pwc-archive/papers-with-abstracts huggingface dataset:
Camera Bias in a Fine Grained Classification Task
Authors: Philip T. Jackson, Stephen Bonner, Ning Jia, Christopher Holder, Jon Stonehouse, Boguslaw Obara
Date: 2020-07-16 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

We show that correlations between the camera used to acquire an image and the class label of that image can be exploited by convolutional neural networks (CNN), resulting in a model that "cheats" at an image classification task by recognizing which camera took the image and inferring the class label from the camera. We show that models trained on a dataset with camera / label correlations do not generalize well to images in which those correlations are absent, nor to images from unencountered cameras. Furthermore, we investigate which visual features they are exploiting for camera recognition. Our experiments present evidence against the importance of global color statistics, lens deformation and chromatic aberration, and in favor of high frequency features, which may be introduced by image processing algorithms built into the cameras.
Extended Stochastic Block Models with Application to Criminal Networks
Authors: Sirio Legramanti, Tommaso Rigon, Daniele Durante, David B. Dunson
Date: 2020-07-16 00:00:00
Tasks: Community Detection, Model Selection, Uncertainty Quantification

Reliably learning group structures among nodes in network data is challenging in several applications. We are particularly motivated by studying covert networks that encode relationships among criminals. These data are subject to measurement errors, and exhibit a complex combination of an unknown number of core-periphery, assortative and disassortative structures that may unveil key architectures of the criminal organization. The coexistence of these noisy block patterns limits the reliability of routinely-used community detection algorithms, and requires extensions of model-based solutions to realistically characterize the node partition process, incorporate information from node attributes, and provide improved strategies for estimation and uncertainty quantification. To cover these gaps, we develop a new class of extended stochastic block models (ESBM) that infer groups of nodes having common connectivity patterns via Gibbs-type priors on the partition process. This choice encompasses many realistic priors for criminal networks, covering solutions with fixed, random and infinite number of possible groups, and facilitates the inclusion of node attributes in a principled manner. Among the new alternatives in our class, we focus on the Gnedin process as a realistic prior that allows the number of groups to be finite, random and subject to a reinforcement process coherent with criminal networks. A collapsed Gibbs sampler is proposed for the whole ESBM class, and refined strategies for estimation, prediction, uncertainty quantification and model selection are outlined. The ESBM performance is illustrated in realistic simulations and in an application to an Italian mafia network, where we unveil key complex block structures, mostly hidden from state-of-the-art alternatives.
SqueezeFacePoseNet: Lightweight Face Verification Across Different Poses for Mobile Platforms
Authors: Fernando Alonso-Fernandez, Javier Barrachina, Kevin Hernandez-Diaz, Josef Bigun
Date: 2020-07-16 00:00:00
Tasks: Face Recognition, Face Verification, Lightweight Face Recognition

Virtual applications through mobile platforms are one of the most critical and ever-growing fields in AI, where ubiquitous and real-time person authentication has become critical after the breakthrough of all services provided via mobile devices. In this context, face verification technologies can provide reliable and robust user authentication, given the availability of cameras in these devices, as well as their widespread use in everyday applications. The rapid development of deep Convolutional Neural Networks has resulted in many accurate face verification architectures. However, their typical size (hundreds of megabytes) makes them infeasible to be incorporated in downloadable mobile applications where the entire file typically may not exceed 100 Mb. Accordingly, we address the challenge of developing a lightweight face recognition network of just a few megabytes that can operate with sufficient accuracy in comparison to much larger models. The network also should be able to operate under different poses, given the variability naturally observed in uncontrolled environments where mobile devices are typically used. In this paper, we adapt the lightweight SqueezeNet model, of just 4.4MB, to effectively provide cross-pose face recognition. After trained on the MS-Celeb-1M and VGGFace2 databases, our model achieves an EER of 1.23% on the difficult frontal vs. profile comparison, and0.54% on profile vs. profile images. Under less extreme variations involving frontal images in any of the enrolment/query images pair, EER is pushed down to<0.3%, and the FRR at FAR=0.1%to less than 1%. This makes our light model suitable for face recognition where at least acquisition of the enrolment image can be controlled. At the cost of a slight degradation in performance, we also test an even lighter model (of just 2.5MB) where regular convolutions are replaced with depth-wise separable convolutions.
FTRANS: Energy-Efficient Acceleration of Transformers using FPGA
Authors: Bingbing Li, Santosh Pandey, Haowen Fang, Yanjun Lyv, Ji Li, Jieyang Chen, Mimi Xie, Lipeng Wan, Hang Liu, Caiwen Ding
Date: 2020-07-16 00:00:00
Tasks: CPU, GPU, Model Compression

