Despite the power of deep neural networks for a wide range of tasks, an overconfident prediction issue has limited their practical use in many safety-critical applications. Many recent works have been proposed to mitigate this issue, but most of them require either additional computational costs in training and/or inference phases or customized architectures to output confidence estimates separately. In this paper, we propose a method of training deep neural networks with a novel loss function, named Correctness Ranking Loss, which regularizes class probabilities explicitly to be better confidence estimates in terms of ordinal ranking according to confidence. The proposed method is easy to implement and can be applied to the existing architectures without any modification. Also, it has almost the same computational costs for training as conventional deep classifiers and outputs reliable predictions by a single inference. Extensive experimental results on classification benchmark datasets indicate that the proposed method helps networks to produce well-ranked confidence estimates. We also demonstrate that it is effective for the tasks closely related to confidence estimation, out-of-distribution detection and active learning.
We propose a generic framework to calibrate accuracy and confidence of a prediction in deep neural networks through stochastic inferences. We interpret stochastic regularization using a Bayesian model, and analyze the relation between predictive uncertainty of networks and variance of the prediction scores obtained by stochastic inferences for a single example. Our empirical study shows that the accuracy and the score of a prediction are highly correlated with the variance of multiple stochastic inferences given by stochastic depth or dropout. Motivated by this observation, we design a novel v…
Beheshti University（贝赫什提大学）；University of Tehran（德黑兰大学）；Isfahan University of Technology（伊斯法罕理工大学）；Deakin University（迪肯大学）
Deep learning (DL) models have received particular attention in medical imaging due to their promising pattern recognition capabilities. However, Deep Neural Networks (DNNs) require a huge amount of data, and because of the lack of sufficient data in this field, transfer learning can be a great solution. DNNs used for disease diagnosis meticulously concentrate on improving the accuracy of predictions without providing a figure about their confidence of predictions. Knowing how much a DNN model is confident in a computer-aided diagnosis model is necessary for gaining clinicians' confidence and …
Trust-Aware Predictive Emissions Monitoring for Gas Turbine Fleets with Limited Labelled Data
School of Natural and Computing Sciences University of Aberdeen（自然与计算科学学院 布拉德福德大学）；School of Computing Sciences University of East Anglia（计算科学学院 东安格利亚大学）；Siemens Energy Industrial Turbomachinery Ltd.（西门子能源工业涡轮机有限公司）；Department of Physics and Technology UiT The Arctic University of Norway（物理与技术系 UiT 北极大学）
CAAL: Confidence-Aware Active Learning for Heteroscedastic Atmospheric Regression
Department of Computer Science The University of Manchester Manchester Greater Manchester UK（计算机科学系 伦敦大学曼彻斯特分校 曼彻斯特 大曼彻斯特 UK）；Department of Atmospheric Sciences Zhejiang University Hangzhou China（大气科学系 浙江大学 杭州 中国）；Yunqi Academy of Engineering Hangzhou China（云启工程学院 杭州 中国）；The University of Manchester（伦敦大学曼彻斯特分校）；Zhejiang University（浙江大学）；Yunqi Academy of Engineering（云启工程学院）
Deep metric learning has gained promising improvement in recent years following the success of deep learning. It has been successfully applied to problems in few-shot learning, image retrieval, and open-set classifications. However, measuring the confidence of a deep metric learning model and identifying unreliable predictions is still an open challenge. This paper focuses on defining a calibrated and interpretable confidence metric that closely reflects its classification accuracy. While performing similarity comparison directly in the latent space using the learned distance metric, our appro…
We propose an algorithm combining calibrated prediction and generalization bounds from learning theory to construct confidence sets for deep neural networks with PAC guarantees---i.e., the confidence set for a given input contains the true label with high probability. We demonstrate how our approach can be used to construct PAC confidence sets on ResNet for ImageNet, a visual object tracking model, and a dynamics model for the half-cheetah reinforcement learning problem.
