pwc-archive/papers-with-abstracts huggingface dataset:
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution
Authors: Nikaash Puri, Sukriti Verma, Piyush Gupta, Dhruv Kayastha, Shripad Deshmukh, Balaji Krishnamurthy, Sameer Singh
Date: 2019-12-23 00:00:00
Tasks: Atari Games, Board Games, Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL), Specificity

As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches to compute saliency often highlight regions of the input that are not relevant to the action taken by the agent. Our proposed approach, SARFA (Specific and Relevant Feature Attribution), generates more focused saliency maps by balancing two aspects (specificity and relevance) that capture different desiderata of saliency. The first captures the impact of perturbation on the relative expected reward of the action to be explained. The second downweighs irrelevant features that alter the relative expected rewards of actions other than the action to be explained. We compare SARFA with existing approaches on agents trained to play board games (Chess and Go) and Atari games (Breakout, Pong and Space Invaders). We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that SARFA generates saliency maps that are more interpretable for humans than existing approaches. For the code release and demo videos, see https://nikaashpuri.github.io/sarfa-saliency/.
Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders
Authors: Yasemin Bozkurt Varolgunes, Tristan Bereau, Joseph F. Rudzinski
Date: 2019-12-22 00:00:00
Tasks: Clustering, Dimensionality Reduction, Physical Intuition

Extracting insight from the enormous quantity of data generated from molecular simulations requires the identification of a small number of collective variables whose corresponding low-dimensional free-energy landscape retains the essential features of the underlying system. Data-driven techniques provide a systematic route to constructing this landscape, without the need for extensive a priori intuition into the relevant driving forces. In particular, autoencoders are powerful tools for dimensionality reduction, as they naturally force an information bottleneck and, thereby, a low-dimensional embedding of the essential features. While variational autoencoders ensure continuity of the embedding by assuming a unimodal Gaussian prior, this is at odds with the multi-basin free-energy landscapes that typically arise from the identification of meaningful collective variables. In this work, we incorporate this physical intuition into the prior by employing a Gaussian mixture variational autoencoder (GMVAE), which encourages the separation of metastable states within the embedding. The GMVAE performs dimensionality reduction and clustering within a single unified framework, and is capable of identifying the inherent dimensionality of the input data, in terms of the number of Gaussians required to categorize the data. We illustrate our approach on two toy models, alanine dipeptide, and a challenging disordered peptide ensemble, demonstrating the enhanced clustering effect of the GMVAE prior compared to standard VAEs. The resulting embeddings appear to be promising representations for constructing Markov state models, highlighting the transferability of the dimensionality reduction from static equilibrium properties to dynamics.
Unsupervised Representation Learning by Predicting Random Distances
Authors: Hu Wang, Guansong Pang, Chunhua Shen, Congbo Ma
Date: 2019-12-22 00:00:00
Tasks: Anomaly Detection, Clustering, Representation Learning

Deep neural networks have gained tremendous success in a broad range of machine learning tasks due to its remarkable capability to learn semantic-rich features from high-dimensional data. However, they often require large-scale labelled data to successfully learn such features, which significantly hinders their adaption into unsupervised learning tasks, such as anomaly detection and clustering, and limits their applications into critical domains where obtaining massive labelled data is prohibitively expensive. To enable unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected space. Random mapping is a theoretically proven approach to obtain approximately preserved distances. To well predict these random distances, the representation learner is optimised to learn genuine class structures that are implicitly embedded in the randomly projected space. Empirical results on 19 real-world datasets show that our learned representations substantially outperform a few state-of-the-art competing methods in both anomaly detection and clustering tasks. Code is available at https://git.io/RDP
A Deep Learning Model for Chilean Bills Classification
Authors: Daniel San Martin, Daniel Manzano
Date: 2019-12-21 00:00:00
Tasks: Classification, Data Augmentation, Deep Learning, General Classification, model

