krunalr786 Goto Github PK

image-super-resolution-using-gan-srgan

It is the replication of the code in simpler terms available on GitHub.

imageanalysis_cml

Semantic Image Search with Convolutional Neural Networks

image_super_resolution

Increase image resolution by autoencoder

infrared-small-target-detection

insider-threat-detection-based-on-granularities

For data alignment, different data record granularity can be selected. For example, if converted to hourly accumulation, you can use:

insider-threat-detection-graphs-

An insider threat detection system using machine learning and the ELK stack

insider-threat-detection-lstm-autoencoder

Detecting insider threats in an organization by analyzing user computer data using LSTM autoencoder model

insiderthreatdetection

With r6.2

intrusion-detection-for-internet-of-vehicles

An intelligent classifier based Intrusion Detection System for IoV’s based on Neural Networks like optimized CNN, transfer learning and ensemble learning techniques and Hyper Parameter Optimizations.

itd_core

The CERT Division, in partnership with ExactData, LLC, and under sponsorship from DARPA I2O, generated a collection of synthetic insider threat test datasets. These datasets provide both synthetic background data and data from synthetic malicious actors. For more background on this data, please see the paper, Bridging the Gap: A Pragmatic Approach to Generating Insider Threat Data. Datasets are organized according to the data generator release that created them. Most releases include multiple datasets (e.g., r3.1 and r3.2).

keras-srgan

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network implemented in Keras

keras_anomaly_detection

CNN based autoencoder combined with kernel density estimation for colour image anomaly detection / novelty detection. Built using Tensforflow 2.0 and Keras

latest-development-of-isr-vsr

Latest development of ISR/VSR. Papers and related resources, mainly state-of-the-art and novel works in ICCV, ECCV and CVPR about image super-resolution and video super-resolution.

learn-tensorflow-and-vision-transformers

Learning Tensorflow Step by Step:: Concepts, Examples & Applications

models

A collection of pre-trained, state-of-the-art models in the ONNX format

models-sample-tensorflow

Models and examples built with TensorFlow

multimodal-deep-learning

This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.

numodril

NUMODRIL - Nuclear Morphology Optimized Deep Hybrid Learning

optformer

pwc

Papers with code. Sorted by stars. Updated weekly.

quantum-binary-classification-kears

Classical-Quantum hybrid model for credit card fraud detection

quantum-classical-hyrid-neural-network-for-binary-image-classification-using-pytorch-qiskit-pipeline

Quantum Hybrid Neural Network model for image classification

quantum-deep-learning

Recent advances in many fields have accelerated the demand for classification, regression, and detection problems from few 2D images/projections. Often, the heart of these modern techniques utilize neural networks, which can be implemented with deep learning algorithms. In our neural network architecture, we embed a dynamically programmable quantum circuit, acting as a hidden layer, to learn the correct parameters to correctly classify handwritten digits from the MNIST database. By starting small and making incremental improvements, we successfully reach a stunning ~95% accuracy on identifying previously unseen digits from 0 to 7 using this architecture!

quantum-machine-learning

Quantum Deep learning Binary Classification

quantum-machine-learning-1

This is an exploration using synthetic data in CSV format to apply QML models for the sake of binary classification. You can find here three different approaches. Two with Qiskit (VQC and QK/SVC) and one with Pennylane (QVC).

sota-deep-anomaly-detection

List of implementation of SOTA deep anomaly detection methods

srgan

HR to LR sample

srgan-tf2

An implementation of the Paper by Ledig, et al. (2017) titled 'Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network' using TensorFlow2 and Keras

super-image

State-of-the-art image super resolution models for PyTorch.

tensorflow

An Open Source Machine Learning Framework for Everyone