In natural language processing (NLP), the "Transformer" architecture was proposed as the first transduction model replying entirely on self-attention mechanisms without using sequence-aligned recurrent neural networks (RNNs) or convolution, and it achieved significant improvements for sequence to sequence tasks. The introduced intensive computation and storage of these pre-trained language representations has impeded their popularity into computation and memory-constrained devices. The field-programmable gate array (FPGA) is widely used to accelerate deep learning algorithms for its high parallelism and low latency. However, the trained models are still too large to accommodate to an FPGA fabric. In this paper, we propose an efficient acceleration framework, Ftrans, for transformer-based large scale language representations. Our framework includes enhanced block-circulant matrix (BCM)-based weight representation to enable model compression on large-scale language representations at the algorithm level with few accuracy degradation, and an acceleration design at the architecture level. Experimental results show that our proposed framework significantly reduces the model size of NLP models by up to 16 times. Our FPGA design achieves 27.07x and 81x improvement in performance and energy efficiency compared to CPU, and up to 8.80x improvement in energy efficiency compared to GPU.
A Smoothed Analysis of Online Lasso for the Sparse Linear Contextual Bandit Problem
Authors: Zhiyuan Liu, Huazheng Wang, Bo Waggoner, Youjian, Liu, Lijun Chen
Date: 2020-07-16 00:00:00

We investigate the sparse linear contextual bandit problem where the parameter $\theta$ is sparse. To relieve the sampling inefficiency, we utilize the "perturbed adversary" where the context is generated adversarilly but with small random non-adaptive perturbations. We prove that the simple online Lasso supports sparse linear contextual bandit with regret bound $\mathcal{O}(\sqrt{kT\log d})$ even when $d \gg T$ where $k$ and $d$ are the number of effective and ambient dimension, respectively. Compared to the recent work from Sivakumar et al. (2020), our analysis does not rely on the precondition processing, adaptive perturbation (the adaptive perturbation violates the i.i.d perturbation setting) or truncation on the error set. Moreover, the special structures in our results explicitly characterize how the perturbation affects exploration length, guide the design of perturbation together with the fundamental performance limit of perturbation method. Numerical experiments are provided to complement the theoretical analysis.
On Robustness and Transferability of Convolutional Neural Networks
Authors: Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic
Date: 2020-07-16 00:00:00
Proceeding: CVPR 2021 1
Tasks: image-classification, Image Classification, Transfer Learning

Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts. However, several recent breakthroughs in transfer learning suggest that these networks can cope with severe distribution shifts and successfully adapt to new tasks from a few training examples. In this work we study the interplay between out-of-distribution and transfer performance of modern image classification CNNs for the first time and investigate the impact of the pre-training data size, the model scale, and the data preprocessing pipeline. We find that increasing both the training set and model sizes significantly improve the distributional shift robustness. Furthermore, we show that, perhaps surprisingly, simple changes in the preprocessing such as modifying the image resolution can significantly mitigate robustness issues in some cases. Finally, we outline the shortcomings of existing robustness evaluation datasets and introduce a synthetic dataset SI-Score we use for a systematic analysis across factors of variation common in visual data such as object size and position.
InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling
Authors: Jun Wang, Shiyi Lan, Mingfei Gao, Larry S. Davis
Date: 2020-07-16 00:00:00
Proceeding: ECCV 2020 8
Tasks: 3D Object Detection, Autonomous Driving, object-detection, Object Detection, Region Proposal

Real-time 3D object detection is crucial for autonomous cars. Achieving promising performance with high efficiency, voxel-based approaches have received considerable attention. However, previous methods model the input space with features extracted from equally divided sub-regions without considering that point cloud is generally non-uniformly distributed over the space. To address this issue, we propose a novel 3D object detection framework with dynamic information modeling. The proposed framework is designed in a coarse-to-fine manner. Coarse predictions are generated in the first stage via a voxel-based region proposal network. We introduce InfoFocus, which improves the coarse detections by adaptively refining features guided by the information of point cloud density. Experiments are conducted on the large-scale nuScenes 3D detection benchmark. Results show that our framework achieves the state-of-the-art performance with 31 FPS and improves our baseline significantly by 9.0% mAP on the nuScenes test set.
A New Look at Ghost Normalization
Authors: Neofytos Dimitriou, Ognjen Arandjelovic
Date: 2020-07-16 00:00:00

