January 31, 2020

2967 words 14 mins read

Paper Group ANR 27

Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Image. Pretrain Soft Q-Learning with Imperfect Demonstrations. Temporal Analysis of Reddit Networks via Role Embeddings. When Labelled Data Hurts: Deep Semi-Supervised Classification with the Graph 1-Laplacian. Knowledge Distillation via Route Constrained Optimization. A …

Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Image


Title	Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Image
Authors	Qingbo Wu, Lei Wang, King N. Ngan, Hongliang Li, Fanman Meng, Linfeng Xu
Abstract	Images acquired by outdoor vision systems easily suffer poor visibility and annoying interference due to the rainy weather, which brings great challenge for accurately understanding and describing the visual contents. Recent researches have devoted great efforts on the task of rain removal for improving the image visibility. However, there is very few exploration about the quality assessment of de-rained image, even it is crucial for accurately measuring the performance of various de-raining algorithms. In this paper, we first create a de-raining quality assessment (DQA) database that collects 206 authentic rain images and their de-rained versions produced by 6 representative single image rain removal algorithms. Then, a subjective study is conducted on our DQA database, which collects the subject-rated scores of all de-rained images. To quantitatively measure the quality of de-rained image with non-uniform artifacts, we propose a bi-directional feature embedding network (B-FEN) which integrates the features of global perception and local difference together. Experiments confirm that the proposed method significantly outperforms many existing universal blind image quality assessment models. To help the research towards perceptually preferred de-raining algorithm, we will publicly release our DQA database and B-FEN source code on https://github.com/wqb-uestc.
Tasks	Blind Image Quality Assessment, Image Quality Assessment, Rain Removal
Published	2019-09-26
URL	https://arxiv.org/abs/1909.11983v3
PDF	https://arxiv.org/pdf/1909.11983v3.pdf
PWC	https://paperswithcode.com/paper/subjective-and-objective-de-raining-quality
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Framework

Pretrain Soft Q-Learning with Imperfect Demonstrations


Title	Pretrain Soft Q-Learning with Imperfect Demonstrations
Authors	Xiaoqin Zhang, Yunfei Li, Huimin Ma, Xiong Luo
Abstract	Pretraining reinforcement learning methods with demonstrations has been an important concept in the study of reinforcement learning since a large amount of computing power is spent on online simulations with existing reinforcement learning algorithms. Pretraining reinforcement learning remains a significant challenge in exploiting expert demonstrations whilst keeping exploration potentials, especially for value based methods. In this paper, we propose a pretraining method for soft Q-learning. Our work is inspired by pretraining methods for actor-critic algorithms since soft Q-learning is a value based algorithm that is equivalent to policy gradient. The proposed method is based on $\gamma$-discounted biased policy evaluation with entropy regularization, which is also the updating target of soft Q-learning. Our method is evaluated on various tasks from Atari 2600. Experiments show that our method effectively learns from imperfect demonstrations, and outperforms other state-of-the-art methods that learn from expert demonstrations.
Tasks	Q-Learning
Published	2019-05-09
URL	https://arxiv.org/abs/1905.03501v1
PDF	https://arxiv.org/pdf/1905.03501v1.pdf
PWC	https://paperswithcode.com/paper/190503501
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Temporal Analysis of Reddit Networks via Role Embeddings


Title	Temporal Analysis of Reddit Networks via Role Embeddings
Authors	Siobhan Grayson, Derek Greene
Abstract	Inspired by diachronic word analysis from the field of natural language processing, we propose an approach for uncovering temporal insights regarding user roles from social networks using graph embedding methods. Specifically, we apply the role embedding algorithm, struc2vec, to a collection of social networks exhibiting either “loyal” or “vagrant” characteristics derived from the popular online social news aggregation website Reddit. For each subreddit, we extract nine months of data and create network role embeddings on consecutive time windows. We are then able to compare and contrast how user roles change over time by aligning the resulting temporal embeddings spaces. In particular, we analyse temporal role embeddings from an individual and a community-level perspective for both loyal and vagrant communities present on Reddit.
Tasks	Graph Embedding, Role Embedding
Published	2019-08-14
URL	https://arxiv.org/abs/1908.05192v1
PDF	https://arxiv.org/pdf/1908.05192v1.pdf
PWC	https://paperswithcode.com/paper/temporal-analysis-of-reddit-networks-via-role
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When Labelled Data Hurts: Deep Semi-Supervised Classification with the Graph 1-Laplacian