Automatic bill classification is an attractive task with many potential applications such as automated detection and counting in images or videos. To address this purpose we present a Deep Learning Model to classify Chilean Banknotes, because of its successful results in image processing applications. For optimal performance of the proposed model, data augmentation techniques are introduced due to the limited number of image samples. Positive results were achieved in this work, verifying that it could be a stating point to be extended to more complex applications.
Detecting Deepfake-Forged Contents with Separable Convolutional Neural Network and Image Segmentation
Authors: Chia-Mu Yu, Ching-Tang Chang, Yen-Wu Ti
Date: 2019-12-21 00:00:00
Tasks: Face Swapping, Image Segmentation, Semantic Segmentation

Recent advances in AI technology have made the forgery of digital images and videos easier, and it has become significantly more difficult to identify such forgeries. These forgeries, if disseminated with malicious intent, can negatively impact social and political stability, and pose significant ethical and legal challenges as well. Deepfake is a variant of auto-encoders that use deep learning techniques to identify and exchange images of a person's face in a picture or film. Deepfake can result in an erosion of public trust in digital images and videos, which has far-reaching effects on political and social stability. This study therefore proposes a solution for facial forgery detection to determine if a picture or film has ever been processed by Deepfake. The proposed solution reaches detection efficiency by using the recently proposed separable convolutional neural network (CNN) and image segmentation. In addition, this study also examined how different image segmentation methods affect detection results. Finally, the ensemble model is used to improve detection capabilities. Experiment results demonstrated the excellent performance of the proposed solution.
Locality and compositionality in zero-shot learning
Authors: Tristan Sylvain, Linda Petrini, Devon Hjelm
Date: 2019-12-20 00:00:00
Proceeding: ICLR 2020 1
Tasks: Representation Learning, Zero-Shot Learning

In this work we study locality and compositionality in the context of learning representations for Zero Shot Learning (ZSL). In order to well-isolate the importance of these properties in learned representations, we impose the additional constraint that, differently from most recent work in ZSL, no pre-training on different datasets (e.g. ImageNet) is performed. The results of our experiments show how locality, in terms of small parts of the input, and compositionality, i.e. how well can the learned representations be expressed as a function of a smaller vocabulary, are both deeply related to generalization and motivate the focus on more local-aware models in future research directions for representation learning.
Unsupervised Few-shot Learning via Self-supervised Training
Authors: Zilong Ji, Xiaolong Zou, Tiejun Huang, Si Wu
Date: 2019-12-20 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Few-Shot Learning, Person Re-Identification, Unsupervised Few-Shot Image Classification, Unsupervised Few-Shot Learning

Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community. However, current few-shot learners are mostly supervised and rely heavily on a large amount of labeled examples. Unsupervised learning is a more natural procedure for cognitive mammals and has produced promising results in many machine learning tasks. In the current study, we develop a method to learn an unsupervised few-shot learner via self-supervised training (UFLST), which can effectively generalize to novel but related classes. The proposed model consists of two alternate processes, progressive clustering and episodic training. The former generates pseudo-labeled training examples for constructing episodic tasks; and the later trains the few-shot learner using the generated episodic tasks which further optimizes the feature representations of data. The two processes facilitate with each other, and eventually produce a high quality few-shot learner. Using the benchmark dataset Omniglot and Mini-ImageNet, we show that our model outperforms other unsupervised few-shot learning methods. Using the benchmark dataset Market1501, we further demonstrate the feasibility of our model to a real-world application on person re-identification.
Axial Attention in Multidimensional Transformers
Authors: Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn, Tim Salimans
Date: 2019-12-20 00:00:00
Tasks: Image Generation

