February 2, 2020

3038 words 15 mins read

Paper Group AWR 33

Active Learning for Decision-Making from Imbalanced Observational Data. BIRL: Benchmark on Image Registration methods with Landmark validation. ANETAC: Arabic Named Entity Transliteration and Classification Dataset. Selecting the independent coordinates of manifolds with large aspect ratios. Seeing Under the Cover: A Physics Guided Learning Approac …

Active Learning for Decision-Making from Imbalanced Observational Data


Title	Active Learning for Decision-Making from Imbalanced Observational Data
Authors	Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski
Abstract	Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based decision-making in a task of deciding which action $a$ to take for a target unit after observing its covariates $\tilde{x}$ and predicted outcomes $\hat{p}(\tilde{y} \mid \tilde{x}, a)$. An example case is personalized medicine and the decision of which treatment to give to a patient. A common problem when learning these models from observational data is imbalance, that is, difference in treated/control covariate distributions, which is known to increase the upper bound of the expected ITE estimation error. We propose to assess the decision-making reliability by estimating the ITE model’s Type S error rate, which is the probability of the model inferring the sign of the treatment effect wrong. Furthermore, we use the estimated reliability as a criterion for active learning, in order to collect new (possibly expensive) observations, instead of making a forced choice based on unreliable predictions. We demonstrate the effectiveness of this decision-making aware active learning in two decision-making tasks: in simulated data with binary outcomes and in a medical dataset with synthetic and continuous treatment outcomes.
Tasks	Active Learning, Decision Making
Published	2019-04-10
URL	https://arxiv.org/abs/1904.05268v2
PDF	https://arxiv.org/pdf/1904.05268v2.pdf
PWC	https://paperswithcode.com/paper/active-learning-for-decision-making-from
Repo	https://github.com/IirisSundin/active-learning-for-decision-making
Framework	none

BIRL: Benchmark on Image Registration methods with Landmark validation


Title	BIRL: Benchmark on Image Registration methods with Landmark validation
Authors	Jiri Borovec
Abstract	This report presents a generic image registration benchmark with automatic evaluation using landmark annotations. The key features of the BIRL framework are: easily extendable, performance evaluation, parallel experimentation, simple visualisations, experiment’s time-out limit, resuming unfinished experiments. From the research practice, we identified and focused on these two main use-cases: (a) comparison of user’s (newly developed) method with some State-of-the-Art (SOTA) methods on a common dataset and (b) experimenting SOTA methods on user’s custom dataset (which should contain landmark annotation). Moreover, we present an integration of several standard image registration methods aiming at biomedical imaging into the BIRL framework. This report also contains experimental results of these SOTA methods on the CIMA dataset, which is a dataset of Whole Slice Imaging (WSI) from histology/pathology containing several multi-stain tissue samples from three tissue kinds. Source and results: https://borda.github.io/BIRL
Tasks	BIRL, Image Registration, Medical Image Registration
Published	2019-12-31
URL	https://arxiv.org/abs/1912.13452v2
PDF	https://arxiv.org/pdf/1912.13452v2.pdf
PWC	https://paperswithcode.com/paper/birl-benchmark-on-image-registration-methods
Repo	https://github.com/Borda/BIRL
Framework	none

ANETAC: Arabic Named Entity Transliteration and Classification Dataset


Title	ANETAC: Arabic Named Entity Transliteration and Classification Dataset
Authors	Mohamed Seghir Hadj Ameur, Farid Meziane, Ahmed Guessoum
Abstract	In this paper, we make freely accessible ANETAC our English-Arabic named entity transliteration and classification dataset that we built from freely available parallel translation corpora. The dataset contains 79,924 instances, each instance is a triplet (e, a, c), where e is the English named entity, a is its Arabic transliteration and c is its class that can be either a Person, a Location, or an Organization. The ANETAC dataset is mainly aimed for the researchers that are working on Arabic named entity transliteration, but it can also be used for named entity classification purposes.
Tasks	Transliteration
Published	2019-07-06
URL	https://arxiv.org/abs/1907.03110v1
PDF	https://arxiv.org/pdf/1907.03110v1.pdf
PWC	https://paperswithcode.com/paper/anetac-arabic-named-entity-transliteration
Repo	https://github.com/MohamedHadjAmeur/ANETAC
Framework	none

