pwc-archive/papers-with-abstracts huggingface dataset:
Tackling Partial Domain Adaptation with Self-Supervision
Authors: Silvia Bucci, Antonio D'Innocente, Tatiana Tommasi
Date: 2019-06-12 00:00:00
Tasks: Domain Adaptation, Domain Generalization, Partial Domain Adaptation

Domain adaptation approaches have shown promising results in reducing the marginal distribution difference among visual domains. They allow to train reliable models that work over datasets of different nature (photos, paintings etc), but they still struggle when the domains do not share an identical label space. In the partial domain adaptation setting, where the target covers only a subset of the source classes, it is challenging to reduce the domain gap without incurring in negative transfer. Many solutions just keep the standard domain adaptation techniques by adding heuristic sample weighting strategies. In this work we show how the self-supervisory signal obtained from the spatial co-location of patches can be used to define a side task that supports adaptation regardless of the exact label sharing condition across domains. We build over a recent work that introduced a jigsaw puzzle task for domain generalization: we describe how to reformulate this approach for partial domain adaptation and we show how it boosts existing adaptive solutions when combined with them. The obtained experimental results on three datasets supports the effectiveness of our approach.
Vispi: Automatic Visual Perception and Interpretation of Chest X-rays
Authors: Xin Li, Rui Cao, Dongxiao Zhu
Date: 2019-06-12 00:00:00
Proceeding: MIDL 2019 7
Tasks: Diagnostic, Image Captioning

Medical imaging contains the essential information for rendering diagnostic and treatment decisions. Inspecting (visual perception) and interpreting image to generate a report are tedious clinical routines for a radiologist where automation is expected to greatly reduce the workload. Despite rapid development of natural image captioning, computer-aided medical image visual perception and interpretation remain a challenging task, largely due to the lack of high-quality annotated image-report pairs and tailor-made generative models for sufficient extraction and exploitation of localized semantic features, particularly those associated with abnormalities. To tackle these challenges, we present Vispi, an automatic medical image interpretation system, which first annotates an image via classifying and localizing common thoracic diseases with visual support and then followed by report generation from an attentive LSTM model. Analyzing an open IU X-ray dataset, we demonstrate a superior performance of Vispi in disease classification, localization and report generation using automatic performance evaluation metrics ROUGE and CIDEr.
Boosting Few-Shot Visual Learning with Self-Supervision
Authors: Spyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick Pérez, Matthieu Cord
Date: 2019-06-12 00:00:00
Proceeding: ICCV 2019 10
Tasks: Few-Shot Learning, Self-Supervised Learning

Few-shot learning and self-supervised learning address different facets of the same problem: how to train a model with little or no labeled data. Few-shot learning aims for optimization methods and models that can learn efficiently to recognize patterns in the low data regime. Self-supervised learning focuses instead on unlabeled data and looks into it for the supervisory signal to feed high capacity deep neural networks. In this work we exploit the complementarity of these two domains and propose an approach for improving few-shot learning through self-supervision. We use self-supervision as an auxiliary task in a few-shot learning pipeline, enabling feature extractors to learn richer and more transferable visual representations while still using few annotated samples. Through self-supervision, our approach can be naturally extended towards using diverse unlabeled data from other datasets in the few-shot setting. We report consistent improvements across an array of architectures, datasets and self-supervision techniques.
Parameterized Structured Pruning for Deep Neural Networks
Authors: Guenther Schindler, Wolfgang Roth, Franz Pernkopf, Holger Froening
Date: 2019-06-12 00:00:00
Tasks: Quantization

As a result of the growing size of Deep Neural Networks (DNNs), the gap to hardware capabilities in terms of memory and compute increases. To effectively compress DNNs, quantization and connection pruning are usually considered. However, unconstrained pruning usually leads to unstructured parallelism, which maps poorly to massively parallel processors, and substantially reduces the efficiency of general-purpose processors. Similar applies to quantization, which often requires dedicated hardware. We propose Parameterized Structured Pruning (PSP), a novel method to dynamically learn the shape of DNNs through structured sparsity. PSP parameterizes structures (e.g. channel- or layer-wise) in a weight tensor and leverages weight decay to learn a clear distinction between important and unimportant structures. As a result, PSP maintains prediction performance, creates a substantial amount of sparsity that is structured and, thus, easy and efficient to map to a variety of massively parallel processors, which are mandatory for utmost compute power and energy efficiency. PSP is experimentally validated on the popular CIFAR10/100 and ILSVRC2012 datasets using ResNet and DenseNet architectures, respectively.
Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites
Authors: Alberto Alvarez, Steve Dahlskog, Jose Font, Julian Togelius
Date: 2019-06-12 00:00:00
Tasks: Diversity

