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High-Fidelity Synthesis with Disentangled Representation

, , ,  - 2020

Learning disentangled representation of data without supervision is an important step towards improving the interpretability of generative models. Despite recent advances in disentangled representation learning, existing approaches often suffer from the trade-off between representation learning and generation performance i.e. improving generation...


A Differentiable Color Filter for Generating Unrestricted Adversarial Images

, ,  - 2020

We propose Adversarial Color Filtering (AdvCF), an approach that uses a differentiable color filter to create adversarial images. The color filter allows us to introduce large perturbations into images, while still maintaining or enhancing their photographic quality and appeal. AdvCF is motivated by properties that are necessary if adversarial im...


Channel Pruning via Automatic Structure Search

, , , , ,  - 2020

Channel pruning is among the predominant approaches to compress deep neural networks. To this end, most existing pruning methods focus on selecting channels (filters) by importance/optimization or regularization based on rule-of-thumb designs, which defects in sub-optimal pruning. In this paper, we propose a new channel pruning method based on ar...


Centralized and distributed online learning for sparse time-varying optimization

 - 2020

The development of online algorithms to track time-varying systems has drawn a lot of attention in the last years, in particular in the framework of online convex optimization. Meanwhile, sparse time-varying optimization has emerged as a powerful tool to deal with widespread applications, ranging from dynamic compressed sensing to parsimonious sy...


S$^{2}$OMGAN: Shortcut from Remote Sensing Images to Online Maps

, , , , , ,  - 2020

Traditional online maps, widely used on Internet such as Google map and Baidu map, are rendered from vector data. Timely updating online maps from vector data, of which the generating is time-consuming, is a difficult mission. It is a shortcut to generate online maps in time from remote sensing images, which can be acquired timely without vector ...


Unsupervised Anomaly Detection for X-Ray Images

, , , , , , ,  - 2020

Obtaining labels for medical (image) data requires scarce and expensive experts. Moreover, due to ambiguous symptoms, single images rarely suffice to correctly diagnose a medical condition. Instead, it often requires to take additional background information such as the patient's medical history or test results into account. Hence, instead of foc...


OPFython: A Python-Inspired Optimum-Path Forest Classifier

, ,  - 2020

Machine learning techniques have been paramount throughout the last years, being applied in a wide range of tasks, such as classification, object recognition, person identification, image segmentation, among others. Nevertheless, conventional classification algorithms, e.g., Logistic Regression, Decision Trees, Bayesian classifiers, might lack co...


StageNet: Stage-Aware Neural Networks for Health Risk Prediction

, , , , ,  - 2020

Deep learning has demonstrated success in health risk prediction especially for patients with chronic and progressing conditions. Most existing works focus on learning disease Network (StageNet) model to extract disease stage information from patient data and integrate it into risk prediction. StageNet is enabled by (1) a stage-aware long short-t...


A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation

, , , ,  - 2020

Story generation, namely generating a reasonable story from a leading context, is an important but challenging task. In spite of the success in modeling fluency and local coherence, existing neural language generation models (e.g., GPT-2) still suffer from repetition, logic conflicts, and lack of long-range coherence in generated stories. We conj...


Torch-Struct: Deep Structured Prediction Library

 - 2020

The literature on structured prediction for NLP describes a rich collection of distributions and algorithms over sequences, segmentations, alignments, and trees; however, these algorithms are difficult to utilize in deep learning frameworks. We introduce Torch-Struct, a library for structured prediction designed to take advantage of and integrate...