Batch normalization (BatchNorm) is an effective yet poorly understood technique for neural network optimization. It is often assumed that the degradation in BatchNorm performance to smaller batch sizes stems from it having to estimate layer statistics using smaller sample sizes. However, recently, Ghost normalization (GhostNorm), a variant of BatchNorm that explicitly uses smaller sample sizes for normalization, has been shown to improve upon BatchNorm in some datasets. Our contributions are: (i) we uncover a source of regularization that is unique to GhostNorm, and not simply an extension from BatchNorm, (ii) three types of GhostNorm implementations are described, two of which employ BatchNorm as the underlying normalization technique, (iii) by visualising the loss landscape of GhostNorm, we observe that GhostNorm consistently decreases the smoothness when compared to BatchNorm, (iv) we introduce Sequential Normalization (SeqNorm), and report superior performance over state-of-the-art methodologies on both CIFAR--10 and CIFAR--100 datasets.
Smooth Deformation Field-based Mismatch Removal in Real-time
Authors: Haoyin Zhou, Jagadeesan Jayender
Date: 2020-07-16 00:00:00

This paper studies the mismatch removal problem, which may serve as the subsequent step of feature matching. Non-rigid deformation makes it difficult to remove mismatches because no parametric transformation can be found. To solve this problem, we first propose an algorithm based on the re-weighting and 1-point RANSAC strategy (R1P-RNSC), which is a parametric method under a reasonable assumption that the non-rigid deformation can be approximately represented by multiple locally rigid transformations. R1P-RNSC is fast but suffers from a drawback that the local smoothing information cannot be taken into account. Then, we propose a non-parametric algorithm based on the expectation maximization algorithm and dual quaternion (EMDQ) representation to generate the smooth deformation field. The two algorithms compensate for the drawbacks of each other. Specifically, EMDQ needs good initial values provided by R1P-RNSC, and R1P-RNSC needs EMDQ for refinement. Experimental results with real-world data demonstrate that the combination of the two algorithms has the best accuracy compared to other state-of-the-art methods, which can handle up to 85% of outliers in real-time. The ability to generate dense deformation field from sparse matches with outliers in real-time makes the proposed algorithms have many potential applications, such as non-rigid registration and SLAM.
Talking-head Generation with Rhythmic Head Motion
Authors: Lele Chen, Guofeng Cui, Celong Liu, Zhong Li, Ziyi Kou, Yi Xu, Chenliang Xu
Date: 2020-07-16 00:00:00
Tasks: Talking Head Generation

When people deliver a speech, they naturally move heads, and this rhythmic head motion conveys prosodic information. However, generating a lip-synced video while moving head naturally is challenging. While remarkably successful, existing works either generate still talkingface videos or rely on landmark/video frames as sparse/dense mapping guidance to generate head movements, which leads to unrealistic or uncontrollable video synthesis. To overcome the limitations, we propose a 3D-aware generative network along with a hybrid embedding module and a non-linear composition module. Through modeling the head motion and facial expressions1 explicitly, manipulating 3D animation carefully, and embedding reference images dynamically, our approach achieves controllable, photo-realistic, and temporally coherent talking-head videos with natural head movements. Thoughtful experiments on several standard benchmarks demonstrate that our method achieves significantly better results than the state-of-the-art methods in both quantitative and qualitative comparisons. The code is available on https://github.com/ lelechen63/Talking-head-Generation-with-Rhythmic-Head-Motion.
In search of the weirdest galaxies in the Universe
Authors: Job Formsma, Teymoor Saifollahi
Date: 2020-07-16 00:00:00
Tasks: Clustering, Dimensionality Reduction, Outlier Detection

Weird galaxies are outliers that have either unknown or very uncommon features making them different from the normal sample. These galaxies are very interesting as they may provide new insights into current theories, or can be used to form new theories about processes in the Universe. Interesting outliers are often found by accident, but this will become increasingly more difficult with future big surveys generating an enormous amount of data. This gives the need for machine learning detection techniques to find the interesting weird objects. In this work, we inspect the galaxy spectra of the third data release of the Galaxy And Mass Assembly survey and look for the weird outlying galaxies using two different outlier detection techniques. First, we apply distance-based Unsupervised Random Forest on the galaxy spectra using the flux values as input features. Spectra with a high outlier score are inspected and divided into different categories such as blends, quasi-stellar objects, and BPT outliers. We also experiment with a reconstruction-based outlier detection method using a variational autoencoder and compare the results of the two different methods. At last, we apply dimensionality reduction techniques on the output of the methods to inspect the clustering of similar spectra. We find that both unsupervised methods extract important features from the data and can be used to find many different types of outliers.
Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment
Authors: Teng Liu, Xiaolin Tang, Jinwei Zhang, Wenbo Li, Zejian Deng, Yalian Yang
Date: 2020-07-16 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