Selecting the independent coordinates of manifolds with large aspect ratios


Title	Selecting the independent coordinates of manifolds with large aspect ratios
Authors	Yu-Chia Chen, Marina Meilă
Abstract	Many manifold embedding algorithms fail apparently when the data manifold has a large aspect ratio (such as a long, thin strip). Here, we formulate success and failure in terms of finding a smooth embedding, showing also that the problem is pervasive and more complex than previously recognized. Mathematically, success is possible under very broad conditions, provided that embedding is done by carefully selected eigenfunctions of the Laplace-Beltrami operator $\Delta$. Hence, we propose a bicriterial Independent Eigencoordinate Selection (IES) algorithm that selects smooth embeddings with few eigenvectors. The algorithm is grounded in theory, has low computational overhead, and is successful on synthetic and large real data.
Tasks
Published	2019-07-02
URL	https://arxiv.org/abs/1907.01651v2
PDF	https://arxiv.org/pdf/1907.01651v2.pdf
PWC	https://paperswithcode.com/paper/selecting-the-independent-coordinates-of
Repo	https://github.com/alainchau/IES.jl
Framework	none

Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation


Title	Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation
Authors	Shuangjun Liu, Sarah Ostadabbas
Abstract	Human in-bed pose estimation has huge practical values in medical and healthcare applications yet still mainly relies on expensive pressure mapping (PM) solutions. In this paper, we introduce our novel physics inspired vision-based approach that addresses the challenging issues associated with the in-bed pose estimation problem including monitoring a fully covered person in complete darkness. We reformulated this problem using our proposed Under the Cover Imaging via Thermal Diffusion (UCITD) method to capture the high resolution pose information of the body even when it is fully covered by using a long wavelength IR technique. We proposed a physical hyperparameter concept through which we achieved high quality groundtruth pose labels in different modalities. A fully annotated in-bed pose dataset called Simultaneously-collected multimodal Lying Pose (SLP) is also formed/released with the same order of magnitude as most existing large-scale human pose datasets to support complex models’ training and evaluation. A network trained from scratch on it and tested on two diverse settings, one in a living room and the other in a hospital room showed pose estimation performance of 99.5% and 95.7% in PCK0.2 standard, respectively. Moreover, in a multi-factor comparison with a state-of-the art in-bed pose monitoring solution based on PM, our solution showed significant superiority in all practical aspects by being 60 times cheaper, 300 times smaller, while having higher pose recognition granularity and accuracy.
Tasks	Pose Estimation
Published	2019-07-03
URL	https://arxiv.org/abs/1907.02161v3
PDF	https://arxiv.org/pdf/1907.02161v3.pdf
PWC	https://paperswithcode.com/paper/seeing-under-the-cover-a-physics-guided
Repo	https://github.com/ostadabbas/Seeing-Under-the-Cover
Framework	pytorch

Robustness properties of Facebook’s ResNeXt WSL models


Title	Robustness properties of Facebook’s ResNeXt WSL models
Authors	A. Emin Orhan
Abstract	We investigate the robustness properties of ResNeXt class image recognition models trained with billion scale weakly supervised data (ResNeXt WSL models). These models, recently made public by Facebook AI, were trained with ~1B images from Instagram and fine-tuned on ImageNet. We show that these models display an unprecedented degree of robustness against common image corruptions and perturbations, as measured by the ImageNet-C and ImageNet-P benchmarks. They also achieve substantially improved accuracies on the recently introduced “natural adversarial examples” benchmark (ImageNet-A). The largest of the released models, in particular, achieves state-of-the-art results on ImageNet-C, ImageNet-P, and ImageNet-A by a large margin. The gains on ImageNet-C, ImageNet-P, and ImageNet-A far outpace the gains on ImageNet validation accuracy, suggesting the former as more useful benchmarks to measure further progress in image recognition. Remarkably, the ResNeXt WSL models even achieve a limited degree of adversarial robustness against state-of-the-art white-box attacks (10-step PGD attacks). However, in contrast to adversarially trained models, the robustness of the ResNeXt WSL models rapidly declines with the number of PGD steps, suggesting that these models do not achieve genuine adversarial robustness. Visualization of the learned features also confirms this conclusion. Finally, we show that although the ResNeXt WSL models are more shape-biased than comparable ImageNet-trained models in a shape-texture cue conflict experiment, they still remain much more texture-biased than humans, suggesting that they share some of the underlying characteristics of ImageNet-trained models that make this benchmark challenging.
Tasks
Published	2019-07-17
URL	https://arxiv.org/abs/1907.07640v5
PDF	https://arxiv.org/pdf/1907.07640v5.pdf
PWC	https://paperswithcode.com/paper/robustness-properties-of-facebooks-resnext
Repo	https://github.com/eminorhan/ood-benchmarks
Framework	pytorch

