pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics
Authors: Krishan Rana, Vibhavari Dasagi, Jesse Haviland, Ben Talbot, Michael Milford, Niko Sünderhauf
Date: 2021-07-21 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL), Safe Exploration

We present Bayesian Controller Fusion (BCF): a hybrid control strategy that combines the strengths of traditional hand-crafted controllers and model-free deep reinforcement learning (RL). BCF thrives in the robotics domain, where reliable but suboptimal control priors exist for many tasks, but RL from scratch remains unsafe and data-inefficient. By fusing uncertainty-aware distributional outputs from each system, BCF arbitrates control between them, exploiting their respective strengths. We study BCF on two real-world robotics tasks involving navigation in a vast and long-horizon environment, and a complex reaching task that involves manipulability maximisation. For both these domains, simple handcrafted controllers exist that can solve the task at hand in a risk-averse manner but do not necessarily exhibit the optimal solution given limitations in analytical modelling, controller miscalibration and task variation. As exploration is naturally guided by the prior in the early stages of training, BCF accelerates learning, while substantially improving beyond the performance of the control prior, as the policy gains more experience. More importantly, given the risk-aversity of the control prior, BCF ensures safe exploration and deployment, where the control prior naturally dominates the action distribution in states unknown to the policy. We additionally show BCF's applicability to the zero-shot sim-to-real setting and its ability to deal with out-of-distribution states in the real world. BCF is a promising approach towards combining the complementary strengths of deep RL and traditional robotic control, surpassing what either can achieve independently. The code and supplementary video material are made publicly available at https://krishanrana.github.io/bcf.
Improved Reinforcement Learning in Cooperative Multi-agent Environments Using Knowledge Transfer
Authors: Mahnoosh Mahdavimoghaddam, Amin Nikanjam, Monireh Abdoos
Date: 2021-07-20 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL), Transfer Learning

Nowadays, cooperative multi-agent systems are used to learn how to achieve goals in large-scale dynamic environments. However, learning in these environments is challenging: from the effect of search space size on learning time to inefficient cooperation among agents. Moreover, reinforcement learning algorithms may suffer from a long time of convergence in such environments. In this paper, a communication framework is introduced. In the proposed communication framework, agents learn to cooperate effectively and also by introduction of a new state calculation method the size of state space will decline considerably. Furthermore, a knowledge-transferring algorithm is presented to share the gained experiences among the different agents, and develop an effective knowledge-fusing mechanism to fuse the knowledge learnt utilizing the agents' own experiences with the knowledge received from other team members. Finally, the simulation results are provided to indicate the efficacy of the proposed method in the complex learning task. We have evaluated our approach on the shepherding problem and the results show that the learning process accelerates by making use of the knowledge transferring mechanism and the size of state space has declined by generating similar states based on state abstraction concept.
High-dimensional Multivariate Time Series Forecasting in IoT Applications using Embedding Non-stationary Fuzzy Time Series
Authors: Hugo Vinicius Bitencourt, Frederico Gadelha Guimarães
Date: 2021-07-20 00:00:00
Tasks: Multivariate Time Series Forecasting, Time Series, Time Series Analysis, Time Series Forecasting

In Internet of things (IoT), data is continuously recorded from different data sources and devices can suffer faults in their embedded electronics, thus leading to a high-dimensional data sets and concept drift events. Therefore, methods that are capable of high-dimensional non-stationary time series are of great value in IoT applications. Fuzzy Time Series (FTS) models stand out as data-driven non-parametric models of easy implementation and high accuracy. Unfortunately, FTS encounters difficulties when dealing with data sets of many variables and scenarios with concept drift. We present a new approach to handle high-dimensional non-stationary time series, by projecting the original high-dimensional data into a low dimensional embedding space and using FTS approach. Combining these techniques enables a better representation of the complex content of non-stationary multivariate time series and accurate forecasts. Our model is able to explain 98% of the variance and reach 11.52% of RMSE, 2.68% of MAE and 2.91% of MAPE.
Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions
Authors: Eoin Delaney, Derek Greene, Mark T. Keane
Date: 2021-07-20 00:00:00
Tasks: counterfactual, Medical Diagnosis, Out-of-Distribution Detection

Whilst an abundance of techniques have recently been proposed to generate counterfactual explanations for the predictions of opaque black-box systems, markedly less attention has been paid to exploring the uncertainty of these generated explanations. This becomes a critical issue in high-stakes scenarios, where uncertain and misleading explanations could have dire consequences (e.g., medical diagnosis and treatment planning). Moreover, it is often difficult to determine if the generated explanations are well grounded in the training data and sensitive to distributional shifts. This paper proposes several practical solutions that can be leveraged to solve these problems by establishing novel connections with other research works in explainability (e.g., trust scores) and uncertainty estimation (e.g., Monte Carlo Dropout). Two experiments demonstrate the utility of our proposed solutions.
What Do You Get When You Cross Beam Search with Nucleus Sampling?
Authors: Uri Shaham, Omer Levy
Date: 2021-07-20 00:00:00
Proceeding: insights (ACL) 2022 5
Tasks: Machine Translation, Text Generation, Translation