We propose the use of quality-diversity algorithms for mixed-initiative game content generation. This idea is implemented as a new feature of the Evolutionary Dungeon Designer, a system for mixed-initiative design of the type of levels you typically find in computer role playing games. The feature uses the MAP-Elites algorithm, an illumination algorithm which divides the population into a number of cells depending on their values along several behavioral dimensions. Users can flexibly and dynamically choose relevant dimensions of variation, and incorporate suggestions produced by the algorithm in their map designs. At the same time, any modifications performed by the human feed back into MAP-Elites, and are used to generate further suggestions.
Multi-local Collaborative AutoEncoder
Authors: Jielei Chu, Hongjun Wang, Jing Liu, Zhiguo Gong, Tianrui Li
Date: 2019-06-12 00:00:00
Tasks: Clustering, Representation Learning

The excellent performance of representation learning of autoencoders have attracted considerable interest in various applications. However, the structure and multi-local collaborative relationships of unlabeled data are ignored in their encoding procedure that limits the capability of feature extraction. This paper presents a Multi-local Collaborative AutoEncoder (MC-AE), which consists of novel multi-local collaborative representation RBM (mcrRBM) and multi-local collaborative representation GRBM (mcrGRBM) models. Here, the Locality Sensitive Hashing (LSH) method is used to divide the input data into multi-local cross blocks which contains multi-local collaborative relationships of the unlabeled data and features since the similar multi-local instances and features of the input data are divided into the same block. In mcrRBM and mcrGRBM models, the structure and multi-local collaborative relationships of unlabeled data are integrated into their encoding procedure. Then, the local hidden features converges on the center of each local collaborative block. Under the collaborative joint influence of each local block, the proposed MC-AE has powerful capability of representation learning for unsupervised clustering. However, our MC-AE model perhaps perform training process for a long time on the large-scale and high-dimensional datasets because more local collaborative blocks are integrate into it. Five most related deep models are compared with our MC-AE. The experimental results show that the proposed MC-AE has more excellent capabilities of collaborative representation and generalization than the contrastive deep models.
Attention-based Multi-Input Deep Learning Architecture for Biological Activity Prediction: An Application in EGFR Inhibitors
Authors: Huy Ngoc Pham, Trung Hoang Le
Date: 2019-06-12 00:00:00
Tasks: Activity Prediction, BIG-bench Machine Learning, Deep Learning, Drug Discovery

Machine learning and deep learning have gained popularity and achieved immense success in Drug discovery in recent decades. Historically, machine learning and deep learning models were trained on either structural data or chemical properties by separated model. In this study, we proposed an architecture training simultaneously both type of data in order to improve the overall performance. Given the molecular structure in the form of SMILES notation and their label, we generated the SMILES-based feature matrix and molecular descriptors. These data were trained on a deep learning model which was also integrated with the Attention mechanism to facilitate training and interpreting. Experiments showed that our model could raise the performance of prediction comparing to the reference. With the maximum MCC 0.58 and AUC 90% by cross-validation on EGFR inhibitors dataset, our architecture was outperforming the referring model. We also successfully integrated Attention mechanism into our model, which helped to interpret the contribution of chemical structures on bioactivity.
Stereoscopic Omnidirectional Image Quality Assessment Based on Predictive Coding Theory
Authors: Zhibo Chen, Jiahua Xu, Chaoyi Lin, Wei Zhou
Date: 2019-06-12 00:00:00
Tasks: Image Quality Assessment

Objective quality assessment of stereoscopic omnidirectional images is a challenging problem since it is influenced by multiple aspects such as projection deformation, field of view (FoV) range, binocular vision, visual comfort, etc. Existing studies show that classic 2D or 3D image quality assessment (IQA) metrics are not able to perform well for stereoscopic omnidirectional images. However, very few research works have focused on evaluating the perceptual visual quality of omnidirectional images, especially for stereoscopic omnidirectional images. In this paper, based on the predictive coding theory of the human vision system (HVS), we propose a stereoscopic omnidirectional image quality evaluator (SOIQE) to cope with the characteristics of 3D 360-degree images. Two modules are involved in SOIQE: predictive coding theory based binocular rivalry module and multi-view fusion module. In the binocular rivalry module, we introduce predictive coding theory to simulate the competition between high-level patterns and calculate the similarity and rivalry dominance to obtain the quality scores of viewport images. Moreover, we develop the multi-view fusion module to aggregate the quality scores of viewport images with the help of both content weight and location weight. The proposed SOIQE is a parametric model without necessary of regression learning, which ensures its interpretability and generalization performance. Experimental results on our published stereoscopic omnidirectional image quality assessment database (SOLID) demonstrate that our proposed SOIQE method outperforms state-of-the-art metrics. Furthermore, we also verify the effectiveness of each proposed module on both public stereoscopic image datasets and panoramic image datasets.
General Video Game Rule Generation
Authors: Ahmed Khalifa, Michael Cerny Green, Diego Perez-Liebana, Julian Togelius
Date: 2019-06-12 00:00:00

