#### Problem addressed: 
Denoising images, Recognition in cluttered images, Segmentation of objects of interest

#### Summary: 
The authors propose an architecture to perform image denoising and segmentation for MNIST variation datasets and MNIST-2. Their work involves producing a 'cognitive' bias which is like a prior assumption that a certain class is present in the input image. Their network generates a denoised image given the prior class distribution at each iteration. A regular classifier is used at the end of generation process for termination condition. The generated image is gated with the input to prevent hallucination due to cognitive bias. MNIST-2 is created by the authors by superimposing 2 digits on the same image.

#### Novelty:
Integrates cognitive bias and feature extraction in the same network

#### Drawbacks:
Does not scale with number of classes. Works only on binarized images due to gating and masking

#### Datasets:
MNIST variations, MNIST-2

#### Resources:
http://papers.nips.cc/paper/5268-attentional-neural-network-feature-selection-using-cognitive-feedback.pdf

#### Presenter:
Bhargava U. Kota