As a typical vehicle-cyber-physical-system (V-CPS), connected automated vehicles attracted more and more attention in recent years. This paper focuses on discussing the decision-making (DM) strategy for autonomous vehicles in a connected environment. First, the highway DM problem is formulated, wherein the vehicles can exchange information via wireless networking. Then, two classical reinforcement learning (RL) algorithms, Q-learning and Dyna, are leveraged to derive the DM strategies in a predefined driving scenario. Finally, the control performance of the derived DM policies in safety and efficiency is analyzed. Furthermore, the inherent differences of the RL algorithms are embodied and discussed in DM strategies.
Accelerating Robustness Verification of Deep Neural Networks Guided by Target Labels
Authors: Wenjie Wan, Zhaodi Zhang, Yiwei Zhu, Min Zhang, Fu Song
Date: 2020-07-16 00:00:00
Tasks: Autonomous Driving, Medical Diagnosis

Deep Neural Networks (DNNs) have become key components of many safety-critical applications such as autonomous driving and medical diagnosis. However, DNNs have been shown suffering from poor robustness because of their susceptibility to adversarial examples such that small perturbations to an input result in misprediction. Addressing to this concern, various approaches have been proposed to formally verify the robustness of DNNs. Most of these approaches reduce the verification problem to optimization problems of searching an adversarial example for a given input so that it is not correctly classified to the original label. However, they are limited in accuracy and scalability. In this paper, we propose a novel approach that can accelerate the robustness verification techniques by guiding the verification with target labels. The key insight of our approach is that the robustness verification problem of DNNs can be solved by verifying sub-problems of DNNs, one per target label. Fixing the target label during verification can drastically reduce the search space and thus improve the efficiency. We also propose an approach by leveraging symbolic interval propagation and linear relaxation techniques to sort the target labels in terms of chances that adversarial examples exist. This often allows us to quickly falsify the robustness of DNNs and the verification for remaining target labels could be avoided. Our approach is orthogonal to, and can be integrated with, many existing verification techniques. For evaluation purposes, we integrate it with three recent promising DNN verification tools, i.e., MipVerify, DeepZ, and Neurify. Experimental results show that our approach can significantly improve these tools by 36X speedup when the perturbation distance is set in a reasonable range.
Detecting Deepfake Videos: An Analysis of Three Techniques
Authors: Armaan Pishori, Brittany Rollins, Nicolas van Houten, Nisha Chatwani, Omar Uraimov
Date: 2020-07-15 00:00:00
Tasks: DeepFake Detection, Face Swapping

Recent advances in deepfake generating algorithms that produce manipulated media have had dangerous implications in privacy, security and mass communication. Efforts to combat this issue have risen in the form of competitions and funding for research to detect deepfakes. This paper presents three techniques and algorithms: convolutional LSTM, eye blink detection and grayscale histograms-pursued while participating in the Deepfake Detection Challenge. We assessed the current knowledge about deepfake videos, a more severe version of manipulated media, and previous methods used, and found relevance in the grayscale histogram technique over others. We discussed the implications of each method developed and provided further steps to improve the given findings.
Prediction of Cancer Microarray and DNA Methylation Data using Non-negative Matrix Factorization
Authors: Parth Patel, Kalpdrum Passi, Chakresh Kumar Jain
Date: 2020-07-15 00:00:00

Over the past few years, there has been a considerable spread of microarray technology in many biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms and approaches for the reduction of dimensionality of such microarray datasets. This study exploits the matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification accuracies are then compared for these algorithms. This technique gives an accuracy of 98%.
The Monte Carlo Transformer: a stochastic self-attention model for sequence prediction
Authors: Alice Martin, Charles Ollion, Florian Strub, Sylvain Le Corff, Olivier Pietquin
Date: 2020-07-15 00:00:00

This paper introduces the Sequential Monte Carlo Transformer, an original approach that naturally captures the observations distribution in a transformer architecture. The keys, queries, values and attention vectors of the network are considered as the unobserved stochastic states of its hidden structure. This generative model is such that at each time step the received observation is a random function of its past states in a given attention window. In this general state-space setting, we use Sequential Monte Carlo methods to approximate the posterior distributions of the states given the observations, and to estimate the gradient of the log-likelihood. We hence propose a generative model giving a predictive distribution, instead of a single-point estimate.