We combine beam search with the probabilistic pruning technique of nucleus sampling to create two deterministic nucleus search algorithms for natural language generation. The first algorithm, p-exact search, locally prunes the next-token distribution and performs an exact search over the remaining space. The second algorithm, dynamic beam search, shrinks and expands the beam size according to the entropy of the candidate's probability distribution. Despite the probabilistic intuition behind nucleus search, experiments on machine translation and summarization benchmarks show that both algorithms reach the same performance levels as standard beam search.
An Efficient Multi-objective Evolutionary Approach for Solving the Operation of Multi-Reservoir System Scheduling in Hydro-Power Plants
Authors: C. G. Marcelino, G. M. C. Leite, C. A. D. M Delgado, L. B. de Oliveira, E. F. Wanner, S. Jiménez-Fernández, S. Salcedo-Sanz
Date: 2021-07-20 00:00:00
Tasks: Evolutionary Algorithms, Scheduling

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system - a cascade-based operation scenario. For this, we propose a new mathematical modelling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of \$412,500 per month in a projection analysis carried out.
Learning MR-Sort Models from Non-Monotone Data
Authors: Pegdwende Minoungou, Vincent Mousseau, Wassila Ouerdane, Paolo Scotton
Date: 2021-07-20 00:00:00

The Majority Rule Sorting (MR-Sort) method assigns alternatives evaluated on multiple criteria to one of the predefined ordered categories. The Inverse MR-Sort problem (Inv-MR-Sort) computes MR-Sort parameters that match a dataset. Existing learning algorithms for Inv-MR-Sort consider monotone preferences on criteria. We extend this problem to the case where the preferences on criteria are not necessarily monotone, but possibly single-peaked (or single-valley). We propose a mixed-integer programming based algorithm that learns the preferences on criteria together with the other MR-Sort parameters from the training data. We investigate the performance of the algorithm using numerical experiments and we illustrate its use on a real-world case study.
Human Perception of Audio Deepfakes
Authors: Nicolas M. Müller, Karla Pizzi, Jennifer Williams
Date: 2021-07-20 00:00:00
Tasks: DeepFake Detection, Face Recognition, Face Swapping, Human Detection, object-detection, Object Detection

The recent emergence of deepfakes has brought manipulated and generated content to the forefront of machine learning research. Automatic detection of deepfakes has seen many new machine learning techniques, however, human detection capabilities are far less explored. In this paper, we present results from comparing the abilities of humans and machines for detecting audio deepfakes used to imitate someone's voice. For this, we use a web-based application framework formulated as a game. Participants were asked to distinguish between real and fake audio samples. In our experiment, 472 unique users competed against a state-of-the-art AI deepfake detection algorithm for 14912 total of rounds of the game. We find that humans and deepfake detection algorithms share similar strengths and weaknesses, both struggling to detect certain types of attacks. This is in contrast to the superhuman performance of AI in many application areas such as object detection or face recognition. Concerning human success factors, we find that IT professionals have no advantage over non-professionals but native speakers have an advantage over non-native speakers. Additionally, we find that older participants tend to be more susceptible than younger ones. These insights may be helpful when designing future cybersecurity training for humans as well as developing better detection algorithms.
CycleMLP: A MLP-like Architecture for Dense Prediction
Authors: Shoufa Chen, Enze Xie, Chongjian Ge, Runjian Chen, Ding Liang, Ping Luo
Date: 2021-07-21 00:00:00
Proceeding: ICLR 2022 4
Tasks: Image Classification, Instance Segmentation, object-detection, Object Detection, Prediction, Segmentation, Semantic Segmentation

