pwc-archive/papers-with-abstracts huggingface dataset:
Spectral Clustering with Smooth Tiny Clusters
Authors: Hengrui Wang, Yubo Zhang, Mingzhi Chen, Tong Yang
Date: 2020-09-10 00:00:00
Tasks: Clustering

Spectral clustering is one of the most prominent clustering approaches. The distance-based similarity is the most widely used method for spectral clustering. However, people have already noticed that this is not suitable for multi-scale data, as the distance varies a lot for clusters with different densities. State of the art(ROSC and CAST ) addresses this limitation by taking the reachability similarity of objects into account. However, we observe that in real-world scenarios, data in the same cluster tend to present in a smooth manner, and previous algorithms never take this into account. Based on this observation, we propose a novel clustering algorithm, which con-siders the smoothness of data for the first time. We first divide objects into a great many tiny clusters. Our key idea is to cluster tiny clusters, whose centers constitute smooth graphs. Theoretical analysis and experimental results show that our clustering algorithm significantly outperforms state of the art. Although in this paper, we singly focus on multi-scale situations, the idea of data smoothness can certainly be extended to any clustering algorithms
A Framework for Fairer Machine Learning in Organizations
Authors: Lily Morse, Mike H. M. Teodorescu, Yazeed Awwad, Gerald Kane
Date: 2020-09-10 00:00:00
Tasks: BIG-bench Machine Learning, Ethics, Fairness, Management

With the increase in adoption of machine learning tools by organizations risks of unfairness abound, especially when human decision processes in outcomes of socio-economic importance such as hiring, housing, lending, and admissions are automated. We reveal sources of unfair machine learning, review fairness criteria, and provide a framework which, if implemented, would enable an organization to both avoid implementing an unfair machine learning model, but also to avoid the common situation that as an algorithm learns with more data it can become unfair over time. Issues of behavioral ethics in machine learning implementations by organizations have not been thoroughly addressed in the literature, because many of the necessary concepts are dispersed across three literatures: ethics, machine learning, and management. Further, tradeoffs between fairness criteria in machine learning have not been addressed with regards to organizations. We advance the research by introducing an organizing framework for selecting and implementing fair algorithms in organizations.
CAD-PU: A Curvature-Adaptive Deep Learning Solution for Point Set Upsampling
Authors: Jiehong Lin, Xian Shi, Yuan Gao, Ke Chen, Kui Jia
Date: 2020-09-10 00:00:00
Tasks: Point Set Upsampling

Point set is arguably the most direct approximation of an object or scene surface, yet its practical acquisition often suffers from the shortcoming of being noisy, sparse, and possibly incomplete, which restricts its use for a high-quality surface recovery. Point set upsampling aims to increase its density and regularity such that a better surface recovery could be achieved. The problem is severely ill-posed and challenging, considering that the upsampling target itself is only an approximation of the underlying surface. Motivated to improve the surface approximation via point set upsampling, we identify the factors that are critical to the objective, by pairing the surface approximation error bounds of the input and output point sets. It suggests that given a fixed budget of points in the upsampling result, more points should be distributed onto the surface regions where local curvatures are relatively high. To implement the motivation, we propose a novel design of Curvature-ADaptive Point set Upsampling network (CAD-PU), the core of which is a module of curvature-adaptive feature expansion. To train CAD-PU, we follow the same motivation and propose geometrically intuitive surrogates that approximate discrete notions of surface curvature for the upsampled point set. We further integrate the proposed surrogates into an adversarial learning based curvature minimization objective, which gives a practically effective learning of CAD-PU. We conduct thorough experiments that show the efficacy of our contributions and the advantages of our method over existing ones. Our implementation codes are publicly available at https://github.com/JiehongLin/CAD-PU.
Improved Robustness to Open Set Inputs via Tempered Mixup
Authors: Ryne Roady, Tyler L. Hayes, Christopher Kanan
Date: 2020-09-10 00:00:00
Tasks: Classification, General Classification, open-set classification