We introduce the General Video Game Rule Generation problem, and the eponymous software framework which will be used in a new track of the General Video Game AI (GVGAI) competition. The problem is, given a game level as input, to generate the rules of a game that fits that level. This can be seen as the inverse of the General Video Game Level Generation problem. Conceptualizing these two problems as separate helps breaking the very hard problem of generating complete games into smaller, more manageable subproblems. The proposed framework builds on the GVGAI software and thus asks the rule generator for rules defined in the Video Game Description Language. We describe the API, and three different rule generators: a random, a constructive and a search-based generator. Early results indicate that the constructive generator generates playable and somewhat interesting game rules but has a limited expressive range, whereas the search-based generator generates remarkably diverse rulesets, but with an uneven quality.
Evaluation of Dataflow through layers of Deep Neural Networks in Classification and Regression Problems
Authors: Ahmad Kalhor, Mohsen Saffar, Melika Kheirieh, Somayyeh Hoseinipoor, Babak N. Araabi
Date: 2019-06-12 00:00:00
Tasks: Classification, General Classification, regression

This paper introduces two straightforward, effective indices to evaluate the input data and the data flowing through layers of a feedforward deep neural network. For classification problems, the separation rate of target labels in the space of dataflow is explained as a key factor indicating the performance of designed layers in improving the generalization of the network. According to the explained concept, a shapeless distance-based evaluation index is proposed. Similarly, for regression problems, the smoothness rate of target outputs in the space of dataflow is explained as a key factor indicating the performance of designed layers in improving the generalization of the network. According to the explained smoothness concept, a shapeless distance-based smoothness index is proposed for regression problems. To consider more strictly concepts of separation and smoothness, their extended versions are introduced, and by interpreting a regression problem as a classification problem, it is shown that the separation and smoothness indices are related together. Through four case studies, the profits of using the introduced indices are shown. In the first case study, for classification and regression problems , the challenging of some known input datasets are compared respectively by the proposed separation and smoothness indices. In the second case study, the quality of dataflow is evaluated through layers of two pre-trained VGG 16 networks in classification of Cifar10 and Cifar100. In the third case study, it is shown that the correct classification rate and the separation index are almost equivalent through layers particularly while the serration index is increased. In the last case study, two multi-layer neural networks, which are designed for the prediction of Boston Housing price, are compared layer by layer by using the proposed smoothness index.
Putting words in context: LSTM language models and lexical ambiguity
Authors: Laura Aina, Kristina Gulordava, Gemma Boleda
Date: 2019-06-12 00:00:00
Proceeding: ACL 2019 7
Tasks: Language Modeling, Language Modelling, Word Embeddings

In neural network models of language, words are commonly represented using context-invariant representations (word embeddings) which are then put in context in the hidden layers. Since words are often ambiguous, representing the contextually relevant information is not trivial. We investigate how an LSTM language model deals with lexical ambiguity in English, designing a method to probe its hidden representations for lexical and contextual information about words. We find that both types of information are represented to a large extent, but also that there is room for improvement for contextual information.
Recognizing Manipulation Actions from State-Transformations
Authors: Nachwa Aboubakr, James L. Crowley, Remi Ronfard
Date: 2019-06-12 00:00:00
Tasks: Action Recognition, Object, Spatio-temporal Action Recognition

Manipulation actions transform objects from an initial state into a final state. In this paper, we report on the use of object state transitions as a mean for recognizing manipulation actions. Our method is inspired by the intuition that object states are visually more apparent than actions from a still frame and thus provide information that is complementary to spatio-temporal action recognition. We start by defining a state transition matrix that maps action labels into a pre-state and a post-state. From each keyframe, we learn appearance models of objects and their states. Manipulation actions can then be recognized from the state transition matrix. We report results on the EPIC kitchen action recognition challenge.
A decentralized trust-aware collaborative filtering recommender system based on weighted items for social tagging systems
Authors: Hossein Monshizadeh Naeen, Mehrdad Jalali
Date: 2019-06-12 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems, TAG