This paper presents a simple MLP-like architecture, CycleMLP, which is a versatile backbone for visual recognition and dense predictions. As compared to modern MLP architectures, e.g., MLP-Mixer, ResMLP, and gMLP, whose architectures are correlated to image size and thus are infeasible in object detection and segmentation, CycleMLP has two advantages compared to modern approaches. (1) It can cope with various image sizes. (2) It achieves linear computational complexity to image size by using local windows. In contrast, previous MLPs have $O(N^2)$ computations due to fully spatial connections. We build a family of models which surpass existing MLPs and even state-of-the-art Transformer-based models, e.g., Swin Transformer, while using fewer parameters and FLOPs. We expand the MLP-like models' applicability, making them a versatile backbone for dense prediction tasks. CycleMLP achieves competitive results on object detection, instance segmentation, and semantic segmentation. In particular, CycleMLP-Tiny outperforms Swin-Tiny by 1.3% mIoU on ADE20K dataset with fewer FLOPs. Moreover, CycleMLP also shows excellent zero-shot robustness on ImageNet-C dataset. Code is available at https://github.com/ShoufaChen/CycleMLP.
Objective video quality metrics application to video codecs comparisons: choosing the best for subjective quality estimation
Authors: Anastasia Antsiferova, Alexander Yakovenko, Nickolay Safonov, Dmitriy Kulikov, Alexander Gushin, Dmitriy Vatolin
Date: 2021-07-21 00:00:00
Tasks: SSIM, Video Compression

Quality assessment plays a key role in creating and comparing video compression algorithms. Despite the development of a large number of new methods for assessing quality, generally accepted and well-known codecs comparisons mainly use the classical methods like PSNR, SSIM and new method VMAF. These methods can be calculated following different rules: they can use different frame-by-frame averaging techniques or different summation of color components. In this paper, a fundamental comparison of various versions of generally accepted metrics is carried out to find the most relevant and recommended versions of video quality metrics to be used in codecs comparisons. For comparison, we used a set of videos encoded with video codecs of different standards, and visual quality scores collected for the resulting set of streams since 2018 until 2021
DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation
Authors: Wentao Bao, Qi Yu, Yu Kong
Date: 2021-07-21 00:00:00
Proceeding: ICCV 2021 10
Tasks: Accident Anticipation, Decision Making

Traffic accident anticipation aims to accurately and promptly predict the occurrence of a future accident from dashcam videos, which is vital for a safety-guaranteed self-driving system. To encourage an early and accurate decision, existing approaches typically focus on capturing the cues of spatial and temporal context before a future accident occurs. However, their decision-making lacks visual explanation and ignores the dynamic interaction with the environment. In this paper, we propose Deep ReInforced accident anticipation with Visual Explanation, named DRIVE. The method simulates both the bottom-up and top-down visual attention mechanism in a dashcam observation environment so that the decision from the proposed stochastic multi-task agent can be visually explained by attentive regions. Moreover, the proposed dense anticipation reward and sparse fixation reward are effective in training the DRIVE model with our improved reinforcement learning algorithm. Experimental results show that the DRIVE model achieves state-of-the-art performance on multiple real-world traffic accident datasets. Code and pre-trained model are available at \url{https://www.rit.edu/actionlab/drive}.
3D fluorescence microscopy data synthesis for segmentation and benchmarking
Authors: Dennis Eschweiler, Malte Rethwisch, Mareike Jarchow, Simon Koppers, Johannes Stegmaier
Date: 2021-07-21 00:00:00
Tasks: Benchmarking

Automated image processing approaches are indispensable for many biomedical experiments and help to cope with the increasing amount of microscopy image data in a fast and reproducible way. Especially state-of-the-art deep learning-based approaches most often require large amounts of annotated training data to produce accurate and generalist outputs, but they are often compromised by the general lack of those annotated data sets. In this work, we propose how conditional generative adversarial networks can be utilized to generate realistic image data for 3D fluorescence microscopy from annotation masks of 3D cellular structures. In combination with mask simulation approaches, we demonstrate the generation of fully-annotated 3D microscopy data sets that we make publicly available for training or benchmarking. An additional positional conditioning of the cellular structures enables the reconstruction of position-dependent intensity characteristics and allows to generate image data of different quality levels. A patch-wise working principle and a subsequent full-size reassemble strategy is used to generate image data of arbitrary size and different organisms. We present this as a proof-of-concept for the automated generation of fully-annotated training data sets requiring only a minimum of manual interaction to alleviate the need of manual annotations.
Evidential Deep Learning for Open Set Action Recognition
Authors: Wentao Bao, Qi Yu, Yu Kong
Date: 2021-07-21 00:00:00
Proceeding: ICCV 2021 10
Tasks: Action Recognition, Deep Learning, Open Set Action Recognition, Open Set Learning, Out-of-Distribution Detection