Supervised classification methods often assume that evaluation data is drawn from the same distribution as training data and that all classes are present for training. However, real-world classifiers must handle inputs that are far from the training distribution including samples from unknown classes. Open set robustness refers to the ability to properly label samples from previously unseen categories as novel and avoid high-confidence, incorrect predictions. Existing approaches have focused on either novel inference methods, unique training architectures, or supplementing the training data with additional background samples. Here, we propose a simple regularization technique easily applied to existing convolutional neural network architectures that improves open set robustness without a background dataset. Our method achieves state-of-the-art results on open set classification baselines and easily scales to large-scale open set classification problems.
Learning Universal Representations from Word to Sentence
Authors: Yian Li, Hai Zhao
Date: 2020-09-10 00:00:00
Tasks: Representation Learning, Sentence

Despite the well-developed cut-edge representation learning for language, most language representation models usually focus on specific level of linguistic unit, which cause great inconvenience when being confronted with handling multiple layers of linguistic objects in a unified way. Thus this work introduces and explores the universal representation learning, i.e., embeddings of different levels of linguistic unit in a uniform vector space through a task-independent evaluation. We present our approach of constructing analogy datasets in terms of words, phrases and sentences and experiment with multiple representation models to examine geometric properties of the learned vector space. Then we empirically verify that well pre-trained Transformer models incorporated with appropriate training settings may effectively yield universal representation. Especially, our implementation of fine-tuning ALBERT on NLI and PPDB datasets achieves the highest accuracy on analogy tasks in different language levels. Further experiments on the insurance FAQ task show effectiveness of universal representation models in real-world applications.
Universal consistency of Wasserstein $k$-NN classifier: Negative and Positive Results
Authors: Donlapark Ponnoprat
Date: 2020-09-10 00:00:00

The Wasserstein distance provides a notion of dissimilarities between probability measures, which has recent applications in learning of structured data with varying size such as images and text documents. In this work, we study the $k$-nearest neighbor classifier ($k$-NN) of probability measures under the Wasserstein distance. We show that the $k$-NN classifier is not universally consistent on the space of measures supported in $(0,1)$. As any Euclidean ball contains a copy of $(0,1)$, one should not expect to obtain universal consistency without some restriction on the base metric space, or the Wasserstein space itself. To this end, via the notion of $\sigma$-finite metric dimension, we show that the $k$-NN classifier is universally consistent on spaces of measures supported in a $\sigma$-uniformly discrete set. In addition, by studying the geodesic structures of the Wasserstein spaces for $p=1$ and $p=2$, we show that the $k$-NN classifier is universally consistent on the space of measures supported on a finite set, the space of Gaussian measures, and the space of measures with densities expressed as finite wavelet series.
Towards Fine-grained Large Object Segmentation 1st Place Solution to 3D AI Challenge 2020 -- Instance Segmentation Track
Authors: Zehui Chen, Qiaofei Li, Feng Zhao
Date: 2020-09-10 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation

This technical report introduces our solutions of Team 'FineGrainedSeg' for Instance Segmentation track in 3D AI Challenge 2020. In order to handle extremely large objects in 3D-FUTURE, we adopt PointRend as our basic framework, which outputs more fine-grained masks compared to HTC and SOLOv2. Our final submission is an ensemble of 5 PointRend models, which achieves the 1st place on both validation and test leaderboards. The code is available at https://github.com/zehuichen123/3DFuture_ins_seg.
COVID-19 Pandemic Cyclic Lockdown Optimization Using Reinforcement Learning
Authors: Mauricio Arango, Lyudmil Pelov
Date: 2020-09-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This work examines the use of reinforcement learning (RL) to optimize cyclic lockdowns, which is one of the methods available for control of the COVID-19 pandemic. The problem is structured as an optimal control system for tracking a reference value, corresponding to the maximum usage level of a critical resource, such as ICU beds. However, instead of using conventional optimal control methods, RL is used to find optimal control policies. A framework was developed to calculate optimal cyclic lockdown timings using an RL-based on-off controller. The RL-based controller is implemented as an RL agent that interacts with an epidemic simulator, implemented as an extended SEIR epidemic model. The RL agent learns a policy function that produces an optimal sequence of open/lockdown decisions such that goals specified in the RL reward function are optimized. Two concurrent goals were used: the first one is a public health goal that minimizes overshoots of ICU bed usage above an ICU bed threshold, and the second one is a socio-economic goal that minimizes the time spent under lockdowns. It is assumed that cyclic lockdowns are considered as a temporary alternative to extended lockdowns when a region faces imminent danger of overpassing resource capacity limits and when imposing an extended lockdown would cause severe social and economic consequences due to lack of necessary economic resources to support its affected population during an extended lockdown.
Non-contact Real time Eye Gaze Mapping System Based on Deep Convolutional Neural Network
Authors: Hoyeon Ahn
Date: 2020-09-10 00:00:00
Tasks: Gaze Estimation, Marketing

Human-Computer Interaction(HCI) is a field that studies interactions between human users and computer systems. With the development of HCI, individuals or groups of people can use various digital technologies to achieve the optimal user experience. Human visual attention and visual intelligence are related to cognitive science, psychology, and marketing informatics, and are used in various applications of HCI. Gaze recognition is closely related to the HCI field because it is meaningful in that it can enhance understanding of basic human behavior. We can obtain reliable visual attention by the Gaze Matching method that finds the area the user is staring at. In the previous methods, the user wears a glasses-type device which in the form of glasses equipped with a gaze tracking function and performs gaze tracking within a limited monitor area. Also, the gaze estimation within a limited range is performed while the user's posture is fixed. We overcome the physical limitations of the previous method in this paper and propose a non-contact gaze mapping system applicable in real-world environments. In addition, we introduce the GIST Gaze Mapping (GGM) dataset, a Gaze mapping dataset created to learn and evaluate gaze mapping.
Enhanced Quadratic Video Interpolation
Authors: Yihao Liu, Liangbin Xie, Li Si-Yao, Wenxiu Sun, Yu Qiao, Chao Dong
Date: 2020-09-10 00:00:00
Tasks: Super-Resolution, Video Frame Interpolation

With the prosperity of digital video industry, video frame interpolation has arisen continuous attention in computer vision community and become a new upsurge in industry. Many learning-based methods have been proposed and achieved progressive results. Among them, a recent algorithm named quadratic video interpolation (QVI) achieves appealing performance. It exploits higher-order motion information (e.g. acceleration) and successfully models the estimation of interpolated flow. However, its produced intermediate frames still contain some unsatisfactory ghosting, artifacts and inaccurate motion, especially when large and complex motion occurs. In this work, we further improve the performance of QVI from three facets and propose an enhanced quadratic video interpolation (EQVI) model. In particular, we adopt a rectified quadratic flow prediction (RQFP) formulation with least squares method to estimate the motion more accurately. Complementary with image pixel-level blending, we introduce a residual contextual synthesis network (RCSN) to employ contextual information in high-dimensional feature space, which could help the model handle more complicated scenes and motion patterns. Moreover, to further boost the performance, we devise a novel multi-scale fusion network (MS-Fusion) which can be regarded as a learnable augmentation process. The proposed EQVI model won the first place in the AIM2020 Video Temporal Super-Resolution Challenge.
On the Fairness of 'Fake' Data in Legal AI
Authors: Lauren Boswell, Arjun Prakash
Date: 2020-09-10 00:00:00
Tasks: Fairness