Recommender systems are used with the purpose of suggesting contents and resources to the users in a social network. These systems use ranks or tags each user assign to different resources to predict or make suggestions to users. Lately, social tagging systems, in which users can insert new contents, tag, organize, share, and search for contents are becoming more popular. These systems have a lot of valuable information, but data growth is one of its biggest challenges and this has led to the need for recommender systems that will predict what each user may like or need. One approach to the design of these systems which uses social environment of users is known as collaborative filtering (CF). One of the problems in CF systems is trustworthy of users and their tags. In this work, we consider a trust metric (which is concluded from users tagging behavior) beside the similarities to give suggestions and examine its effect on results. On the other hand, a decentralized approach is introduced which calculates similarity and trust relationships between users in a distributed manner. This causes the capability of implementing the proposed approach among all types of users with respect to different types of items, which are accessed by unique id across heterogeneous networks and environments. Finally, we show that the proposed model for calculating similarities between users reduces the size of the user-item matrix and considering trust in collaborative systems can lead to a better performance in generating suggestions.
High Accuracy Classification of White Blood Cells using TSLDA Classifier and Covariance Features
Authors: Hamed Talebi, Amin Ranjbar, Alireza Davoudi, Hamed Gholami, Mohammad Bagher Menhaj
Date: 2019-06-12 00:00:00
Tasks: Clustering, General Classification

creating automated processes in different areas of medical science with the application of engineering tools is a highly growing field over recent decades. In this context, many medical image processing and analyzing researchers use worthwhile methods in artificial intelligence, which can reduce necessary human power while increases accuracy of results. Among various medical images, blood microscopic images play a vital role in heart failure diagnosis, e.g., blood cancers. The prominent component in blood cancer diagnosis is white blood cells (WBCs) which due to its general characteristics in microscopic images sometimes make difficulties in recognition and classification tasks such as non-uniform colors/illuminances, different shapes, sizes, and textures. Moreover, overlapped WBCs in bone marrow images and neighboring to red blood cells are identified as reasons for errors in the classification task. In this paper, we have endeavored to segment various parts in medical images via Na\"ive Bayes clustering method and in next stage via TSLDA classifier, which is supplied by features acquired from covariance descriptor results in the accuracy of 98.02%. It seems that this result is delightful in WBCs recognition.
LED2Net: Deep Illumination-aware Dehazing with Low-light and Detail Enhancement
Authors: Guisik Kim, Junseok Kwon
Date: 2019-06-12 00:00:00

We present a novel dehazing and low-light enhancement method based on an illumination map that is accurately estimated by a convolutional neural network (CNN). In this paper, the illumination map is used as a component for three different tasks, namely, atmospheric light estimation, transmission map estimation, and low-light enhancement. To train CNNs for dehazing and low-light enhancement simultaneously based on the retinex theory, we synthesize numerous low-light and hazy images from normal hazy images from the FADE data set. In addition, we further improve the network using detail enhancement. Experimental results demonstrate that our method surpasses recent state-of-theart algorithms quantitatively and qualitatively. In particular, our haze-free images present vivid colors and enhance visibility without a halo effect or color distortion.
Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function
Authors: Zihan Zhang, Xiangyang Ji
Date: 2019-06-12 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present an algorithm based on the \emph{Optimism in the Face of Uncertainty} (OFU) principle which is able to learn Reinforcement Learning (RL) modeled by Markov decision process (MDP) with finite state-action space efficiently. By evaluating the state-pair difference of the optimal bias function $h^{*}$, the proposed algorithm achieves a regret bound of $\tilde{O}(\sqrt{SAHT})$\footnote{The symbol $\tilde{O}$ means $O$ with log factors ignored. } for MDP with $S$ states and $A$ actions, in the case that an upper bound $H$ on the span of $h^{*}$, i.e., $sp(h^{*})$ is known. This result outperforms the best previous regret bounds $\tilde{O}(S\sqrt{AHT}) $\citep{fruit2019improved} by a factor of $\sqrt{S}$. Furthermore, this regret bound matches the lower bound of $\Omega(\sqrt{SAHT}) $\citep{jaksch2010near} up to a logarithmic factor. As a consequence, we show that there is a near optimal regret bound of $\tilde{O}(\sqrt{SADT})$ for MDPs with a finite diameter $D$ compared to the lower bound of $\Omega(\sqrt{SADT}) $\citep{jaksch2010near}.
Secure Federated Matrix Factorization
Authors: Di Chai, Leye Wang, Kai Chen, Qiang Yang
Date: 2019-06-12 00:00:00
Tasks: BIG-bench Machine Learning, Federated Learning

To protect user privacy and meet law regulations, federated (machine) learning is obtaining vast interests in recent years. The key principle of federated learning is training a machine learning model without needing to know each user's personal raw private data. In this paper, we propose a secure matrix factorization framework under the federated learning setting, called FedMF. First, we design a user-level distributed matrix factorization framework where the model can be learned when each user only uploads the gradient information (instead of the raw preference data) to the server. While gradient information seems secure, we prove that it could still leak users' raw data. To this end, we enhance the distributed matrix factorization framework with homomorphic encryption. We implement the prototype of FedMF and test it with a real movie rating dataset. Results verify the feasibility of FedMF. We also discuss the challenges for applying FedMF in practice for future research.