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" }

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