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"Topic": models, code, and papers

An approach to image denoising using manifold approximation without clean images

Apr 28, 2019
Rohit Jena

Image restoration has been an extensively researched topic in numerous fields. With the advent of deep learning, a lot of the current algorithms were replaced by algorithms that are more flexible and robust. Deep networks have demonstrated impressive performance in a variety of tasks like blind denoising, image enhancement, deblurring, super-resolution, inpainting, among others. Most of these learning-based algorithms use a large amount of clean data during the training process. However, in certain applications in medical image processing, one may not have access to a large amount of clean data. In this paper, we propose a method for denoising that attempts to learn the denoising process by pushing the noisy data close to the clean data manifold, using only noisy images during training. Furthermore, we use perceptual loss terms and an iterative refinement step to further refine the clean images without losing important features.

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Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection Challenge

Feb 04, 2019
Md Ashraful Alam Milton

The main essence of this paper is to investigate the performance of RetinaNet based object detectors on pedestrian detection. Pedestrian detection is an important research topic as it provides a baseline for general object detection and has a great number of practical applications like autonomous car, robotics and Security camera. Though extensive research has made huge progress in pedestrian detection, there are still many issues and open for more research and improvement. Recent deep learning based methods have shown state-of-the-art performance in computer vision tasks such as image classification, object detection, and segmentation. Wider pedestrian detection challenge aims at finding improve solutions for pedestrian detection problem. In this paper, We propose a pedestrian detection system based on RetinaNet. Our solution has scored 0.4061 mAP. The code is available at

* ECCV Wider pedestrian detection challenege submission 

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Pair-wise Exchangeable Feature Extraction for Arbitrary Style Transfer

Nov 26, 2018
Zhijie Wu, Chunjin Song, Yang Zhou, Minglun Gong, Hui Huang

Style transfer has been an important topic in both computer vision and graphics. Gatys et al. first prove that deep features extracted by the pre-trained VGG network represent both content and style features of an image and hence, style transfer can be achieved through optimization in feature space. Huang et al. then show that real-time arbitrary style transfer can be done by simply aligning the mean and variance of each feature channel. In this paper, however, we argue that only aligning the global statistics of deep features cannot always guarantee a good style transfer. Instead, we propose to jointly analyze the input image pair and extract common/exchangeable style features between the two. Besides, a new fusion mode is developed for combining content and style information in feature space. Qualitative and quantitative experiments demonstrate the advantages of our approach.

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HANDS18: Methods, Techniques and Applications for Hand Observation

Oct 25, 2018
Iason Oikonomidis, Guillermo Garcia-Hernando, Angela Yao, Antonis Argyros, Vincent Lepetit, Tae-Kyun Kim

This report outlines the proceedings of the Fourth International Workshop on Observing and Understanding Hands in Action (HANDS 2018). The fourth instantiation of this workshop attracted significant interest from both academia and the industry. The program of the workshop included regular papers that are published as the workshop's proceedings, extended abstracts, invited posters, and invited talks. Topics of the submitted works and invited talks and posters included novel methods for hand pose estimation from RGB, depth, or skeletal data, datasets for special cases and real-world applications, and techniques for hand motion re-targeting and hand gesture recognition. The invited speakers are leaders in their respective areas of specialization, coming from both industry and academia. The main conclusions that can be drawn are the turn of the community towards RGB data and the maturation of some methods and techniques, which in turn has led to increasing interest for real-world applications.

* 11 pages, 1 figure, Discussion of the HANDS 2018 workshop held in conjunction with ECCV 2018 

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Small nonlinearities in activation functions create bad local minima in neural networks

Sep 28, 2018
Chulhee Yun, Suvrit Sra, Ali Jadbabaie

We investigate the loss surface of neural networks. We prove that even for one-hidden-layer networks with "slightest" nonlinearity, the empirical risks have spurious local minima in most cases. Our results thus indicate that in general "no spurious local minima" is a property limited to deep linear networks, and insights obtained from linear networks are not robust. Specifically, for ReLU(-like) networks we constructively prove that for almost all (in contrast to previous results) practical datasets there exist infinitely many local minima. We also present a counterexample for more general activations (sigmoid, tanh, arctan, ReLU, etc.), for which there exists a bad local minimum. Our results make the least restrictive assumptions relative to existing results on local optimality in neural networks. We complete our discussion by presenting a comprehensive characterization of global optimality for deep linear networks, which unifies other results on this topic.

