The Dual Memory is build from a simple CNN for the deep memory and Linear Regression fro the fast Memory

Overview

Simple-DMA

a simple Dual Memory Architecture for classifications.

based on the paper Dual-Memory Deep Learning Architectures for Lifelong Learning of Everyday Human Behaviors written by Sang-Woo Lee1, Chung-Yeon Lee1, Dong Hyun Kwak2, Jiwon Kim3, Jeonghee Kim3, and Byoung-Tak Zhang1,2.

The Dual Memory is build from a simple CNN for the deep memory and Linear Regression fro the fast Memory

CNN architecture:

CNN Architecture

DMA architecture:

DMA

The Code:

main.py - the Dual Memory architecture model training and running.
CNN-full_dataset.py - running the CNN on the entire dataset inorder to see how well it works.
CNN_retrainning_model.py - running the CNN on 10 split datasets to see the catastrophic forgetting phenomenon.
cnn.py - the class for creating a cnn.
model.py - the class for creating the Dual Memory model.
Bqueue.py - the class for managing the long term memory.
Plotter.py - the class for plotting the graphs of the scores from the DMA snd CNN. 

The libraries we used:

Tensorflow.keras - for the nueral networks
sklearn - for the preprocessing of the data, shuffling of the data, splitting the data to train and test
  	  and for the linear model.
logging - for writing to the output window
numpy - for processing the data
copy - for copying the model
shutil - for getting the terminal columns.
tqdm - for printing the process bar in the output window.
matplotlib.pyplot - for plotting the graphs.

RUN

In order to run the DMA just open it in pycharm, make sure you have all the libraries installed.

with the scores of the CNN_retrainning_model.py that you recieve after running it you plot the graph of the catastrophic forgetting
that we have presented in class - ithe plot happenes automatically.

with the scores of the main.py that you recieve after running it you plot the graph of the final results that we have presented in class 
and the scores of the DMA vs the scores of the CNN - the plot happenes automatically.
TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning

TransZero++ This repository contains the testing code for the paper "TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning" submitted

Shiming Chen 6 Aug 16, 2022
Axel - 3D printed robotic hands and they controll with Raspberry Pi and Arduino combo

Axel It's our graduation project about 3D printed robotic hands and they control

0 Feb 14, 2022
Lightweight Python library for adding real-time object tracking to any detector.

Norfair is a customizable lightweight Python library for real-time 2D object tracking. Using Norfair, you can add tracking capabilities to any detecto

Tryolabs 1.7k Jan 05, 2023
Automatic tool focused on deriving metallicities of open clusters

metalcode Automatic tool focused on deriving metallicities of open clusters. Based on the method described in Pöhnl & Paunzen (2010, https://ui.adsabs

2 Dec 13, 2021
A simplified framework and utilities for PyTorch

Here is Poutyne. Poutyne is a simplified framework for PyTorch and handles much of the boilerplating code needed to train neural networks. Use Poutyne

GRAAL/GRAIL 534 Dec 17, 2022
Official PyTorch implementation of the paper "Recycling Discriminator: Towards Opinion-Unaware Image Quality Assessment Using Wasserstein GAN", accepted to ACM MM 2021 BNI Track.

RecycleD Official PyTorch implementation of the paper "Recycling Discriminator: Towards Opinion-Unaware Image Quality Assessment Using Wasserstein GAN

Yunan Zhu 23 Nov 05, 2022
Assginment for UofT CSC420: Intro to Image Understanding

Run the code Open edge_detection.ipynb in google colab. Upload image1.jpg,image2.jpg and my_image.jpg to '/content/drive/My Drive'. chooose 'Run all'

Ziyi-Zhou 1 Feb 24, 2022
A weakly-supervised scene graph generation codebase. The implementation of our CVPR2021 paper ``Linguistic Structures as Weak Supervision for Visual Scene Graph Generation''

README.md shall be finished soon. WSSGG 0 Overview 1 Installation 1.1 Faster-RCNN 1.2 Language Parser 1.3 GloVe Embeddings 2 Settings 2.1 VG-GT-Graph

Keren Ye 35 Nov 20, 2022
MLOps will help you to understand how to build a Continuous Integration and Continuous Delivery pipeline for an ML/AI project.

page_type languages products description sample python azure azure-machine-learning-service azure-devops Code which demonstrates how to set up and ope

1 Nov 01, 2021
Official PyTorch Code of GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection (CVPR 2021)

GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Mo

Abhinav Kumar 76 Jan 02, 2023
Speedy Implementation of Instance-based Learning (IBL) agents in Python

A Python library to create single or multi Instance-based Learning (IBL) agents that are built based on Instance Based Learning Theory (IBLT) 1 Instal

0 Nov 18, 2021
Deep Structured Instance Graph for Distilling Object Detectors (ICCV 2021)

DSIG Deep Structured Instance Graph for Distilling Object Detectors Authors: Yixin Chen, Pengguang Chen, Shu Liu, Liwei Wang, Jiaya Jia. [pdf] [slide]

DV Lab 31 Nov 17, 2022
A general python framework for visual object tracking and video object segmentation, based on PyTorch

PyTracking A general python framework for visual object tracking and video object segmentation, based on PyTorch. 📣 Two tracking/VOS papers accepted

2.6k Jan 04, 2023
Automated Hyperparameter Optimization Competition

QQ浏览器2021AI算法大赛 - 自动超参数优化竞赛 ACM CIKM 2021 AnalyticCup 在信息流推荐业务场景中普遍存在模型或策略效果依赖于“超参数”的问题,而“超参数"的设定往往依赖人工经验调参,不仅效率低下维护成本高,而且难以实现更优效果。因此,本次赛题以超参数优化为主题,从真

20 Dec 09, 2021
Adaptive Denoising Training (ADT) for Recommendation.

DenoisingRec Adaptive Denoising Training for Recommendation. This is the pytorch implementation of our paper at WSDM 2021: Denoising Implicit Feedback

Wenjie Wang 51 Dec 30, 2022
Implementation of the SUMO (Slim U-Net trained on MODA) model

SUMO - Slim U-Net trained on MODA Implementation of the SUMO (Slim U-Net trained on MODA) model as described in: TODO: add reference to paper once ava

6 Nov 19, 2022
Replication package for the manuscript "Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?" submitted to TOSEM

tosem2021-personality-rep-package Replication package for the manuscript "Using Personality Detection Tools for Software Engineering Research: How Far

Collaborative Development Group 1 Dec 13, 2021
Public repository created to store my custom-made tools for Just Dance (UbiArt Engine)

Woody's Just Dance Tools Public repository created to store my custom-made tools for Just Dance (UbiArt Engine) Development and updates Almost all of

Wodson de Andrade 8 Dec 24, 2022
CSAC - Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization

CSAC Introduction This repository contains the implementation code for paper: Co

ScottYuan 5 Jul 22, 2022
System-oriented IR evaluations are limited to rather abstract understandings of real user behavior

Validating Simulations of User Query Variants This repository contains the scripts of the experiments and evaluations, simulated queries, as well as t

IR Group at Technische Hochschule Köln 2 Nov 23, 2022