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Found 4 papers, 1 papers with code
Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers
Picasso is a free open-source (Eclipse Public License) web application written in Python for rendering standard visualizations useful for analyzing convolutional neural networks.
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks
In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels.
Ranked #3 on
Age Estimation
on ChaLearn 2015
Seven ways to improve example-based single image super resolution
In this paper we present seven techniques that everybody should know to improve example-based single image super resolution (SR): 1) augmentation of data, 2) use of large dictionaries with efficient search structures, 3) cascading, 4) image self-similarities, 5) back projection refinement, 6) enhanced prediction by consistency check, and 7) context reasoning.
Ranked #45 on
Image Super-Resolution
on BSD100 - 4x upscaling
Some like it hot - visual guidance for preference prediction
Our computational pipeline comprises a face detector, convolutional neural networks for the extraction of deep features, standard support vector regression for gender, age and facial beauty, and - as the main novelties - visual regularized collaborative filtering to infer inter-person preferences as well as a novel regression technique for handling visual queries without rating history.
