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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