ImageNet-trained deep neural network exhibits illusion-like response to the Scintillating Grid


Title	ImageNet-trained deep neural network exhibits illusion-like response to the Scintillating Grid
Authors	Eric D. Sun, Ron Dekel
Abstract	Deep neural network (DNN) models for computer vision are now capable of human-level object recognition. Consequently, similarities in the performance and vulnerabilities of DNN and human vision are of great interest. Here we characterize the response of the VGG-19 DNN to images of the Scintillating Grid visual illusion, in which white dots are perceived to be partially black. We observed a significant deviation from the expected monotonic relation between VGG-19 representational dissimilarity and dot whiteness in the Scintillating Grid. That is, a linear increase in dot whiteness leads to a non-linear increase and then, remarkably, a decrease (non-monotonicity) in representational dissimilarity. In control images, mostly monotonic relations between representational dissimilarity and dot whiteness were observed. Furthermore, the dot whiteness level corresponding to the maximal representational dissimilarity (i.e. onset of non-monotonic dissimilarity) matched closely with that corresponding to the onset of illusion perception in human observers. As such, the non-monotonic response in the DNN is a potential model correlate for human illusion perception.
Tasks	Object Recognition
Published	2019-07-21
URL	https://arxiv.org/abs/1907.09019v2
PDF	https://arxiv.org/pdf/1907.09019v2.pdf
PWC	https://paperswithcode.com/paper/imagenet-trained-deep-neural-network-exhibits
Repo	https://github.com/sunericd/dnn-illusion
Framework	none

Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition


Title	Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition
Authors	Genta Indra Winata, Zhaojiang Lin, Jamin Shin, Zihan Liu, Pascale Fung
Abstract	In countries that speak multiple main languages, mixing up different languages within a conversation is commonly called code-switching. Previous works addressing this challenge mainly focused on word-level aspects such as word embeddings. However, in many cases, languages share common subwords, especially for closely related languages, but also for languages that are seemingly irrelevant. Therefore, we propose Hierarchical Meta-Embeddings (HME) that learn to combine multiple monolingual word-level and subword-level embeddings to create language-agnostic lexical representations. On the task of Named Entity Recognition for English-Spanish code-switching data, our model achieves the state-of-the-art performance in the multilingual settings. We also show that, in cross-lingual settings, our model not only leverages closely related languages, but also learns from languages with different roots. Finally, we show that combining different subunits are crucial for capturing code-switching entities.
Tasks	Named Entity Recognition, Word Embeddings
Published	2019-09-18
URL	https://arxiv.org/abs/1909.08504v1
PDF	https://arxiv.org/pdf/1909.08504v1.pdf
PWC	https://paperswithcode.com/paper/hierarchical-meta-embeddings-for-code
Repo	https://github.com/gentaiscool/meta-emb
Framework	pytorch

Learning Generalisable Omni-Scale Representations for Person Re-Identification


Title	Learning Generalisable Omni-Scale Representations for Person Re-Identification
Authors	Kaiyang Zhou, Yongxin Yang, Andrea Cavallaro, Tao Xiang
Abstract	An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation. In this paper, we develop novel CNN architectures to address both challenges. First, we present a re-ID CNN termed omni-scale network (OSNet) to learn features that not only capture different spatial scales but also encapsulate a synergistic combination of multiple scales, namely omni-scale features. The basic building block consists of multiple convolutional streams, each detecting features at a certain scale. For omni-scale feature learning, a unified aggregation gate is introduced to dynamically fuse multi-scale features with channel-wise weights. OSNet is lightweight as its building blocks comprise factorised convolutions. Second, to improve generalisable feature learning, we introduce instance normalisation (IN) layers into OSNet to cope with cross-dataset discrepancies. Further, to determine the optimal placements of these IN layers in the architecture, we formulate an efficient differentiable architecture search algorithm. Extensive experiments show that, in the conventional same-dataset setting, OSNet achieves state-of-the-art performance, despite being much smaller than existing re-ID models. In the more challenging yet practical cross-dataset setting, OSNet beats most recent unsupervised domain adaptation methods without requiring any target data for model adaptation. Our code and models are released at \texttt{https://github.com/KaiyangZhou/deep-person-reid}.
Tasks	Domain Adaptation, Person Re-Identification, Unsupervised Domain Adaptation
Published	2019-10-15
URL	https://arxiv.org/abs/1910.06827v3
PDF	https://arxiv.org/pdf/1910.06827v3.pdf
PWC	https://paperswithcode.com/paper/learning-generalisable-omni-scale
Repo	https://github.com/KaiyangZhou/deep-person-reid
Framework	pytorch

Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces


Title	Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces
Authors	Wen Zhang, Dongrui Wu
Abstract	Transfer learning makes use of data or knowledge in one problem to help solve a different, yet related, problem. It is particularly useful in brain-computer interfaces (BCIs), for coping with variations among different subjects and/or tasks. This paper considers offline unsupervised cross-subject electroencephalogram (EEG) classification, i.e., we have labeled EEG trials from one or more source subjects, but only unlabeled EEG trials from the target subject. We propose a novel manifold embedded knowledge transfer (MEKT) approach, which first aligns the covariance matrices of the EEG trials in the Riemannian manifold, extracts features in the tangent space, and then performs domain adaptation by minimizing the joint probability distribution shift between the source and the target domains, while preserving their geometric structures. MEKT can cope with one or multiple source domains, and can be computed efficiently. We also propose a domain transferability estimation (DTE) approach to identify the most beneficial source domains, in case there are a large number of source domains. Experiments on four EEG datasets from two different BCI paradigms demonstrated that MEKT outperformed several state-of-the-art transfer learning approaches, and DTE can reduce more than half of the computational cost when the number of source subjects is large, with little sacrifice of classification accuracy.
Tasks	Domain Adaptation, EEG, Transfer Learning
Published	2019-10-14
URL	https://arxiv.org/abs/1910.05878v2
PDF	https://arxiv.org/pdf/1910.05878v2.pdf
PWC	https://paperswithcode.com/paper/manifold-embedded-knowledge-transfer-for
Repo	https://github.com/chamwen/MEKT
Framework	none

MixMatch Domain Adaptaion: Prize-winning solution for both tracks of VisDA 2019 challenge


Title	MixMatch Domain Adaptaion: Prize-winning solution for both tracks of VisDA 2019 challenge
Authors	Danila Rukhovich, Danil Galeev
Abstract	We present a domain adaptation (DA) system that can be used in multi-source and semi-supervised settings. Using the proposed method we achieved 2nd place on multi-source track and 3rd place on semi-supervised track of the VisDA 2019 challenge (http://ai.bu.edu/visda-2019/). The source code of the method is available at https://github.com/filaPro/visda2019.
Tasks	Domain Adaptation
Published	2019-10-09
URL	https://arxiv.org/abs/1910.03903v1
PDF	https://arxiv.org/pdf/1910.03903v1.pdf
PWC	https://paperswithcode.com/paper/mixmatch-domain-adaptaion-prize-winning
Repo	https://github.com/filaPro/visda2019
Framework	tf

Multi-Source Domain Adaptation and Semi-Supervised Domain Adaptation with Focus on Visual Domain Adaptation Challenge 2019


Title	Multi-Source Domain Adaptation and Semi-Supervised Domain Adaptation with Focus on Visual Domain Adaptation Challenge 2019
Authors	Yingwei Pan, Yehao Li, Qi Cai, Yang Chen, Ting Yao
Abstract	This notebook paper presents an overview and comparative analysis of our systems designed for the following two tasks in Visual Domain Adaptation Challenge (VisDA-2019): multi-source domain adaptation and semi-supervised domain adaptation. Multi-Source Domain Adaptation: We investigate both pixel-level and feature-level adaptation for multi-source domain adaptation task, i.e., directly hallucinating labeled target sample via CycleGAN and learning domain-invariant feature representations through self-learning. Moreover, the mechanism of fusing features from different backbones is further studied to facilitate the learning of domain-invariant classifiers. Source code and pre-trained models are available at \url{https://github.com/Panda-Peter/visda2019-multisource}. Semi-Supervised Domain Adaptation: For this task, we adopt a standard self-learning framework to construct a classifier based on the labeled source and target data, and generate the pseudo labels for unlabeled target data. These target data with pseudo labels are then exploited to re-training the classifier in a following iteration. Furthermore, a prototype-based classification module is additionally utilized to strengthen the predictions. Source code and pre-trained models are available at \url{https://github.com/Panda-Peter/visda2019-semisupervised}.
Tasks	Domain Adaptation
Published	2019-10-08
URL	https://arxiv.org/abs/1910.03548v2
PDF	https://arxiv.org/pdf/1910.03548v2.pdf
PWC	https://paperswithcode.com/paper/multi-source-domain-adaptation-and-semi
Repo	https://github.com/Panda-Peter/visda2019-semisupervised
Framework	pytorch