Reliably assessing model confidence in deep learning and predicting errors likely to be made are key elements in providing safety for model deployment, in particular for applications with dire consequences. In this paper, it is first shown that uncertainty-aware deep Dirichlet neural networks provide an improved separation between the confidence of correct and incorrect predictions in the true class probability (TCP) metric. Second, as the true class is unknown at test time, a new criterion is proposed for learning the true class probability by matching prediction confidence scores while takin…
CAD: Confidence-Aware Adaptive Displacement for Semi-Supervised Medical Image Segmentation
College of Information and Intelligence, Hunan Agricultural University（湖南农业大学信息与智能科学学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Osaka Prefecture University（大阪府立大学）
Not only correctness but also self-confidence play an important role in improving the quality of knowledge. Undesirable situations such as confident incorrect and unconfident correct knowledge prevent learners from revising their knowledge because it is not always easy for them to perceive the situations. To solve this problem, we propose a system that estimates self-confidence while solving multiple-choice questions by eye tracking and gives feedback about which question should be reviewed carefully. We report the results of three studies measuring its effectiveness. (1) On a well-controlled …
Most existing imitation learning approaches assume the demonstrations are drawn from experts who are optimal, but relaxing this assumption enables us to use a wider range of data. Standard imitation learning may learn a suboptimal policy from demonstrations with varying optimality. Prior works use confidence scores or rankings to capture beneficial information from demonstrations with varying optimality, but they suffer from many limitations, e.g., manually annotated confidence scores or high average optimality of demonstrations. In this paper, we propose a general framework to learn from demo…
Knowledge distillation is initially introduced to utilize additional supervision from a single teacher model for the student model training. To boost the student performance, some recent variants attempt to exploit diverse knowledge sources from multiple teachers. However, existing studies mainly integrate knowledge from diverse sources by averaging over multiple teacher predictions or combining them using other various label-free strategies, which may mislead student in the presence of low-quality teacher predictions. To tackle this problem, we propose Confidence-Aware Multi-teacher Knowledge…
CAST: Cluster-Aware Self-Training for Tabular Data via Reliable Confidence
School of Computer Science and Engineering, Pusan National University（釜山国立大学计算机科学与工程学院）；Center for Artificial Intelligence Research, Pusan National University（釜山国立大学人工智能研究中心）
Vector Institute for Artificial Intelligence（向量人工智能研究所）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Huawei Technologies Canada（华为技术加拿大公司）；Cheriton School of Computer Science, University of Waterloo（滑铁卢大学谢里敦计算机科学学院）
Deep learning architectures have proved versatile in a number of drug discovery applications, including the modelling of in vitro compound activity. While controlling for prediction confidence is essential to increase the trust, interpretability and usefulness of virtual screening models in drug discovery, techniques to estimate the reliability of the predictions generated with deep learning networks remain largely underexplored. Here, we present Deep Confidence, a framework to compute valid and efficient confidence intervals for individual predictions using the deep learning technique Snapsho…
Imperial College London（帝国理工学院）；Loughborough University（拉夫堡大学）
In recent years, the deployment of large-scale Internet of Things (IoT) applications has given rise to edge federations that seamlessly interconnect and leverage resources from multiple edge service providers. The requirement of supporting both latency-sensitive and compute-intensive IoT tasks necessitates service resilience, especially for the broker nodes in typical broker-worker deployment designs. Existing fault-tolerance or resilience schemes often lack robustness and generalization capability in non-stationary workload settings. This is typically due to the expensive periodic fine-tuning…
Beijing University of Posts and Telecommunications（北京邮电大学）；ByteDance（字节跳动）；USTC（中国科学技术大学）；Beijing Key Laboratory of Network System and Network Culture（北京网络系统与网络文化重点实验室）；Key Laboratory of Interactive Technology and Experience System, Ministry of Culture and Tourism（文化和旅游部互动技术与体验系统重点实验室）；Zhongguancun Academy（中关村科学城）
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；Peking University（北京大学）
The learned policy of model-free offline reinforcement learning (RL) methods is often constrained to stay within the support of datasets to avoid possible dangerous out-of-distribution actions or states, making it challenging to handle out-of-support region. Model-based RL methods offer a richer dataset and benefit generalization by generating imaginary trajectories with either trained forward or reverse dynamics model. However, the imagined transitions may be inaccurate, thus downgrading the performance of the underlying offline RL method. In this paper, we propose to augment the offline data…
