pwc-archive/papers-with-abstracts huggingface dataset:
Estimation of Predictive Performance in High-Dimensional Data Settings using Learning Curves
Authors: Jeroen M. Goedhart, Thomas Klausch, Mark A. van de Wiel
Date: 2022-06-08 00:00:00

In high-dimensional prediction settings, it remains challenging to reliably estimate the test performance. To address this challenge, a novel performance estimation framework is presented. This framework, called Learn2Evaluate, is based on learning curves by fitting a smooth monotone curve depicting test performance as a function of the sample size. Learn2Evaluate has several advantages compared to commonly applied performance estimation methodologies. Firstly, a learning curve offers a graphical overview of a learner. This overview assists in assessing the potential benefit of adding training samples and it provides a more complete comparison between learners than performance estimates at a fixed subsample size. Secondly, a learning curve facilitates in estimating the performance at the total sample size rather than a subsample size. Thirdly, Learn2Evaluate allows the computation of a theoretically justified and useful lower confidence bound. Furthermore, this bound may be tightened by performing a bias correction. The benefits of Learn2Evaluate are illustrated by a simulation study and applications to omics data.
Predictions of Electromotive Force of Magnetic Shape Memory Alloy (MSMA) Using Constitutive Model and Generalized Regression Neural Network
Authors: Md Esharuzzaman Emu
Date: 2022-06-08 00:00:00

Ferromagnetic shape memory alloys (MSMAs), such as Ni-Mn-Ga single crystals, can exhibit the shape memory effect due to an applied magnetic field at room temperature. Under a variable magnetic field and a constant bias stress loading, MSMAs have been used for actuation applications. This work introduced a new feature to the existing macroscale magneto-mechanical model for Ni-Mn-Ga single crystal. This model includes the fact that the magnetic easy axis in the two variants is not exactly perpendicular as observed by D silva et al. This offset helps explain some of the power harvesting capabilities of MSMAs. Model predictions are compared to experimental data collected on a Ni-Mn-Ga single crystal. The experiments include both stress-controlled loading with constant bias magnetic field load (which mimics power harvesting or sensing) and fieldcontrolled loading with constant bias compressive stress (which mimics actuation). Each type of test was performed at several different load levels, and the applied field was measured without the MSMA specimen present so that demagnetization does not affect the experimentally measured field as suggested by Eberle et al. Results show decent agreement between model predictions and experimental data. Although the model predicts experimental results decently, it does not capture all the features of the experimental data. In order to capture all the experimental features, finally, a generalized regression neural network (GRNN) was used to train the experimental data (stress, strain, magnetic field, and emf) so that it can make a reasonably better prediction.
A Survey on the Fairness of Recommender Systems
Authors: Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, Shaoping Ma
Date: 2022-06-08 00:00:00
Tasks: Fairness, Recommendation Systems, Survey

Recommender systems are an essential tool to relieve the information overload challenge and play an important role in people's daily lives. Since recommendations involve allocations of social resources (e.g., job recommendation), an important issue is whether recommendations are fair. Unfair recommendations are not only unethical but also harm the long-term interests of the recommender system itself. As a result, fairness issues in recommender systems have recently attracted increasing attention. However, due to multiple complex resource allocation processes and various fairness definitions, the research on fairness in recommendation is scattered. To fill this gap, we review over 60 papers published in top conferences/journals, including TOIS, SIGIR, and WWW. First, we summarize fairness definitions in the recommendation and provide several views to classify fairness issues. Then, we review recommendation datasets and measurements in fairness studies and provide an elaborate taxonomy of fairness methods in the recommendation. Finally, we conclude this survey by outlining some promising future directions.
Generalized Scene Classification from Small-Scale Datasets with Multi-Task Learning
Authors: Xiangtao Zheng; Tengfei Gong; Xiaobin Li; Xiaoqiang Lu
Date: 2021-10-08 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2021 10
Tasks: Classification, Multi-Task Learning, Scene Classification, Semantic Segmentation