* 36 pages 

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Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection

Sep 21, 2018
Lev Konstantinovskiy, Oliver Price, Mevan Babakar, Arkaitz Zubiaga

In an effort to assist factcheckers in the process of factchecking, we tackle the claim detection task, one of the necessary stages prior to determining the veracity of a claim. It consists of identifying the set of sentences, out of a long text, deemed capable of being factchecked. This paper is a collaborative work between Full Fact, an independent factchecking charity, and academic partners. Leveraging the expertise of professional factcheckers, we develop an annotation schema and a benchmark for automated claim detection that is more consistent across time, topics and annotators than previous approaches. Our annotation schema has been used to crowdsource the annotation of a dataset with sentences from UK political TV shows. We introduce an approach based on universal sentence representations to perform the classification, achieving an F1 score of 0.83, with over 5% relative improvement over the state-of-the-art methods ClaimBuster and ClaimRank. The system was deployed in production and received positive user feedback.

* Accepted at EMNLP 2018: Conference on Empirical Methods in Natural Language Processing (The First Workshop on Fact Extraction and Verification). (Non-archival) 

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Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts

Jul 08, 2018
Pankaj Gupta, Bernt Andrassy, Hinrich Schütze

The goal of our industrial ticketing system is to retrieve a relevant solution for an input query, by matching with historical tickets stored in knowledge base. A query is comprised of subject and description, while a historical ticket consists of subject, description and solution. To retrieve a relevant solution, we use textual similarity paradigm to learn similarity in the query and historical tickets. The task is challenging due to significant term mismatch in the query and ticket pairs of asymmetric lengths, where subject is a short text but description and solution are multi-sentence texts. We present a novel Replicated Siamese LSTM model to learn similarity in asymmetric text pairs, that gives 22% and 7% gain ([email protected]) for retrieval task, respectively over unsupervised and supervised baselines. We also show that the topic and distributed semantic features for short and long texts improved both similarity learning and retrieval.

* In the 27th International Conference on Computational Linguistics (COLING 2018) workshop on Semantic Deep Learning (SemDeep-3) 

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Road Extraction by Deep Residual U-Net

Nov 29, 2017
Zhengxin Zhang, Qingjie Liu, Yunhong Wang

Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network which combines the strengths of residual learning and U-Net is proposed for road area extraction. The network is built with residual units and has similar architecture to that of U-Net. The benefits of this model is two-fold: first, residual units ease training of deep networks. Second, the rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters however better performance. We test our network on a public road dataset and compare it with U-Net and other two state of the art deep learning based road extraction methods. The proposed approach outperforms all the comparing methods, which demonstrates its superiority over recently developed state of the arts.

* Submitted to IEEE Geoscience and Remote Sensing Letters 

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Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning

Oct 03, 2017
Xiangxiang Chu, Hangjun Ye

Deep reinforcement learning for multi-agent cooperation and competition has been a hot topic recently. This paper focuses on cooperative multi-agent problem based on actor-critic methods under local observations settings. Multi agent deep deterministic policy gradient obtained state of art results for some multi-agent games, whereas, it cannot scale well with growing amount of agents. In order to boost scalability, we propose a parameter sharing deterministic policy gradient method with three variants based on neural networks, including actor-critic sharing, actor sharing and actor sharing with partially shared critic. Benchmarks from rllab show that the proposed method has advantages in learning speed and memory efficiency, well scales with growing amount of agents, and moreover, it can make full use of reward sharing and exchangeability if possible.

* 12 pages, 6 figures 

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Deep Automated Multi-task Learning

Sep 19, 2017
Davis Liang, Yan Shu

Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims at improving the performance of a primary task, by jointly training on a secondary task. This paper introduces automated tasks, which exploit the sequential nature of the input data, as secondary tasks in an MTL model. We explore next word prediction, next character prediction, and missing word completion as potential automated tasks. Our results show that training on a primary task in parallel with a secondary automated task improves both the convergence speed and accuracy for the primary task. We suggest two methods for augmenting an existing network with automated tasks and establish better performance in topic prediction, sentiment analysis, and hashtag recommendation. Finally, we show that the MTL models can perform well on datasets that are small and colloquial by nature.

* IJCNLP 2017 

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