The economics of smaller budgets and larger case numbers necessitates the use of AI in legal proceedings. We examine the concept of disparate impact and how biases in the training data lead to the search for fairer AI. This paper seeks to begin the discourse on what such an implementation would actually look like with a criticism of pre-processing methods in a legal context . We outline how pre-processing is used to correct biased data and then examine the legal implications of effectively changing cases in order to achieve a fairer outcome including the black box problem and the slow encroachment on legal precedent. Finally we present recommendations on how to avoid the pitfalls of pre-processed data with methods that either modify the classifier or correct the output in the final step.
Improving Coreference Resolution by Leveraging Entity-Centric Features with Graph Neural Networks and Second-order Inference
Authors: Lu Liu, Zhenqiao Song, Xiaoqing Zheng, Jun He
Date: 2020-09-10 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Graph Neural Network

One of the major challenges in coreference resolution is how to make use of entity-level features defined over clusters of mentions rather than mention pairs. However, coreferent mentions usually spread far apart in an entire text, which makes it extremely difficult to incorporate entity-level features. We propose a graph neural network-based coreference resolution method that can capture the entity-centric information by encouraging the sharing of features across all mentions that probably refer to the same real-world entity. Mentions are linked to each other via the edges modeling how likely two linked mentions point to the same entity. Modeling by such graphs, the features between mentions can be shared by message passing operations in an entity-centric manner. A global inference algorithm up to second-order features is also presented to optimally cluster mentions into consistent groups. Experimental results show our graph neural network-based method combing with the second-order decoding algorithm (named GNNCR) achieved close to state-of-the-art performance on the English CoNLL-2012 Shared Task dataset.
Multimodal Noisy Segmentation based fragmented burn scars identification in Amazon Rainforest
Authors: Satyam Mohla, Sidharth Mohla, Anupam Guha, Biplab Banerjee
Date: 2020-09-10 00:00:00
Tasks: Decoder, Management

Detection of burn marks due to wildfires in inaccessible rain forests is important for various disaster management and ecological studies. The fragmented nature of arable landscapes and diverse cropping patterns often thwart the precise mapping of burn scars. Recent advances in remote-sensing and availability of multimodal data offer a viable solution to this mapping problem. However, the task to segment burn marks is difficult because of its indistinguishably with similar looking land patterns, severe fragmented nature of burn marks and partially labelled noisy datasets. In this work we present AmazonNET -- a convolutional based network that allows extracting of burn patters from multimodal remote sensing images. The network consists of UNet: a well-known encoder decoder type of architecture with skip connections commonly used in biomedical segmentation. The proposed framework utilises stacked RGB-NIR channels to segment burn scars from the pastures by training on a new weakly labelled noisy dataset from Amazonia. Our model illustrates superior performance by correctly identifying partially labelled burn scars and rejecting incorrectly labelled samples, demonstrating our approach as one of the first to effectively utilise deep learning based segmentation models in multimodal burn scar identification.
Assignment Flow for Order-Constrained OCT Segmentation
Authors: D. Sitenko, B. Boll, C. Schnörr
Date: 2020-09-10 00:00:00
Tasks: Diagnostic

At the present time Optical Coherence Tomography (OCT) is among the most commonly used non-invasive imaging methods for the acquisition of large volumetric scans of human retinal tissues and vasculature. To resolve decisive information from extracted OCT volumes and to make it applicable for further diagnostic analysis, the exact identification of retinal layer thicknesses serves as an essential task be done for each patient separately. However, the manual examination of multiple OCT scans in a row is a demanding and time consuming task, which results in a lengthy qualification process and is frequently confounded in the presence of tissue-dependent speckle noise. Therefore, the elaboration of automated segmentation models has become an important task in the field of medical image processing. We propose a novel, purely data driven \textit{geometric approach to order-constrained 3D OCT retinal cell layer segmentation} which takes as input data in any metric space and comes along with basic operations that can be effectively computed in parallel. As opposed to many established retina detection methods, our presented formulation avoids the use of any shape prior and accomplishes the natural order of the retina in a purely geometric way. This makes the approach unbiased and hence suited for the detection of local anatomical changes of retinal tissue structure. To demonstrate robustness of the proposed approach, we compare two different choices of features on a data set of manually annotated 3D OCT volumes of healthy human retina. The quality of computed segmentations is compared to the state of the art in terms of mean absolute error and the Dice similarity coefficient. The results indicate a great potential for applying our method to the classification of diseased retina and opens a new research direction regarding the joint segmentation of retinal cell layers and blood vessel structures.
Self-Supervised Annotation of Seismic Images using Latent Space Factorization
Authors: Oluwaseun Joseph Aribido, Ghassan AlRegib, Mohamed Deriche
Date: 2020-09-10 00:00:00
Tasks: Decoder

