Sequentially Generated Instance-Dependent Image Representations for Classification
Authors: Matthieu Cord, patrick gallinari, Nicolas Thome, Ludovic Denoyer, Gabriel Dulac-Arnold
ICLR 2014

Abstract: In this paper, we investigate a new framework for image classification that adaptively generates spatial representations. Our strategy is based on a sequential process that learns to explore the different regions of any image in order to infer its category. In particular, the choice of regions is specific to each image, directed by the actual content of previously selected regions.The capacity of the system to handle incomplete image information as well as its adaptive region selection allow the system to perform well in budgeted classification tasks by exploiting a dynamicly generated representation of each image. We demonstrate the system's abilities in a series of image-based exploration and classification tasks that highlight its learned exploration and inference abilities.


Peer review:
[Review]:
Thank you for your time and effort on reviewing our article. Let me respond in a couple points to your comments:

1. Classifier complexity:  In effect, the training algorithm is more complex than a standard SVM, but it only increases in complexity linearly relative to the fixed budget B and the number of windows.  Inference complexity is lower than an SVM computed on the entire picture, and is B times more complex than a fixed SVM on a B-sized window.  

2. With regards to the work of Larochelle & Hinton on foveal glimpse learning, there is a fundamental difference in what is being learned by the region selection algorithm. L&H learn a gaze direction model that greedily chooses regions that are most likely to increase the final classifier's output given the current state.  In our model, intermediate sub-policies learn to select regions that help their subsequent policy the most.  The final sub-policy indeed learns to select a Bth region that helps classification, but each of the previous policies learns to select a region that will best disambiguate the image information given the curernt state, only ultimately helping in classification.  This detail is important, as a greedy system will not spend time on image regions that are irrelevant to classification, but necessary to region selection.   Typically, in Experiment 2, H&L's system must be given a starting point, and would (as far as we can tell) not be able to find it on its own.