We propose Axial Transformers, a self-attention-based autoregressive model for images and other data organized as high dimensional tensors. Existing autoregressive models either suffer from excessively large computational resource requirements for high dimensional data, or make compromises in terms of distribution expressiveness or ease of implementation in order to decrease resource requirements. Our architecture, by contrast, maintains both full expressiveness over joint distributions over data and ease of implementation with standard deep learning frameworks, while requiring reasonable memory and computation and achieving state-of-the-art results on standard generative modeling benchmarks. Our models are based on axial attention, a simple generalization of self-attention that naturally aligns with the multiple dimensions of the tensors in both the encoding and the decoding settings. Notably the proposed structure of the layers allows for the vast majority of the context to be computed in parallel during decoding without introducing any independence assumptions. This semi-parallel structure goes a long way to making decoding from even a very large Axial Transformer broadly applicable. We demonstrate state-of-the-art results for the Axial Transformer on the ImageNet-32 and ImageNet-64 image benchmarks as well as on the BAIR Robotic Pushing video benchmark. We open source the implementation of Axial Transformers.
An Unsupervised Deep Learning Method for Multi-coil Cine MRI
Authors: Ziwen Ke, Jing Cheng, Leslie Ying, Hairong Zheng, Yanjie Zhu, Dong Liang
Date: 2019-12-20 00:00:00
Tasks: Deep Learning, MRI Reconstruction

Deep learning has achieved good success in cardiac magnetic resonance imaging (MRI) reconstruction, in which convolutional neural networks (CNNs) learn a mapping from the undersampled k-space to the fully sampled images. Although these deep learning methods can improve the reconstruction quality compared with iterative methods without requiring complex parameter selection or lengthy reconstruction time, the following issues still need to be addressed: 1) all these methods are based on big data and require a large amount of fully sampled MRI data, which is always difficult to obtain for cardiac MRI; 2) the effect of coil correlation on reconstruction in deep learning methods for dynamic MR imaging has never been studied. In this paper, we propose an unsupervised deep learning method for multi-coil cine MRI via a time-interleaved sampling strategy. Specifically, a time-interleaved acquisition scheme is utilized to build a set of fully encoded reference data by directly merging the k-space data of adjacent time frames. Then these fully encoded data can be used to train a parallel network for reconstructing images of each coil separately. Finally, the images from each coil are combined via a CNN to implicitly explore the correlations between coils. The comparisons with classic k-t FOCUSS, k-t SLR, L+S and KLR methods on in vivo datasets show that our method can achieve improved reconstruction results in an extremely short amount of time.
Mitigating large adversarial perturbations on X-MAS (X minus Moving Averaged Samples)
Authors: Woohyung Chun, Sung-Min Hong, Junho Huh, Inyup Kang
Date: 2019-12-19 00:00:00
Tasks: Avg

We propose the scheme that mitigates the adversarial perturbation $\epsilon$ on the adversarial example $X_{adv}$ ($=$ $X$ $\pm$ $\epsilon$, $X$ is a benign sample) by subtracting the estimated perturbation $\hat{\epsilon}$ from $X$ $+$ $\epsilon$ and adding $\hat{\epsilon}$ to $X$ $-$ $\epsilon$. The estimated perturbation $\hat{\epsilon}$ comes from the difference between $X_{adv}$ and its moving-averaged outcome $W_{avg}*X_{adv}$ where $W_{avg}$ is $N \times N$ moving average kernel that all the coefficients are one. Usually, the adjacent samples of an image are close to each other such that we can let $X$ $\approx$ $W_{avg}*X$ (naming this relation after X-MAS[X minus Moving Averaged Samples]). By doing that, we can make the estimated perturbation $\hat{\epsilon}$ falls within the range of $\epsilon$. The scheme is also extended to do the multi-level mitigation by configuring the mitigated adversarial example $X_{adv}$ $\pm$ $\hat{\epsilon}$ as a new adversarial example to be mitigated. The multi-level mitigation gets $X_{adv}$ closer to $X$ with a smaller (i.e. mitigated) perturbation than original unmitigated perturbation by setting the moving averaged adversarial sample $W_{avg} * X_{adv}$ (which has the smaller perturbation than $X_{adv}$ if $X$ $\approx$ $W_{avg}*X$) as the boundary condition that the multi-level mitigation cannot cross over (i.e. decreasing $\epsilon$ cannot go below and increasing $\epsilon$ cannot go beyond). With the multi-level mitigation, we can get high prediction accuracies even in the adversarial example having a large perturbation (i.e. $\epsilon$ $>$ $16$). The proposed scheme is evaluated with adversarial examples crafted by the FGSM (Fast Gradient Sign Method) based attacks on ResNet-50 trained with ImageNet dataset.
Image Analytics for Legal Document Review: A Transfer Learning Approach
Authors: Nathaniel Huber-Fliflet, Fusheng Wei, Haozhen Zhao, Han Qin, Shi Ye, Amy Tsang
Date: 2019-12-19 00:00:00
Tasks: Clustering, image-classification, Image Classification, Image Clustering, object-detection, Object Detection, Transfer Learning

