointFinder databases.

starspace icon starspace

Learning embeddings for classification, retrieval and ranking.

state-of-the-art-result-for-machine-learning-problems icon state-of-the-art-result-for-machine-learning-problems

This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately.

steganogan icon steganogan

SteganoGAN is a tool for creating steganographic images using adversarial training.

stereovisionmrf icon stereovisionmrf

Using a MRF with loopy belief propagation to infer depth from stereo images.

stock-market-sentiment-analysis icon stock-market-sentiment-analysis

Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka

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