_Objective:_ Specifically adapt Active Learning to Image Classification with deep learning

_Dataset:_ [CARC](https://bcsiriuschen.github.io/CARC/) and [Caltech-256](http://authors.library.caltech.edu/7694/)

## Inner-workings:

They labels from two sources:

*   The most informative/uncertain samples are manually labeled using Least confidence, margin sampling and entropy, see [Active Learning Literature Survey](https://github.com/Deepomatic/papers/issues/192).
*   The other kind is the samples with high prediction confidence that are automatically labelled. They represent the majority of samples.

## Architecture:

[![screen shot 2017-06-29 at 3 57 43 pm](https://user-images.githubusercontent.com/17261080/27691277-d4547196-5ce3-11e7-849c-aadd30d71d68.png)](https://user-images.githubusercontent.com/17261080/27691277-d4547196-5ce3-11e7-849c-aadd30d71d68.png)

They proceed with the following steps:

1.  Initialization: they manually annotate a given number of images for each class in order to pre-trained the network.
2.  Complementary sample selection: they fix the network, identity the most uncertain label for manual annotation and automatically annotate the most certain one if their entropy is higher than a given threshold.
3.  CNN fine-tuning: they train the network using the whole pool of already labeled data and pseudo-labeled. Then they put all the automatically labeled images back into the unlabelled pool.
4.  Threshold updating: as the network gets more and more confident the threshold for auto-labelling is linearly reducing. The idea is that the network gets a more reliable representation and its trustability increases.

## Results:

Roughly divide by 2 the number of annotation needed.  
⚠️ I don't feel like this paper can be trusted 100% ⚠️