Tsinghua University（清华大学）；University of Washington（华盛顿大学）
This paper presents new \emph{variance-aware} confidence sets for linear bandits and linear mixture Markov Decision Processes (MDPs). With the new confidence sets, we obtain the follow regret bounds: For linear bandits, we obtain an $\tilde{O}(poly(d)\sqrt{1 + \sum_{k=1}^{K}σ_k^2})$ data-dependent regret bound, where $d$ is the feature dimension, $K$ is the number of rounds, and $σ_k^2$ is the \emph{unknown} variance of the reward at the $k$-th round. This is the first regret bound that only scales with the variance and the dimension but \emph{no explicit polynomial dependency on $K$}. When va…
Ranking-Aware Calibration for Reliable Multimodal Reinforcement Learning
Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Center, Tsinghua-Bosch Joint ML Center, THBI Lab, Tsinghua University, Beijing（计算机科学与技术系，人工智能研究院，BNRist中心，清华大学-博世联合机器学习中心，THBI实验室，清华大学，北京）；Dept. of Automation, Tsinghua University, Beijing（自动化系，清华大学，北京）
CoCoA-Mix: Confusion-and-Confidence-Aware Mixture Model for Context Optimization
Urban Robotics Lab, School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Republic of Korea（乌尔班机器人实验室，电气工程学院，韩国科学技术院，大韩民国）
CARGO: A Framework for Confidence-Aware Routing of Large Language Models
Department of Computer Science and Engineering, Oakland University（计算机科学与工程系，奥克兰大学）；University of North Carolina Wilmington（北卡罗来纳大学 Wilmington分校）；Mediterranean Institute of Technology (MEDTECH)（地中海技术研究所（MEDTECH））
Confidence-aware Contrastive Learning for Selective Classification
National Key Laboratory for Novel Software Technology（计算机软件新技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；College of Computer Science and Software Engineering, Hohai University（河海大学计算机与软件学院）；Key Laboratory of Water Big Data Technology of Ministry of Water Resources（水利部水大数据技术重点实验室）
University of Campinas（坎皮纳斯大学）；Utrecht University（乌得勒支大学）
Training deep neural networks is challenging when large and annotated datasets are unavailable. Extensive manual annotation of data samples is time-consuming, expensive, and error-prone, notably when it needs to be done by experts. To address this issue, increased attention has been devoted to techniques that propagate uncertain labels (also called pseudo labels) to large amounts of unsupervised samples and use them for training the model. However, these techniques still need hundreds of supervised samples per class in the training set and a validation set with extra supervised samples to tune…
School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；Hebei Key Laboratory of Big Data Computing（河北省大数据计算重点实验室）；School of Economics and Management, Hebei University of Technology（河北工业大学经济管理学院）；State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology（河北工业大学省部共建电工装备可靠性与智能化国家重点实验室）；Alibaba Group（阿里巴巴集团）
As a deep learning model, deep confidence screening forest (gcForestcs) has achieved great success in various applications. Compared with the traditional deep forest approach, gcForestcs effectively reduces the high time cost by passing some instances in the high-confidence region directly to the final stage. However, there is a group of instances with low accuracy in the high-confidence region, which are called mis-partitioned instances. To find these mis-partitioned instances, this paper proposes a deep binning confidence screening forest (DBC-Forest) model, which packs all instances into bi…
Breaking the Prototype Bias Loop: Confidence-Aware Federated Contrastive Learning for Highly Imbalanced Clients
Key Laboratory of Water Big Data Technology of Ministry of Water Resources, College of Computer Science and Software Engineering, Hohai University, Nanjing, China（水利部水大数据技术重点实验室，计算机科学与软件工程学院，河海大学，南京，中国）；Department of Computer Science, City University of Hong Kong, Hong Kong, China（计算机科学系，香港城市大学，香港，中国）；State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China（新型软件技术国家重点实验室，南京大学，南京，中国）；School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China（人工智能与信息技术学院，南京中医药大学，南京，中国）
C$^2$GSPG: Confidence-calibrated Group Sequence Policy Gradient towards Self-aware Reasoning
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高陵人工智能学院）；Tsinghua University（清华大学）；Beijing Key Laboratory of Research on Large Models（北京大模型研究关键实验室）；Engineering Research Center of Next-Generation Intelligent Search（下一代智能搜索工程研究中心）
MDQEC-QAS: Meta-Decoding for Quantum Error Correction with Hardware-Aware VQC Search and Confidence-Gated Recovery
Department of Physics, Indian Institute of Technology (BHU)（印度理工学院（BHU）物理系）；eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD)（纽约大学阿布扎克分校（NYUAD）工程学院eBRAIN实验室）；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD（纽约大学阿布扎克分校（NYUAD）量子与拓扑系统中心（CQTS））
Automated Data Enrichment using Confidence-Aware Fine-Grained Debate among Open-Source LLMs for Mental Health and Online Safety
University of Southampton, UK（英国南安普顿大学）；Queen Mary University of London, UK（伦敦大学玛丽女王学院）；The Alan Turing Institute, UK（阿兰·图灵研究所）；Bar Ilan University, Israel（巴伊兰大学）