Remote sensing images contain a wealth of spatial information. Efficient scene classification is a necessary precedent step for further application. Despite the great practical value, the mainstream methods using deep convolutional neural networks (CNNs) are generally pretrained on other large datasets (such as ImageNet) and thus fail to capture the specific visual characteristics of remote sensing images. For another, it lacks the generalization ability to new tasks when training a new CNN from scratch with an existing remote sensing dataset. This article addresses the dilemma and uses multiple small-scale datasets to learn a generalized model for efficient scene classification. Since the existing datasets are heterogeneous and cannot be directly combined to train a network, a multitask learning network (MTLN) is developed. The MTLN treats each small-scale dataset as an individual task and uses complementary information contained in multiple tasks to improve generalization. Concretely, the MTLN consists of a shared branch for all tasks and multiple task-specific branches with each for one task. The shared branch extracts shared features for all tasks to achieve information sharing among tasks. The task-specific branch distills the shared features into task-specific features toward the optimal estimation of each specific task. By jointly learning shared features and task-specific features, the MTLN maintains both generalization and discrimination abilities. Two types of MTL scenarios are explored to validate the effectiveness of the proposed method: one is to complete multiple scene classification tasks and the other is to jointly perform scene classification and semantic segmentation.
QTTNet: Quantized Tensor Train Neural Networks for 3D Object and Video Recognition.
Authors: DonghyunLee;DinghengWang;YukuanYang;LeiDeng;GuangsheZhao;GuoqiLi
Date: 2021-09-20 00:00:00
Proceeding: Neural Networks 2021 9
Tasks: Quantization, Video Recognition

Relying on the rapidly increasing capacity of computing clusters and hardware, convolutional neural networks (CNNs) have been successfully applied in various fields and achieved state-of-the-art results. Despite these exciting developments, the huge memory cost is still involved in training and inferring a large-scale CNN model and makes it hard to be widely used in resource-limited portable devices. To address this problem, we establish a training framework for three-dimensional convolutional neural networks (3DCNNs) named QTTNet that combines tensor train (TT) decomposition and data quantization together for further shrinking the model size and decreasing the memory and time cost. Through this framework, we can fully explore the superiority of TT in reducing the number of trainable parameters and the advantage of quantization in decreasing the bit-width of data, particularly compressing 3DCNN model greatly with little accuracy degradation. In addition, due to the low bit quantization to all parameters during the inference process including TT-cores, activations, and batch normalizations, the proposed method naturally takes advantage in memory and time cost. Experimental results of compressing 3DCNNs for 3D object and video recognition on ModelNet40, UCF11, and UCF50 datasets verify the effectiveness of the proposed method. The best compression ratio we have obtained is up to nearly 180 with competitive performance compared with other state-of-the-art researches. Moreover, the total bytes of our QTTNet models on ModelNet40 and UCF11 datasets can be 1000 lower than some typical practices such as MVCNN.
High-order Correlation Preserved Incomplete Multi-view Subspace Clustering
Authors: Zhenglai Li, Chang Tang, Xiao Zheng, Xinwang Liu, Senior Member, Wei zhang, Member, IEEE, and En Zhu
Date: 2022-02-21 00:00:00
Proceeding: IEEE Transactions on Image Processing 2022 2
Tasks: Clustering, Incomplete multi-view clustering, Multi-view Subspace Clustering, Vocal Bursts Intensity Prediction