Though technology assisted review in electronic discovery has been focusing on text data, the need of advanced analytics to facilitate reviewing multimedia content is on the rise. In this paper, we present several applications of deep learning in computer vision to Technology Assisted Review of image data in legal industry. These applications include image classification, image clustering, and object detection. We use transfer learning techniques to leverage established pretrained models for feature extraction and fine tuning. These applications are first of their kind in the legal industry for image document review. We demonstrate effectiveness of these applications with solving real world business challenges.
So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification
Authors: Xiao Xiang Zhu, Jingliang Hu, Chunping Qiu, Yilei Shi, Jian Kang, Lichao Mou, Hossein Bagheri, Matthias Häberle, Yuansheng Hua, Rong Huang, Lloyd Hughes, Hao Li, Yao Sun, Guichen Zhang, Shiyao Han, Michael Schmitt, Yuanyuan Wang
Date: 2019-12-19 00:00:00
Tasks: BIG-bench Machine Learning, Cultural Vocal Bursts Intensity Prediction, General Classification

Access to labeled reference data is one of the grand challenges in supervised machine learning endeavors. This is especially true for an automated analysis of remote sensing images on a global scale, which enables us to address global challenges such as urbanization and climate change using state-of-the-art machine learning techniques. To meet these pressing needs, especially in urban research, we provide open access to a valuable benchmark dataset named "So2Sat LCZ42," which consists of local climate zone (LCZ) labels of about half a million Sentinel-1 and Sentinel-2 image patches in 42 urban agglomerations (plus 10 additional smaller areas) across the globe. This dataset was labeled by 15 domain experts following a carefully designed labeling work flow and evaluation process over a period of six months. As rarely done in other labeled remote sensing dataset, we conducted rigorous quality assessment by domain experts. The dataset achieved an overall confidence of 85%. We believe this LCZ dataset is a first step towards an unbiased globallydistributed dataset for urban growth monitoring using machine learning methods, because LCZ provide a rather objective measure other than many other semantic land use and land cover classifications. It provides measures of the morphology, compactness, and height of urban areas, which are less dependent on human and culture. This dataset can be accessed from http://doi.org/10.14459/2018mp1483140.
Intra-Variable Handwriting Inspection Reinforced with Idiosyncrasy Analysis
Authors: Chandranath Adak, Bidyut. B. Chaudhuri, Chin-Teng Lin, Michael Blumenstein
Date: 2019-12-19 00:00:00
Tasks: Reinforcement Learning, Triplet

In this paper, we work on intra-variable handwriting, where the writing samples of an individual can vary significantly. Such within-writer variation throws a challenge for automatic writer inspection, where the state-of-the-art methods do not perform well. To deal with intra-variability, we analyze the idiosyncrasy in individual handwriting. We identify/verify the writer from highly idiosyncratic text-patches. Such patches are detected using a deep recurrent reinforcement learning-based architecture. An idiosyncratic score is assigned to every patch, which is predicted by employing deep regression analysis. For writer identification, we propose a deep neural architecture, which makes the final decision by the idiosyncratic score-induced weighted average of patch-based decisions. For writer verification, we propose two algorithms for patch-fed deep feature aggregation, which assist in authentication using a triplet network. The experiments were performed on two databases, where we obtained encouraging results.
Metamorphic Testing for Object Detection Systems
Authors: Shuai Wang, Zhendong Su
Date: 2019-12-19 00:00:00
Tasks: Autonomous Driving, Object, object-detection, Object Detection