Title	When Labelled Data Hurts: Deep Semi-Supervised Classification with the Graph 1-Laplacian
Authors	Angelica I. Aviles-Rivero, Nicolas Papadakis, Ruoteng Li, Samar M Alsaleh, Robby T Tan, Carola-Bibiane Schonlieb
Abstract	We consider the task of classifying when a significantly reduced amount of labelled data is available. This problem is of a great interest, in several real-world problems, as obtaining large amounts of labelled data is expensive and time consuming. We present a novel semi-supervised framework for multi-class classification that is based on the non-smooth $\ell_1$ norm of the normalised graph 1-Laplacian. Our transductive framework is framed under a novel functional with carefully selected class priors - that enforces a sufficiently smooth solution and strengthens the intrinsic relation between the labelled and unlabelled data. We provide theoretical results of our new optimisation model and show its connections with deep learning for handling large-scale datasets. We demonstrate through extensive experimental results on large datasets - CIFAR-10, CIFAR-100 and ChestX-Ray14 - that our method outperforms classic methods and readily competes with recent deep-learning approaches.
Tasks
Published	2019-06-20
URL	https://arxiv.org/abs/1906.08635v3
PDF	https://arxiv.org/pdf/1906.08635v3.pdf
PWC	https://paperswithcode.com/paper/beyond-supervised-classification-extreme
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Knowledge Distillation via Route Constrained Optimization


Title	Knowledge Distillation via Route Constrained Optimization
Authors	Xiao Jin, Baoyun Peng, Yichao Wu, Yu Liu, Jiaheng Liu, Ding Liang, Xiaolin Hu
Abstract	Distillation-based learning boosts the performance of the miniaturized neural network based on the hypothesis that the representation of a teacher model can be used as structured and relatively weak supervision, and thus would be easily learned by a miniaturized model. However, we find that the representation of a converged heavy model is still a strong constraint for training a small student model, which leads to a high lower bound of congruence loss. In this work, inspired by curriculum learning we consider the knowledge distillation from the perspective of curriculum learning by routing. Instead of supervising the student model with a converged teacher model, we supervised it with some anchor points selected from the route in parameter space that the teacher model passed by, as we called route constrained optimization (RCO). We experimentally demonstrate this simple operation greatly reduces the lower bound of congruence loss for knowledge distillation, hint and mimicking learning. On close-set classification tasks like CIFAR100 and ImageNet, RCO improves knowledge distillation by 2.14% and 1.5% respectively. For the sake of evaluating the generalization, we also test RCO on the open-set face recognition task MegaFace.
Tasks	Face Recognition
Published	2019-04-19
URL	http://arxiv.org/abs/1904.09149v1
PDF	http://arxiv.org/pdf/1904.09149v1.pdf
PWC	https://paperswithcode.com/paper/knowledge-distillation-via-route-constrained
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A Fast Content-Based Image Retrieval Method Using Deep Visual Features


Title	A Fast Content-Based Image Retrieval Method Using Deep Visual Features
Authors	Hiroki Tanioka
Abstract	Fast and scalable Content-Based Image Retrieval using visual features is required for document analysis, Medical image analysis, etc. in the present age. Convolutional Neural Network (CNN) activations as features achieved their outstanding performance in this area. Deep Convolutional representations using the softmax function in the output layer are also ones among visual features. However, almost all the image retrieval systems hold their index of visual features on main memory in order to high responsiveness, limiting their applicability for big data applications. In this paper, we propose a fast calculation method of cosine similarity with L2 norm indexed in advance on Elasticsearch. We evaluate our approach with ImageNet Dataset and VGG-16 pre-trained model. The evaluation results show the effectiveness and efficiency of our proposed method.
Tasks	Content-Based Image Retrieval, Image Retrieval
Published	2019-08-05
URL	https://arxiv.org/abs/1908.01505v1
PDF	https://arxiv.org/pdf/1908.01505v1.pdf
PWC	https://paperswithcode.com/paper/a-fast-content-based-image-retrieval-method
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Information Retrieval and Its Sister Disciplines


