Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks on ShortScience.org

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Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
Oquab, Maxime and Bottou, Léon and Laptev, Ivan and Sivic, Josef
Conference and Computer Vision and Pattern Recognition - 2014 via Local Bibsonomy
Keywords: dblp

Summaries/Notes 1

[link] Summary by Léo Paillier 6 years ago

_Objective:_ Transfer feature learned from large-scale dataset to small-scale dataset

 _Dataset:_ [ImageNet](www.image-net.org), [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/).


#### Inner-workings:

Basically they train the network on the large dataset, then replace the last layers, sometimes adding a new one and train this on the new dataset. Pretty standard transfer learning nowadays.  
[![screen shot 2017-06-14 at 3 06 37 pm](https://user-images.githubusercontent.com/17261080/27133634-2d4c0fde-5113-11e7-848a-719514b1a12c.png)](https://user-images.githubusercontent.com/17261080/27133634-2d4c0fde-5113-11e7-848a-719514b1a12c.png)

What's a bit more interesting is how they deal with background being overrepresented by using the bounding box that they have.  
[![screen shot 2017-06-14 at 3 06 43 pm](https://user-images.githubusercontent.com/17261080/27133641-34d4ee7e-5113-11e7-8307-f1ff708bd5c7.png)](https://user-images.githubusercontent.com/17261080/27133641-34d4ee7e-5113-11e7-8307-f1ff708bd5c7.png)

#### Results:

A bit dated, not really applicable but the part on specifically tackling the domain shift (such as background) is interesting.  
Plus they use the bounding-box information to refine the dataset.

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