Attentional Neural Network: Feature Selection Using Cognitive Feedback Attentional Neural Network: Feature Selection Using Cognitive Feedback
Paper summary #### 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
papers.nips.cc
scholar.google.com
Attentional Neural Network: Feature Selection Using Cognitive Feedback
Wang, Qian and Zhang, Jiaxing and Song, Sen and Zhang, Zheng
Neural Information Processing Systems Conference - 2014 via Local Bibsonomy
Keywords: dblp


Loading...
Your comment:


ShortScience.org allows researchers to publish paper summaries that are voted on and ranked!
About