Recent advances in deep neural networks (DNNs) have led to object detectors that can rapidly process pictures or videos, and recognize the objects that they contain. Despite the promising progress by industrial manufacturers such as Amazon and Google in commercializing deep learning-based object detection as a standard computer vision service, object detection systems - similar to traditional software - may still produce incorrect results. These errors, in turn, can lead to severe negative outcomes for the users of these object detection systems. For instance, an autonomous driving system that fails to detect pedestrians can cause accidents or even fatalities. However, principled, systematic methods for testing object detection systems do not yet exist, despite their importance. To fill this critical gap, we introduce the design and realization of MetaOD, the first metamorphic testing system for object detectors to effectively reveal erroneous detection results by commercial object detectors. To this end, we (1) synthesize natural-looking images by inserting extra object instances into background images, and (2) design metamorphic conditions asserting the equivalence of object detection results between the original and synthetic images after excluding the prediction results on the inserted objects. MetaOD is designed as a streamlined workflow that performs object extraction, selection, and insertion. Evaluated on four commercial object detection services and four pretrained models provided by the TensorFlow API, MetaOD found tens of thousands of detection defects in these object detectors. To further demonstrate the practical usage of MetaOD, we use the synthetic images that cause erroneous detection results to retrain the model. Our results show that the model performance is increased significantly, from an mAP score of 9.3 to an mAP score of 10.5.
Estudo comparativo de meta-heurísticas para problemas de colorações de grafos
Authors: Flávio José Mendes Coelho
Date: 2019-12-18 00:00:00
Tasks: Combinatorial Optimization

A classic graph coloring problem is to assign colors to vertices of any graph so that distinct colors are assigned to adjacent vertices. Optimal graph coloring colors a graph with a minimum number of colors, which is its chromatic number. Finding out the chromatic number is a combinatorial optimization problem proven to be computationally intractable, which implies that no algorithm that computes large instances of the problem in a reasonable time is known. For this reason, approximate methods and metaheuristics form a set of techniques that do not guarantee optimality but obtain good solutions in a reasonable time. This paper reports a comparative study of the Hill-Climbing, Simulated Annealing, Tabu Search, and Iterated Local Search metaheuristics for the classic graph coloring problem considering its time efficiency for processing the DSJC125 and DSJC250 instances of the DIMACS benchmark.
Design Considerations for Efficient Deep Neural Networks on Processing-in-Memory Accelerators
Authors: Tien-Ju Yang, Vivienne Sze
Date: 2019-12-18 00:00:00

This paper describes various design considerations for deep neural networks that enable them to operate efficiently and accurately on processing-in-memory accelerators. We highlight important properties of these accelerators and the resulting design considerations using experiments conducted on various state-of-the-art deep neural networks with the large-scale ImageNet dataset.
Unsupervised Adversarial Image Inpainting
Authors: Arthur Pajot, Emmanuel de Bezenac, Patrick Gallinari
Date: 2019-12-18 00:00:00
Tasks: Image Inpainting, Imputation

We consider inpainting in an unsupervised setting where there is neither access to paired nor unpaired training data. The only available information is provided by the uncomplete observations and the inpainting process statistics. In this context, an observation should give rise to several plausible reconstructions which amounts at learning a distribution over the space of reconstructed images. We model the reconstruction process by using a conditional GAN with constraints on the stochastic component that introduce an explicit dependency between this component and the generated output. This allows us sampling from the latent component in order to generate a distribution of images associated to an observation. We demonstrate the capacity of our model on several image datasets: faces (CelebA), food images (Recipe-1M) and bedrooms (LSUN Bedrooms) with different types of imputation masks. The approach yields comparable performance to model variants trained with additional supervision.