Title	Information Retrieval and Its Sister Disciplines
Authors	Grace Hui Yang
Abstract	This article presents a summary graph to show the relationships between Information Retrieval (IR) and other related disciplines. The figure tells the key differences between them and the conditions under which one would transition into another.
Tasks	Information Retrieval
Published	2019-12-05
URL	https://arxiv.org/abs/1912.02346v1
PDF	https://arxiv.org/pdf/1912.02346v1.pdf
PWC	https://paperswithcode.com/paper/information-retrieval-and-its-sister
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Tensor Oriented No-Reference Light Field Image Quality Assessment


Title	Tensor Oriented No-Reference Light Field Image Quality Assessment
Authors	Wei Zhou, Likun Shi, Zhibo Chen
Abstract	Light field image (LFI) quality assessment is becoming more and more important, which helps to better guide the acquisition, processing and application of immersive media. However, due to the inherent high dimensional characteristics of LFI, the LFI quality assessment turns into a multi-dimensional problem that requires consideration of the quality degradation in both spatial and angular dimensions. Therefore, we propose a novel Tensor oriented No-reference Light Field image Quality evaluator (Tensor-NLFQ) based on tensor theory. Specifically, since the LFI is regarded as a low-rank 4D tensor, the principle components of four oriented sub-aperture view stacks are obtained via Tucker decomposition. Then, the Principal Component Spatial Characteristic (PCSC) is designed to measure the spatial-dimensional quality of LFI considering its global naturalness and local frequency properties. Finally, the Tensor Angular Variation Index (TAVI) is proposed to measure angular consistency quality by analyzing the structural similarity distribution between the first principal component and each view in the view stack. Extensive experimental results on four publicly available LFI quality databases demonstrate that the proposed Tensor-NLFQ model outperforms state-of-the-art 2D, 3D, multi-view, and LFI quality assessment algorithms.
Tasks	Image Quality Assessment
Published	2019-09-05
URL	https://arxiv.org/abs/1909.02358v1
PDF	https://arxiv.org/pdf/1909.02358v1.pdf
PWC	https://paperswithcode.com/paper/tensor-oriented-no-reference-light-field
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Spurious Local Minima of Shallow ReLU Networks Conform with the Symmetry of the Target Model


Title	Spurious Local Minima of Shallow ReLU Networks Conform with the Symmetry of the Target Model
Authors	Yossi Arjevani, Michael Field
Abstract	We consider the optimization problem associated with fitting two-layer ReLU networks with respect to the squared loss, where labels are assumed to be generated by a target network. Focusing first on standard Gaussian inputs, we show that the structure of spurious local minima detected by stochastic gradient descent (SGD) is, in a well-defined sense, the \emph{least loss of symmetry} with respect to the target weights. A closer look at the analysis indicates then that this principle of least symmetry breaking may apply to a broader range of settings. Motivated by this, we conduct a series of experiments which corroborate this hypothesis for different classes of non-isotropic non-product distributions, smooth activation functions and networks with a few layers.
Tasks
Published	2019-12-26
URL	https://arxiv.org/abs/1912.11939v1
PDF	https://arxiv.org/pdf/1912.11939v1.pdf
PWC	https://paperswithcode.com/paper/spurious-local-minima-of-shallow-relu
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Framework

