pwc-archive/papers-with-abstracts huggingface dataset:
Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos
Authors: Mahsa Ehsanpour, Alireza Abedin, Fatemeh Saleh, Javen Shi, Ian Reid, Hamid Rezatofighi
Date: 2020-07-06 00:00:00
Proceeding: ECCV 2020 8
Tasks: Activity Recognition, Group Activity Recognition

The state-of-the art solutions for human activity understanding from a video stream formulate the task as a spatio-temporal problem which requires joint localization of all individuals in the scene and classification of their actions or group activity over time. Who is interacting with whom, e.g. not everyone in a queue is interacting with each other, is often not predicted. There are scenarios where people are best to be split into sub-groups, which we call social groups, and each social group may be engaged in a different social activity. In this paper, we solve the problem of simultaneously grouping people by their social interactions, predicting their individual actions and the social activity of each social group, which we call the social task. Our main contributions are: i) we propose an end-to-end trainable framework for the social task; ii) our proposed method also sets the state-of-the-art results on two widely adopted benchmarks for the traditional group activity recognition task (assuming individuals of the scene form a single group and predicting a single group activity label for the scene); iii) we introduce new annotations on an existing group activity dataset, re-purposing it for the social task.
Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
Authors: Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro, Enrico Magli
Date: 2020-07-04 00:00:00
Tasks: Denoising, Sar Image Despeckling

Information extraction from synthetic aperture radar (SAR) images is heavily impaired by speckle noise, hence despeckling is a crucial preliminary step in scene analysis algorithms. The recent success of deep learning envisions a new generation of despeckling techniques that could outperform classical model-based methods. However, current deep learning approaches to despeckling require supervision for training, whereas clean SAR images are impossible to obtain. In the literature, this issue is tackled by resorting to either synthetically speckled optical images, which exhibit different properties with respect to true SAR images, or multi-temporal SAR images, which are difficult to acquire or fuse accurately. In this paper, inspired by recent works on blind-spot denoising networks, we propose a self-supervised Bayesian despeckling method. The proposed method is trained employing only noisy SAR images and can therefore learn features of real SAR images rather than synthetic data. Experiments show that the performance of the proposed approach is very close to the supervised training approach on synthetic data and superior on real data in both quantitative and visual assessments.
End-to-end Learning of a Fisher Vector Encoding for Part Features in Fine-grained Recognition
Authors: Dimitri Korsch, Paul Bodesheim, Joachim Denzler
Date: 2020-07-04 00:00:00
Tasks: Fine-Grained Image Classification

Part-based approaches for fine-grained recognition do not show the expected performance gain over global methods, although explicitly focusing on small details that are relevant for distinguishing highly similar classes. We assume that part-based methods suffer from a missing representation of local features, which is invariant to the order of parts and can handle a varying number of visible parts appropriately. The order of parts is artificial and often only given by ground-truth annotations, whereas viewpoint variations and occlusions result in not observable parts. Therefore, we propose integrating a Fisher vector encoding of part features into convolutional neural networks. The parameters for this encoding are estimated by an online EM algorithm jointly with those of the neural network and are more precise than the estimates of previous works. Our approach improves state-of-the-art accuracies for three bird species classification datasets.
Learning Graph-Convolutional Representations for Point Cloud Denoising
Authors: Francesca Pistilli, Giulia Fracastoro, Diego Valsesia, Enrico Magli
Date: 2020-07-06 00:00:00
Proceeding: ECCV 2020 8
Tasks: Denoising

Point clouds are an increasingly relevant data type but they are often corrupted by noise. We propose a deep neural network based on graph-convolutional layers that can elegantly deal with the permutation-invariance problem encountered by learning-based point cloud processing methods. The network is fully-convolutional and can build complex hierarchies of features by dynamically constructing neighborhood graphs from similarity among the high-dimensional feature representations of the points. When coupled with a loss promoting proximity to the ideal surface, the proposed approach significantly outperforms state-of-the-art methods on a variety of metrics. In particular, it is able to improve in terms of Chamfer measure and of quality of the surface normals that can be estimated from the denoised data. We also show that it is especially robust both at high noise levels and in presence of structured noise such as the one encountered in real LiDAR scans.
Relevance Transformer: Generating Concise Code Snippets with Relevance Feedback
Authors: Carlos Gemmell, Federico Rossetto, Jeffrey Dalton
Date: 2020-07-06 00:00:00
Tasks: Code Generation, Decoder, Diversity, Machine Translation, Retrieval, Translation