ATL: Autonomous Knowledge Transfer from Many Streaming Processes


Title	ATL: Autonomous Knowledge Transfer from Many Streaming Processes
Authors	Mahardhika Pratama, Marcus de Carvalho, Renchunzi Xie, Edwin Lughofer, Jie Lu
Abstract	Transferring knowledge across many streaming processes remains an uncharted territory in the existing literature and features unique characteristics: no labelled instance of the target domain, covariate shift of source and target domain, different period of drifts in the source and target domains. Autonomous transfer learning (ATL) is proposed in this paper as a flexible deep learning approach for the online unsupervised transfer learning problem across many streaming processes. ATL offers an online domain adaptation strategy via the generative and discriminative phases coupled with the KL divergence based optimization strategy to produce a domain invariant network while putting forward an elastic network structure. It automatically evolves its network structure from scratch with/without the presence of ground truth to overcome independent concept drifts in the source and target domain. The rigorous numerical evaluation has been conducted along with a comparison against recently published works. ATL demonstrates improved performance while showing significantly faster training speed than its counterparts.
Tasks	Domain Adaptation, Transfer Learning
Published	2019-10-08
URL	https://arxiv.org/abs/1910.03434v2
PDF	https://arxiv.org/pdf/1910.03434v2.pdf
PWC	https://paperswithcode.com/paper/atl-autonomous-knowledge-transfer-from-many
Repo	https://github.com/Ivsucram/ATL_Python
Framework	pytorch

Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling


Title	Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling
Authors	Jiarui Qin, Kan Ren, Yuchen Fang, Weinan Zhang, Yong Yu
Abstract	Sequential recommendation task aims to predict user preference over items in the future given user historical behaviors. The order of user behaviors implies that there are resourceful sequential patterns embedded in the behavior history which reveal the underlying dynamics of user interests. Various sequential recommendation methods are proposed to model the dynamic user behaviors. However, most of the models only consider the user’s own behaviors and dynamics, while ignoring the collaborative relations among users and items, i.e., similar tastes of users or analogous properties of items. Without modeling collaborative relations, those methods suffer from the lack of recommendation diversity and thus may have worse performance. Worse still, most existing methods only consider the user-side sequence and ignore the temporal dynamics on the item side. To tackle the problems of the current sequential recommendation models, we propose Sequential Collaborative Recommender (SCoRe) which effectively mines high-order collaborative information using cross-neighbor relation modeling and, additionally utilizes both user-side and item-side historical sequences to better capture user and item dynamics. Experiments on three real-world yet large-scale datasets demonstrate the superiority of the proposed model over strong baselines.
Tasks
Published	2019-11-10
URL	https://arxiv.org/abs/1911.03883v1
PDF	https://arxiv.org/pdf/1911.03883v1.pdf
PWC	https://paperswithcode.com/paper/sequential-recommendation-with-dual-side
Repo	https://github.com/qinjr/SCoRe
Framework	tf

Semi-Supervised Variational Autoencoder for Survival Prediction


Title	Semi-Supervised Variational Autoencoder for Survival Prediction
Authors	Sveinn Pálsson, Stefano Cerri, Andrea Dittadi, Koen Van Leemput
Abstract	In this paper we propose a semi-supervised variational autoencoder for classification of overall survival groups from tumor segmentation masks. The model can use the output of any tumor segmentation algorithm, removing all assumptions on the scanning platform and the specific type of pulse sequences used, thereby increasing its generalization properties. Due to its semi-supervised nature, the method can learn to classify survival time by using a relatively small number of labeled subjects. We validate our model on the publicly available dataset from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2019.
Tasks	Brain Tumor Segmentation
Published	2019-10-10
URL	https://arxiv.org/abs/1910.04488v1
PDF	https://arxiv.org/pdf/1910.04488v1.pdf
PWC	https://paperswithcode.com/paper/semi-supervised-variational-autoencoder-for
Repo	https://github.com/sveinnpalsson/semivaebrats
Framework	pytorch