Cold-start Playlist Recommendation with Multitask Learning


Title	Cold-start Playlist Recommendation with Multitask Learning
Authors	Dawei Chen, Cheng Soon Ong, Aditya Krishna Menon
Abstract	Playlist recommendation involves producing a set of songs that a user might enjoy. We investigate this problem in three cold-start scenarios: (i) cold playlists, where we recommend songs to form new personalised playlists for an existing user; (ii) cold users, where we recommend songs to form new playlists for a new user; and (iii) cold songs, where we recommend newly released songs to extend users’ existing playlists. We propose a flexible multitask learning method to deal with all three settings. The method learns from user-curated playlists, and encourages songs in a playlist to be ranked higher than those that are not by minimising a bipartite ranking loss. Inspired by an equivalence between bipartite ranking and binary classification, we show how one can efficiently approximate an optimal solution of the multitask learning objective by minimising a classification loss. Empirical results on two real playlist datasets show the proposed approach has good performance for cold-start playlist recommendation.
Tasks
Published	2019-01-18
URL	http://arxiv.org/abs/1901.06125v1
PDF	http://arxiv.org/pdf/1901.06125v1.pdf
PWC	https://paperswithcode.com/paper/cold-start-playlist-recommendation-with
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Domain Adaptive Person Re-Identification via Camera Style Generation and Label Propagation


Title	Domain Adaptive Person Re-Identification via Camera Style Generation and Label Propagation
Authors	Chuan-Xian Ren, Bo-Hua Liang, Zhen Lei
Abstract	Unsupervised domain adaptation in person re-identification resorts to labeled source data to promote the model training on target domain, facing the dilemmas caused by large domain shift and large camera variations. The non-overlapping labels challenge that source domain and target domain have entirely different persons further increases the re-identification difficulty. In this paper, we propose a novel algorithm to narrow such domain gaps. We derive a camera style adaptation framework to learn the style-based mappings between different camera views, from the target domain to the source domain, and then we can transfer the identity-based distribution from the source domain to the target domain on the camera level. To overcome the non-overlapping labels challenge and guide the person re-identification model to narrow the gap further, an efficient and effective soft-labeling method is proposed to mine the intrinsic local structure of the target domain through building the connection between GAN-translated source domain and the target domain. Experiment results conducted on real benchmark datasets indicate that our method gets state-of-the-art results.
Tasks	Domain Adaptation, Person Re-Identification, Unsupervised Domain Adaptation
Published	2019-05-14
URL	https://arxiv.org/abs/1905.05382v1
PDF	https://arxiv.org/pdf/1905.05382v1.pdf
PWC	https://paperswithcode.com/paper/domain-adaptive-person-re-identification-via
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Principal Component Analysis Using Structural Similarity Index for Images


Title	Principal Component Analysis Using Structural Similarity Index for Images
Authors	Benyamin Ghojogh, Fakhri Karray, Mark Crowley
Abstract	Despite the advances of deep learning in specific tasks using images, the principled assessment of image fidelity and similarity is still a critical ability to develop. As it has been shown that Mean Squared Error (MSE) is insufficient for this task, other measures have been developed with one of the most effective being Structural Similarity Index (SSIM). Such measures can be used for subspace learning but existing methods in machine learning, such as Principal Component Analysis (PCA), are based on Euclidean distance or MSE and thus cannot properly capture the structural features of images. In this paper, we define an image structure subspace which discriminates different types of image distortions. We propose Image Structural Component Analysis (ISCA) and also kernel ISCA by using SSIM, rather than Euclidean distance, in the formulation of PCA. This paper provides a bridge between image quality assessment and manifold learning opening a broad new area for future research.
Tasks	Image Quality Assessment
Published	2019-08-25
URL	https://arxiv.org/abs/1908.09287v1
PDF	https://arxiv.org/pdf/1908.09287v1.pdf
PWC	https://paperswithcode.com/paper/principal-component-analysis-using-structural
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CAWA: An Attention-Network for Credit Attribution