Tools capable of automatic code generation have the potential to augment programmer's capabilities. While straightforward code retrieval is incorporated into many IDEs, an emerging area is explicit code generation. Code generation is currently approached as a Machine Translation task, with Recurrent Neural Network (RNN) based encoder-decoder architectures trained on code-description pairs. In this work we introduce and study modern Transformer architectures for this task. We further propose a new model called the Relevance Transformer that incorporates external knowledge using pseudo-relevance feedback. The Relevance Transformer biases the decoding process to be similar to existing retrieved code while enforcing diversity. We perform experiments on multiple standard benchmark datasets for code generation including Django, Hearthstone, and CoNaLa. The results show improvements over state-of-the-art methods based on BLEU evaluation. The Relevance Transformer model shows the potential of Transformer-based architectures for code generation and introduces a method of incorporating pseudo-relevance feedback during inference.
Pseudo-Rehearsal for Continual Learning with Normalizing Flows
Authors: Jary Pomponi, Simone Scardapane, Aurelio Uncini
Date: 2020-07-05 00:00:00
Proceeding: ICML Workshop LifelongML 2020 7
Tasks: Continual Learning

Catastrophic forgetting (CF) happens whenever a neural network overwrites past knowledge while being trained on new tasks. Common techniques to handle CF include regularization of the weights (using, e.g., their importance on past tasks), and rehearsal strategies, where the network is constantly re-trained on past data. Generative models have also been applied for the latter, in order to have endless sources of data. In this paper, we propose a novel method that combines the strengths of regularization and generative-based rehearsal approaches. Our generative model consists of a normalizing flow (NF), a probabilistic and invertible neural network, trained on the internal embeddings of the network. By keeping a single NF conditioned on the task, we show that our memory overhead remains constant. In addition, exploiting the invertibility of the NF, we propose a simple approach to regularize the network's embeddings with respect to past tasks. We show that our method performs favorably with respect to state-of-the-art approaches in the literature, with bounded computational power and memory overheads.
Discount Factor as a Regularizer in Reinforcement Learning
Authors: Ron Amit, Ron Meir, Kamil Ciosek
Date: 2020-07-04 00:00:00
Proceeding: ICML 2020 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Specifying a Reinforcement Learning (RL) task involves choosing a suitable planning horizon, which is typically modeled by a discount factor. It is known that applying RL algorithms with a lower discount factor can act as a regularizer, improving performance in the limited data regime. Yet the exact nature of this regularizer has not been investigated. In this work, we fill in this gap. For several Temporal-Difference (TD) learning methods, we show an explicit equivalence between using a reduced discount factor and adding an explicit regularization term to the algorithm's loss. Motivated by the equivalence, we empirically study this technique compared to standard $L_2$ regularization by extensive experiments in discrete and continuous domains, using tabular and functional representations. Our experiments suggest the regularization effectiveness is strongly related to properties of the available data, such as size, distribution, and mixing rate.
Anatomical Data Augmentation via Fluid-based Image Registration
Authors: Zhengyang Shen, Zhenlin Xu, Sahin Olut, Marc Niethammer
Date: 2020-07-05 00:00:00
Tasks: Data Augmentation, Image Augmentation, Image Registration, Image Segmentation, Medical Image Analysis, One-Shot Learning, Segmentation, Semantic Segmentation

We introduce a fluid-based image augmentation method for medical image analysis. In contrast to existing methods, our framework generates anatomically meaningful images via interpolation from the geodesic subspace underlying given samples. Our approach consists of three steps: 1) given a source image and a set of target images, we construct a geodesic subspace using the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model; 2) we sample transformations from the resulting geodesic subspace; 3) we obtain deformed images and segmentations via interpolation. Experiments on brain (LPBA) and knee (OAI) data illustrate the performance of our approach on two tasks: 1) data augmentation during training and testing for image segmentation; 2) one-shot learning for single atlas image segmentation. We demonstrate that our approach generates anatomically meaningful data and improves performance on these tasks over competing approaches. Code is available at https://github.com/uncbiag/easyreg.
Integrating Distributed Architectures in Highly Modular RL Libraries
Authors: Albert Bou, Sebastian Dittert, Gianni de Fabritiis
Date: 2020-07-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Advancing reinforcement learning (RL) requires tools that are flexible enough to easily prototype new methods while avoiding impractically slow experimental turnaround times. To match the first requirement, the most popular RL libraries advocate for highly modular agent composability, which facilitates experimentation and development. To solve challenging environments within reasonable time frames, scaling RL to large sampling and computing resources has proved a successful strategy. However, this capability has been so far difficult to combine with modularity. In this work, we explore design choices to allow agent composability both at a local and distributed level of execution. We propose a versatile approach that allows the definition of RL agents at different scales through independent reusable components. We demonstrate experimentally that our design choices allow us to reproduce classical benchmarks, explore multiple distributed architectures, and solve novel and complex environments while giving full control to the user in the agent definition and training scheme definition. We believe this work can provide useful insights to the next generation of RL libraries.
Automatic semantic segmentation for prediction of tuberculosis using lens-free microscopy images
Authors: Dennis Núñez-Fernández, Lamberto Ballan, Gabriel Jiménez-Avalos, Jorge Coronel, Mirko Zimic
Date: 2020-07-06 00:00:00
Tasks: Segmentation, Semantic Segmentation

