https://github.com/MiyainNYC/Visual-Memorability-through-Caffe

CNN, Caffe, LaMem,Azure
https://github.com/MiyainNYC/Visual-Memorability-through-Caffe

Last synced: 3 months ago
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CNN, Caffe, LaMem,Azure

• Host: GitHub
• URL: https://github.com/MiyainNYC/Visual-Memorability-through-Caffe
• Owner: MiyainNYC
• Created: 2016-03-05T03:31:52.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2016-04-30T12:36:33.000Z (over 8 years ago)
• Last Synced: 2024-01-23T10:49:57.274Z (10 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 11.5 MB
• Stars: 18
• Watchers: 2
• Forks: 8
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• Awesome-CoreML-Models - LaMem

README

        
# Visual Memorability with Caffe Model

> @inproceedings{ICCV15_Khosla, author = "Aditya Khosla and Akhil S. Raju and Antonio Torralba and Aude Oliva", title = "Understanding and Predicting Image Memorability at a Large Scale", booktitle = "International Conference on Computer Vision (ICCV)", year = "2015" }

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Score the memorability of pictures by Running LaMem (image process model) through Caffe (deep learning framework)

### Interface:

* IPython Notebook

### Platform:

* Ubuntu

## Knowledge Applied:

* Convolutional Neural Network and Gradient Descent

* Loss Functions and Optimization

* Activation Functions and Weight Regularization

> Mini-batch SGD: 

* Sample a batch of data

* Forward prop it through the graph, get loss

* Backprop to calculate the gradients

* Update the parameters using the gradient

> Convoluntion Layer: COnvolvve the filter with the image and convolve(slide) over all spatial locations

> Pooling Layer: make the representations smaller and more manageable and operate over each activation map independently

> Fully Connected Layer(FC layer): contain neurons that connect to the entire input volume, as in ordinary Neural Networks

### Summary

- ConNets stack CONV,POOL, FC layers

- Trend towards smaller filters and deeper architectures

- Trend towards getting rid of POOL/FC layers(just CONV)

- Trend towards smaller