Incomplete multi-view clustering aims to exploit theinformation of multiple incomplete views to partition data into their clusters. Existing methods only utilize the pair-wise sample correlation and pair-wise view correlation to improve the clustering performance but neglect the high-order correlation of samples and that of views. To address this issue, we propose a high-order correlation preserved incomplete multi-view subspace clustering (HCP-IMSC) method which effectively recovers the missing views of samples and the subspace structure of incomplete multiview data. Specifically, multiple affinity matrices constructed from the incomplete multi-view data are treated as a thirdorder low rank tensor with a tensor factorization regularization which preserves the high-order view correlation and sample correlation. Then, a unified affinity matrix can be obtained by fusing the view-specific affinity matrices in a self-weighted manner. A hypergraph is further constructed from the unified affinity matrix to preserve the high-order geometrical structure of the data with incomplete views. Then, the samples with missing views are restricted to be reconstructed by their neighbor samples under the hypergraph-induced hyper-Laplacian regularization. Furthermore, the learning of view-specific affinity matrices as well as the unified one, tensor factorization, and hyper-Laplacian regularization are integrated into a unified optimization framework. An iterative algorithm is designed to solve the resultant model. Experimental results on various benchmark datasets indicate the superiority of the proposed method. The code is implemented by using MATLAB R2018a and MindSpore library: https://github.com/ChangTang/HCP-IMSC
A Scan-to-Locality Map Strategy for 2D LiDAR and RGB-D Data Fusion
Authors: Jiaqing Zhang, Minghao Yang, Yuanhao Qu, Jinlong Chen, Baohua Qiang, Hong Shi
Date: 2021-12-02 00:00:00
Proceeding: ICONIP 2021 12
Tasks: Simultaneous Localization and Mapping

2D LiDAR and RGB-D camera are two widely used sensors in the task of simultaneous localization and mapping (SLAM). In spite of various map methods for SLAM, an effective strategy which is able to fuse 2D-LiDAR and RGB-D data in a uniform style is still expected. This work proposes a novel map strategy for 2D-LiDAR and RGB-D data. Different from traditional maps where the map information for different views is stored separately in their original two-dimensional (2D) grids or three-dimensional (3D) voxels, in the proposed map strategy, the data obtained by multiple sensors for current view are fused as feature vectors and stored in the current grids. We call this as Scan-to-Locality (STL) map strategy. In re-localization phase, the fusion vector obtained at the current view is used to find the similar ones from the STL map using distance based similarity evaluation and Siamese-network image matching technique. In this way, the re-localization strategy is optimized as a coarse-to-fine schema in the proposed STL map strategy. We validate the proposed method on the widely recognized in-door navigation database Robot@Home. The experiments indicate the proposed method own the abilities of accurate re-localization in nearly real time.
Fast Organization of Objects' Spatial Positions in Manipulator Space from Single RGB-D Camera
Authors: Yangchang Sun, Minghao Yang, Jialing Li, Baohua Qiang, Jinlong Chen, Qingyu Jia
Date: 2021-12-05 00:00:00
Proceeding: ICONIP 2021 12
Tasks: 3D Reconstruction

For the grasp task in physical environment, it is important for the manipulator to know the objects’ spatial positions with as few sensors as possible in real time. This work proposed an effective framework to organize the objects’ spatial positions in the manipulator 3D workspace with a single RGB-D camera robustly and fast. It mainly contains two steps: (1) a 3D reconstruction strategy for objects’ contours obtained in environment; (2) a distance-restricted outlier point elimination strategy to reduce the reconstruction errors caused by sensor noise. The first step ensures fast object extraction and 3D reconstruction from scene image, and the second step contributes to more accurate reconstructions by eliminating outlier points from initial result obtained by the first step. We validated the proposed method in a physical system containing a Kinect 2.0 RGB-D camera and a Mico2 robot. Experiments show that the proposed method can run in quasi real time on a common PC and it outperforms the traditional 3D reconstruction methods.
Pseudo-Label Generation-Evaluation Framework For Cross Domain Weakly Supervised Object Detection
Authors: Shengxiong Ouyang, Xinglu Wang, Kejie Lyu, Yingming Li
Date: 2021-08-23 00:00:00
Proceeding: IEEE International Conference on Image Processing (ICIP) 2021 8
Tasks: object-detection, Object Detection, Pseudo Label, Weakly Supervised Object Detection