Tuberculosis (TB), caused by a germ called Mycobacterium tuberculosis, is one of the most serious public health problems in Peru and the world. The development of this project seeks to facilitate and automate the diagnosis of tuberculosis by the MODS method and using lens-free microscopy, due they are easier to calibrate and easier to use (by untrained personnel) in comparison with lens microscopy. Thus, we employ a U-Net network in our collected dataset to perform the automatic segmentation of the TB cords in order to predict tuberculosis. Our initial results show promising evidence for automatic segmentation of TB cords.
Using Capsule Neural Network to predict Tuberculosis in lens-free microscopic images
Authors: Dennis Núñez-Fernández, Lamberto Ballan, Gabriel Jiménez-Avalos, Jorge Coronel, Mirko Zimic
Date: 2020-07-05 00:00:00

Tuberculosis, caused by a bacteria called Mycobacterium tuberculosis, is one of the most serious public health problems worldwide. This work seeks to facilitate and automate the prediction of tuberculosis by the MODS method and using lens-free microscopy, which is easy to use by untrained personnel. We employ the CapsNet architecture in our collected dataset and show that it has a better accuracy than traditional CNN architectures.
Lale: Consistent Automated Machine Learning
Authors: Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar
Date: 2020-07-04 00:00:00
Tasks: BIG-bench Machine Learning

Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies. Unfortunately, the syntax for automated machine learning tools is inconsistent with manual machine learning, with each other, and with error checks. Furthermore, few tools support advanced features such as topology search or higher-order operators. This paper introduces Lale, a library of high-level Python interfaces that simplifies and unifies automated machine learning in a consistent way.
Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation
Authors: Jiaxing Huang, Shijian Lu, Dayan Guan, Xiaobing Zhang
Date: 2020-07-05 00:00:00
Proceeding: ECCV 2020 8
Tasks: Domain Adaptation, Relation, Segmentation, Semantic Segmentation, Unsupervised Domain Adaptation

Recent advances in unsupervised domain adaptation for semantic segmentation have shown great potentials to relieve the demand of expensive per-pixel annotations. However, most existing works address the domain discrepancy by aligning the data distributions of two domains at a global image level whereas the local consistencies are largely neglected. This paper presents an innovative local contextual-relation consistent domain adaptation (CrCDA) technique that aims to achieve local-level consistencies during the global-level alignment. The idea is to take a closer look at region-wise feature representations and align them for local-level consistencies. Specifically, CrCDA learns and enforces the prototypical local contextual-relations explicitly in the feature space of a labelled source domain while transferring them to an unlabelled target domain via backpropagation-based adversarial learning. An adaptive entropy max-min adversarial learning scheme is designed to optimally align these hundreds of local contextual-relations across domain without requiring discriminator or extra computation overhead. The proposed CrCDA has been evaluated extensively over two challenging domain adaptive segmentation tasks (e.g., GTA5 to Cityscapes and SYNTHIA to Cityscapes), and experiments demonstrate its superior segmentation performance as compared with state-of-the-art methods.
Choosing a sampling frequency for ECG QRS detection using convolutional networks
Authors: Ahsan Habib, Chandan Karmakar, John Yearwood
Date: 2020-07-04 00:00:00
Tasks: ECG QRS Detection

Automated QRS detection methods depend on the ECG data which is sampled at a certain frequency, irrespective of filter-based traditional methods or convolutional network (CNN) based deep learning methods. These methods require a selection of the sampling frequency at which they operate in the very first place. While working with data from two different datasets, which are sampled at different frequencies, often, data from both the datasets may need to resample at a common target frequency, which may be the frequency of either of the datasets or could be a different one. However, choosing data sampled at a certain frequency may have an impact on the model's generalisation capacity, and complexity. There exist some studies that investigate the effects of ECG sample frequencies on traditional filter-based methods, however, an extensive study of the effect of ECG sample frequency on deep learning-based models (convolutional networks), exploring their generalisability and complexity is yet to be explored. This experimental research investigates the impact of six different sample frequencies (50, 100, 250, 500, 1000, and 2000Hz) on four different convolutional network-based models' generalisability and complexity in order to form a basis to decide on an appropriate sample frequency for the QRS detection task for a particular performance requirement. Intra-database tests report an accuracy improvement no more than approximately 0.6\% from 100Hz to 250Hz and the shorter interquartile range for those two frequencies for all CNN-based models. The findings reveal that convolutional network-based deep learning models are capable of scoring higher levels of detection accuracies on ECG signals sampled at frequencies as low as 100Hz or 250Hz while maintaining lower model complexity (number of trainable parameters and training time).
Searching Scientific Literature for Answers on COVID-19 Questions
Authors: Vincent Nguyen, Maciek Rybinski, Sarvnaz Karimi, Zhenchang Xing
Date: 2020-07-06 00:00:00
Tasks: Retrieval

