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64 papers · 1 filter
Efficient Super Resolution Using Binarized Neural Network
Yinglan Ma, Hongyu Xiong, Zhe Hu +1
Deep convolutional neural networks (DCNNs) have recently demonstrated high-quality results in single-image super-resolution (SR). DCNNs often suffer from over-parametrization and l…
Guided Zoom: Questioning Network Evidence for Fine-grained Classification
Sarah Adel Bargal, Andrea Zunino, Vitali Petsiuk +4
We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions. It does so by making sure the model has "the right reas…
Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations
Michael B. Cohen, Jonathan Kelner, Rasmus Kyng +4
We show how to solve directed Laplacian systems in nearly-linear time. Given a linear system in an Eulerian directed Laplacian with nonzero entries, we show how to…
Noise- and Outlier-Resistant Tomographic Reconstruction under Unknown Viewing Parameters
Ritwick Chaudhry, Arunabh Ghosh, Ajit Rajwade
In this paper, we present an algorithm for effectively reconstructing an object from a set of its tomographic projections without any knowledge of the viewing directions or any pri…
Unsupervised Deep Clustering for Source Separation: Direct Learning from Mixtures using Spatial Information
Efthymios Tzinis, Shrikant Venkataramani, Paris Smaragdis
We present a monophonic source separation system that is trained by only observing mixtures with no ground truth separation information. We use a deep clustering approach which tra…
Learning to Separate Multiple Illuminants in a Single Image
Zhuo Hui, Ayan Chakrabarti, Kalyan Sunkavalli +1
We present a method to separate a single image captured under two illuminants, with different spectra, into the two images corresponding to the appearance of the scene under each i…