Cross domain weakly supervised object detection (CDWSOD), where we can get access to instance-level annotations in the source domain while only image-level annotations are available in the target domain, adapts object detectors from label-rich to label-poor domains. It usually generates pseudo labels in the target domain and utilize them to finetune the detector pretrained in the source domain. In this paper, we propose a new pseudo-label generation-evaluation framework for CDWSOD task. In particular, an evaluator is introduced for the generated pseudo labels in the target domain and the transferring process involves two players: the detector to generate instance-level pseudo labels and the evaluator to judge the quality of pseudo labels. Only high-quality pseudo labels selected by the evaluator are utilized to finetune the detector. Experiments on three representative datasets demonstrate the effectiveness of our framework in various domains.
Linear Precoder Design in Massive MIMO under Realistic Power Amplifier Consumption Constraint
Authors: Emanuele Peschiera, François Rottenberg
Date: 2022-06-08 00:00:00

The energy consumption of wireless networks is a growing concern. In massive MIMO systems, which are being increasingly deployed as part of the 5G roll-out, the power amplifiers in the base stations have a large impact in terms of power demands. Most of the current massive MIMO precoders are designed to minimize the transmit power. However, the efficiency of the power amplifiers depend on their operating regime with respect to their saturation regime, and the consumed power proves to be non-linearly related to the transmit power. Power consumption-based equivalents of maximum ratio transmission, zero-forcing, and regularized zero-forcing precoders are therefore proposed. We show how the structure of the solutions radically changes. While all antennas should be active in order to minimize the transmit power, we find on the contrary that a smaller number of antennas should be activated if the objective is the power consumed by the power amplifiers.
DRMNet: Difference Image Reconstruction Enhanced Multiresolution Network for Optical Change Detection
Authors: Avinash Chouhan, Arijit Sur, Dibyajyoti Chutia
Date: 2022-06-13 00:00:00
Proceeding: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2022 6
Tasks: Change Detection, Change detection for remote sensing images, Image Reconstruction

Change detection in satellite images is an important research area as it has a wide range of applications in natural resource monitoring, geo-hazard detections, urban planning, etc. Identifying physical changes on the ground and avoiding spurious changes due to other reasons like co-registration issues, change in illumination conditions, sun angle, and presence of cloud and fog is a challenging task. This work proposes a multitask learning based change detection model where two parallel pipeline architectures predict change map and image difference. The proposed model takes two images and their difference as input and provides them to a backbone network (BN). The output of the BN is fed into the proposed multiscale attention module for the effective identification of changes in multitemporal and very high-resolution aerial images. In another parallel path, the output of the BN is downsampled and passed to the proposed deconvolution with a subpixel convolution module to generate image difference. Two loss functions are utilized in two parallel paths to train the overall model in an end-to-end supervised setting. A comprehensive set of experiments have been carried out, and the results reveal that the proposed DRMNet model has achieved an F1 score improvement of 1.66% in CDD, 1.61% in SYSU, and 0.14% in LEVIR-CD datasets. It achieved an F1 score of 86.11% for the BCDD dataset with the new test image.
Fusing Event-based and RGB camera for Robust Object Detection in Adverse Conditions
Authors: Abhishek Tomy, Anshul Paigwar, Khushdeep Singh Mann, Alessandro Renzaglia, Christian Laugier
Date: 2022-03-30 00:00:00
Proceeding: ICRA 2022 3
Tasks: 3D Object Detection, Adversarial Attack, Autonomous Driving, Event-based vision, Event Detection, Infrared And Visible Image Fusion, object-detection, Object Detection, Robust Object Detection, Sensor Fusion, Stereo-LiDAR Fusion