Title	CAWA: An Attention-Network for Credit Attribution
Authors	Saurav Manchanda, George Karypis
Abstract	Credit attribution is the task of associating individual parts in a document with their most appropriate class labels. It is an important task with applications to information retrieval and text summarization. When labeled training data is available, traditional approaches for sequence tagging can be used for credit attribution. However, generating such labeled datasets is expensive and time-consuming. In this paper, we present “Credit Attribution With Attention (CAWA)", a neural-network-based approach, that instead of using sentence-level labeled data, uses the set of class labels that are associated with an entire document as a source of distant-supervision. CAWA combines an attention mechanism with a multilabel classifier into an end-to-end learning framework to perform credit attribution. CAWA labels the individual sentences from the input document using the resultant attention-weights. CAWA improves upon the state-of-the-art credit attribution approach by not constraining a sentence to belong to just one class, but modeling each sentence as a distribution over all classes, leading to better modeling of semantically-similar classes. Experiments on the credit attribution task on a variety of datasets show that the sentence class labels generated by CAWA outperform the competing approaches. Additionally, on the multilabel text classification task, CAWA performs better than the competing credit attribution approaches.
Tasks	Information Retrieval, Text Classification, Text Summarization
Published	2019-11-26
URL	https://arxiv.org/abs/1911.11358v1
PDF	https://arxiv.org/pdf/1911.11358v1.pdf
PWC	https://paperswithcode.com/paper/cawa-an-attention-network-for-credit
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Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing


Title	Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing
Authors	Milan Straka, Jana Straková, Jan Hajič
Abstract	We present an extensive evaluation of three recently proposed methods for contextualized embeddings on 89 corpora in 54 languages of the Universal Dependencies 2.3 in three tasks: POS tagging, lemmatization, and dependency parsing. Employing the BERT, Flair and ELMo as pretrained embedding inputs in a strong baseline of UDPipe 2.0, one of the best-performing systems of the CoNLL 2018 Shared Task and an overall winner of the EPE 2018, we present a one-to-one comparison of the three contextualized word embedding methods, as well as a comparison with word2vec-like pretrained embeddings and with end-to-end character-level word embeddings. We report state-of-the-art results in all three tasks as compared to results on UD 2.2 in the CoNLL 2018 Shared Task.
Tasks	Dependency Parsing, Lemmatization, Word Embeddings
Published	2019-08-20
URL	https://arxiv.org/abs/1908.07448v1
PDF	https://arxiv.org/pdf/1908.07448v1.pdf
PWC	https://paperswithcode.com/paper/evaluating-contextualized-embeddings-on-54
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Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection


Title	Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection
Authors	Xiaoxiao Li, Nicha C. Dvornek, Juntang Zhuang, Pamela Ventola, James Duncan
Abstract	Significant progress has been made using fMRI to characterize the brain changes that occur in ASD, a complex neuro-developmental disorder. However, due to the high dimensionality and low signal-to-noise ratio of fMRI, embedding informative and robust brain regional fMRI representations for both graph-level classification and region-level functional difference detection tasks between ASD and healthy control (HC) groups is difficult. Here, we model the whole brain fMRI as a graph, which preserves geometrical and temporal information and use a Graph Neural Network (GNN) to learn from the graph-structured fMRI data. We investigate the potential of including mutual information (MI) loss (Infomax), which is an unsupervised term encouraging large MI of each nodal representation and its corresponding graph-level summarized representation to learn a better graph embedding. Specifically, this work developed a pipeline including a GNN encoder, a classifier and a discriminator, which forces the encoded nodal representations to both benefit classification and reveal the common nodal patterns in a graph. We simultaneously optimize graph-level classification loss and Infomax. We demonstrated that Infomax graph embedding improves classification performance as a regularization term. Furthermore, we found separable nodal representations of ASD and HC groups in prefrontal cortex, cingulate cortex, visual regions, and other social, emotional and execution related brain regions. In contrast with GNN with classification loss only, the proposed pipeline can facilitate training more robust ASD classification models. Moreover, the separable nodal representations can detect the functional differences between the two groups and contribute to revealing new ASD biomarkers.
Tasks	Graph Embedding
Published	2019-08-09
URL	https://arxiv.org/abs/1908.04769v2
PDF	https://arxiv.org/pdf/1908.04769v2.pdf
PWC	https://paperswithcode.com/paper/graph-embedding-using-infomax-for-asd
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