Annotating seismic data is expensive, laborious and subjective due to the number of years required for seismic interpreters to attain proficiency in interpretation. In this paper, we develop a framework to automate annotating pixels of a seismic image to delineate geological structural elements given image-level labels assigned to each image. Our framework factorizes the latent space of a deep encoder-decoder network by projecting the latent space to learned sub-spaces. Using constraints in the pixel space, the seismic image is further factorized to reveal confidence values on pixels associated with the geological element of interest. Details of the annotated image are provided for analysis and qualitative comparison is made with similar frameworks.
Adjusting Bias in Long Range Stereo Matching: A semantics guided approach
Authors: WeiQin Chuah, Ruwan Tennakoon, Reza Hoseinnezhad, Alireza Bab-Hadiashar, David Suter
Date: 2020-09-10 00:00:00
Tasks: 3D Object Detection, Autonomous Navigation, Depth Estimation, Disparity Estimation, object-detection, Object Detection, Simultaneous Localization and Mapping, Stereo Matching

Stereo vision generally involves the computation of pixel correspondences and estimation of disparities between rectified image pairs. In many applications, including simultaneous localization and mapping (SLAM) and 3D object detection, the disparities are primarily needed to calculate depth values and the accuracy of depth estimation is often more compelling than disparity estimation. The accuracy of disparity estimation, however, does not directly translate to the accuracy of depth estimation, especially for faraway objects. In the context of learning-based stereo systems, this is largely due to biases imposed by the choices of the disparity-based loss function and the training data. Consequently, the learning algorithms often produce unreliable depth estimates of foreground objects, particularly at large distances~($>50$m). To resolve this issue, we first analyze the effect of those biases and then propose a pair of novel depth-based loss functions for foreground and background, separately. These loss functions are tunable and can balance the inherent bias of the stereo learning algorithms. The efficacy of our solution is demonstrated by an extensive set of experiments, which are benchmarked against state of the art. We show on KITTI~2015 benchmark that our proposed solution yields substantial improvements in disparity and depth estimation, particularly for objects located at distances beyond 50 meters, outperforming the previous state of the art by $10\%$.
QuantNet: Learning to Quantize by Learning within Fully Differentiable Framework
Authors: Junjie Liu, Dongchao Wen, Deyu Wang, Wei Tao, Tse-Wei Chen, Kinya Osa, Masami Kato
Date: 2020-09-10 00:00:00
Tasks: Binarization, Image Classification, Quantization

Despite the achievements of recent binarization methods on reducing the performance degradation of Binary Neural Networks (BNNs), gradient mismatching caused by the Straight-Through-Estimator (STE) still dominates quantized networks. This paper proposes a meta-based quantizer named QuantNet, which utilizes a differentiable sub-network to directly binarize the full-precision weights without resorting to STE and any learnable gradient estimators. Our method not only solves the problem of gradient mismatching, but also reduces the impact of discretization errors, caused by the binarizing operation in the deployment, on performance. Generally, the proposed algorithm is implemented within a fully differentiable framework, and is easily extended to the general network quantization with any bits. The quantitative experiments on CIFAR-100 and ImageNet demonstrate that QuantNet achieves the signifficant improvements comparing with previous binarization methods, and even bridges gaps of accuracies between binarized models and full-precision models.