Finding answers related to a pandemic of a novel disease raises new challenges for information seeking and retrieval, as the new information becomes available gradually. TREC COVID search track aims to assist in creating search tools to aid scientists, clinicians, policy makers and others with similar information needs in finding reliable answers from the scientific literature. We experiment with different ranking algorithms as part of our participation in this challenge. We propose a novel method for neural retrieval, and demonstrate its effectiveness on the TREC COVID search.
GanglionNet: Objectively Assess the Density and Distribution of Ganglion Cells With NABLA-N Network
Authors: Md Zahangir Alom, Raj P. Kapur, TJ Browen, Vijayan K. Asari
Date: 2020-07-05 00:00:00
Tasks: Cell Detection, Management

Hirschsprungs disease (HD) is a birth defect which is diagnosed and managed by multiple medical specialties such as pediatric gastroenterology, surgery, radiology, and pathology. HD is characterized by absence of ganglion cells in the distal intestinal tract with a gradual normalization of ganglion cell numbers in adjacent upstream bowel, termed as the transition zone (TZ). Definitive surgical management to remove the abnormal bowel requires accurate assessment of ganglion cell density in histological sections from the TZ, which is difficult, time-consuming and prone to operator error. We present an automated method to detect and count immunostained ganglion cells using a new NABLA_N network based deep learning (DL) approach, called GanglionNet. The morphological image analysis methods are applied for refinement of the regions for counting of the cells and define ganglia regions (a set of ganglion cells) from the predicted masks. The proposed model is trained with single point annotated samples by the expert pathologist. The GanglionNet is tested on ten completely new High Power Field (HPF) images with dimension of 2560x1920 pixels and the outputs are compared against the manual counting results by the expert pathologist. The proposed method shows a robust 97.49% detection accuracy for ganglion cells, when compared to counts by the expert pathologist, which demonstrates the robustness of GanglionNet. The proposed DL based ganglion cell detection and counting method will simplify and standardize TZ diagnosis for HD patients.
Handling highly correlated genes in prediction analysis of genomic studies
Authors: Li Xing, Songwan Joun, Kurt Mackay, Mary Lesperance, Xuekui Zhang
Date: 2020-07-05 00:00:00
Tasks: feature selection, Prediction

Background: Selecting feature genes to predict phenotypes is one of the typical tasks in analyzing genomics data. Though many general-purpose algorithms were developed for prediction, dealing with highly correlated genes in the prediction model is still not well addressed. High correlation among genes introduces technical problems, such as multi-collinearity issues, leading to unreliable prediction models. Furthermore, when a causal gene (whose variants have an actual biological effect on a phenotype) is highly correlated with other genes, most algorithms select the feature gene from the correlated group in a purely data-driven manner. Since the correlation structure among genes could change substantially when condition changes, the prediction model based on not correctly selected feature genes is unreliable. Therefore, we aim to keep the causal biological signal in the prediction process and build a more robust prediction model. Method: We propose a grouping algorithm, which treats highly correlated genes as a group and uses their common pattern to represent the group's biological signal in feature selection. Our novel grouping algorithm can be integrated into existing prediction algorithms to enhance their prediction performance. Our proposed grouping method has two advantages. First, using the gene group's common patterns makes the prediction more robust and reliable under condition change. Second, it reports whole correlated gene groups as discovered biomarkers for prediction tasks, allowing researchers to conduct follow-up studies to identify causal genes within the identified groups. Result: Using real benchmark scRNA-seq datasets with simulated cell phenotypes, we demonstrate our novel method significantly outperforms standard models in both (1) prediction of cell phenotypes and (2) feature gene selection.
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Authors: Silviu Pitis, Harris Chan, Stephen Zhao, Bradly Stadie, Jimmy Ba
Date: 2020-07-06 00:00:00
Proceeding: ICML 2020 1
Tasks: Multi-Goal Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

What goals should a multi-goal reinforcement learning agent pursue during training in long-horizon tasks? When the desired (test time) goal distribution is too distant to offer a useful learning signal, we argue that the agent should not pursue unobtainable goals. Instead, it should set its own intrinsic goals that maximize the entropy of the historical achieved goal distribution. We propose to optimize this objective by having the agent pursue past achieved goals in sparsely explored areas of the goal space, which focuses exploration on the frontier of the achievable goal set. We show that our strategy achieves an order of magnitude better sample efficiency than the prior state of the art on long-horizon multi-goal tasks including maze navigation and block stacking.