In a real-world scenario, human actions are typically out of the distribution from training data, which requires a model to both recognize the known actions and reject the unknown. Different from image data, video actions are more challenging to be recognized in an open-set setting due to the uncertain temporal dynamics and static bias of human actions. In this paper, we propose a Deep Evidential Action Recognition (DEAR) method to recognize actions in an open testing set. Specifically, we formulate the action recognition problem from the evidential deep learning (EDL) perspective and propose a novel model calibration method to regularize the EDL training. Besides, to mitigate the static bias of video representation, we propose a plug-and-play module to debias the learned representation through contrastive learning. Experimental results show that our DEAR method achieves consistent performance gain on multiple mainstream action recognition models and benchmarks. Code and pre-trained models are available at {\small{\url{https://www.rit.edu/actionlab/dear}}}.
AUGCO: Augmentation Consistency-guided Self-training for Source-free Domain Adaptive Semantic Segmentation
Authors: Viraj Prabhu, Shivam Khare, Deeksha Kartik, Judy Hoffman
Date: 2021-07-21 00:00:00
Tasks: Domain Adaptation, Segmentation, Semantic Segmentation, Source-Free Domain Adaptation

Most modern approaches for domain adaptive semantic segmentation rely on continued access to source data during adaptation, which may be infeasible due to computational or privacy constraints. We focus on source-free domain adaptation for semantic segmentation, wherein a source model must adapt itself to a new target domain given only unlabeled target data. We propose Augmentation Consistency-guided Self-training (AUGCO), a source-free adaptation algorithm that uses the model's pixel-level predictive consistency across diverse, automatically generated views of each target image along with model confidence to identify reliable pixel predictions, and selectively self-trains on those. AUGCO achieves state-of-the-art results for source-free adaptation on 3 standard benchmarks for semantic segmentation, all within a simple to implement and fast to converge method.
Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentation
Authors: Shuailin Li, Zhitong Gao, Xuming He
Date: 2021-07-21 00:00:00
Tasks: Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation, Superpixels

Learning segmentation from noisy labels is an important task for medical image analysis due to the difficulty in acquiring highquality annotations. Most existing methods neglect the pixel correlation and structural prior in segmentation, often producing noisy predictions around object boundaries. To address this, we adopt a superpixel representation and develop a robust iterative learning strategy that combines noise-aware training of segmentation network and noisy label refinement, both guided by the superpixels. This design enables us to exploit the structural constraints in segmentation labels and effectively mitigate the impact of label noise in learning. Experiments on two benchmarks show that our method outperforms recent state-of-the-art approaches, and achieves superior robustness in a wide range of label noises. Code is available at https://github.com/gaozhitong/SP_guided_Noisy_Label_Seg.
HistoCartography: A Toolkit for Graph Analytics in Digital Pathology
Authors: Guillaume Jaume, Pushpak Pati, Valentin Anklin, Antonio Foncubierta, Maria Gabrani
Date: 2021-07-21 00:00:00
Proceeding: MICCAI Workshop COMPAY 2021 9
Tasks: BIG-bench Machine Learning, Translation

Advances in entity-graph based analysis of histopathology images have brought in a new paradigm to describe tissue composition, and learn the tissue structure-to-function relationship. Entity-graphs offer flexible and scalable representations to characterize tissue organization, while allowing the incorporation of prior pathological knowledge to further support model interpretability and explainability. However, entity-graph analysis requires prerequisites for image-to-graph translation and knowledge of state-of-the-art machine learning algorithms applied to graph-structured data, which can potentially hinder their adoption. In this work, we aim to alleviate these issues by developing HistoCartography, a standardized python API with necessary preprocessing, machine learning and explainability tools to facilitate graph-analytics in computational pathology. Further, we have benchmarked the computational time and performance on multiple datasets across different imaging types and histopathology tasks to highlight the applicability of the API for building computational pathology workflows.
From Single to Multiple: Leveraging Multi-level Prediction Spaces for Video Forecasting
Authors: Mengcheng Lan, Shuliang Ning, Yanran Li, Qian Chen, Xunlai Chen, Xiaoguang Han, Shuguang Cui
Date: 2021-07-21 00:00:00
Tasks: Prediction, Video Forecasting, Video Prediction

Despite video forecasting has been a widely explored topic in recent years, the mainstream of the existing work still limits their models with a single prediction space but completely neglects the way to leverage their model with multi-prediction spaces. This work fills this gap. For the first time, we deeply study numerous strategies to perform video forecasting in multi-prediction spaces and fuse their results together to boost performance. The prediction in the pixel space usually lacks the ability to preserve the semantic and structure content of the video however the prediction in the high-level feature space is prone to generate errors in the reduction and recovering process. Therefore, we build a recurrent connection between different feature spaces and incorporate their generations in the upsampling process. Rather surprisingly, this simple idea yields a much more significant performance boost than PhyDNet (performance improved by 32.1% MAE on MNIST-2 dataset, and 21.4% MAE on KTH dataset). Both qualitative and quantitative evaluations on four datasets demonstrate the generalization ability and effectiveness of our approach. We show that our model significantly reduces the troublesome distortions and blurry artifacts and brings remarkable improvements to the accuracy in long term video prediction. The code will be released soon.