The ability to detect objects, under image corruptions and different weather conditions is vital for deep learning models especially when applied to real-world applications such as autonomous driving. Traditional RGB-based detection fails under these conditions and it is thus important to design a sensor suite that is redundant to failures of the primary frame-based detection. Event-based cameras can complement frame-based cameras in low-light conditions and high dynamic range scenarios that an autonomous vehicle can encounter during navigation. Accordingly, we propose a redundant sensor fusion model of event-based and frame-based cameras that is robust to common image corruptions. The method utilizes a voxel grid representation for events as input and proposes a two-parallel feature extractor network for frames and events. Our sensor fusion approach is more robust to corruptions by over 30% compared to only frame-based detections and outperforms the only event-based detection. The model is trained and evaluated on the publicly released DSEC dataset.
On the Adversarial Robustness of Causal Algorithmic Recourse
Authors: Ricardo Dominguez-Olmedo, Amir-Hossein Karimi, Bernhard Schölkopf
Date: 2021-12-21 00:00:00
Tasks: Adversarial Robustness, Decision Making

Algorithmic recourse seeks to provide actionable recommendations for individuals to overcome unfavorable classification outcomes from automated decision-making systems. Recourse recommendations should ideally be robust to reasonably small uncertainty in the features of the individual seeking recourse. In this work, we formulate the adversarially robust recourse problem and show that recourse methods that offer minimally costly recourse fail to be robust. We then present methods for generating adversarially robust recourse for linear and for differentiable classifiers. Finally, we show that regularizing the decision-making classifier to behave locally linearly and to rely more strongly on actionable features facilitates the existence of adversarially robust recourse.
Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation
Authors: Chin-Chia Tsai, Tsung-Hsuan Wu, and Shang-Hong Lai
Date: 2022-10-23 00:00:00
Proceeding: CVPR 2022 10
Tasks: Anomaly Detection, Representation Learning, Segmentation, Self-Supervised Learning

Unsupervised representation learning has been proven
to be effective for the challenging anomaly detection and
segmentation tasks. In this paper, we propose a multi-scale
patch-based representation learning method to extract critical and representative information from normal images.
By taking the relative feature similarity between patches
of different local distances into account, we can achieve
better representation learning. Moreover, we propose a
refined way to improve the self-supervised learning strategy, thus allowing our model to learn better geometric relationship between neighboring patches. Through sliding
patches of different scales all over an image, our model
extracts representative features from each patch and compares them with those in the training set of normal images
to detect the anomalous regions. Our experimental results
on MVTec AD dataset and BTAD dataset demonstrate the
proposed method achieves the state-of-the-art accuracy for
both anomaly detection and segmentation.
White-box Membership Attack Against Machine Learning Based Retinopathy Classification
Authors: Mounia Hamidouche, Reda Bellafqira, Gwenolé Quellec, Gouenou Coatrieux
Date: 2022-05-30 00:00:00
Tasks: BIG-bench Machine Learning, Inference Attack, Membership Inference Attack, Memorization

The advances in machine learning (ML) have greatly improved AI-based diagnosis aid systems in medical imaging. However, being based on collecting medical data specific to individuals induces several security issues, especially in terms of privacy. Even though the owner of the images like a hospital put in place strict privacy protection provisions at the level of its information system, the model trained over his images still holds disclosure potential. The trained model may be accessible to an attacker as: 1) White-box: accessing to the model architecture and parameters; 2) Black box: where he can only query the model with his own inputs through an appropriate interface. Existing attack methods include: feature estimation attacks (FEA), membership inference attack (MIA), model memorization attack (MMA) and identification attacks (IA). In this work we focus on MIA against a model that has been trained to detect diabetic retinopathy from retinal images. Diabetic retinopathy is a condition that can cause vision loss and blindness in the people who have diabetes. MIA is the process of determining whether a data sample comes from the training data set of a trained ML model or not. From a privacy perspective in our use case where a diabetic retinopathy classification model is given to partners that have at their disposal images along with patients' identifiers, inferring the membership status of a data sample can help to state if a patient has contributed or not to the training of the model.