32,227 research outputs found
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation
In the field of domain adaptation, a trade-off exists between the model
performance and the number of target domain annotations. Active learning,
maximizing model performance with few informative labeled data, comes in handy
for such a scenario. In this work, we present D2ADA, a general active domain
adaptation framework for semantic segmentation. To adapt the model to the
target domain with minimum queried labels, we propose acquiring labels of the
samples with high probability density in the target domain yet with low
probability density in the source domain, complementary to the existing source
domain labeled data. To further facilitate labeling efficiency, we design a
dynamic scheduling policy to adjust the labeling budgets between domain
exploration and model uncertainty over time. Extensive experiments show that
our method outperforms existing active learning and domain adaptation baselines
on two benchmarks, GTA5 -> Cityscapes and SYNTHIA -> Cityscapes. With less than
5% target domain annotations, our method reaches comparable results with that
of full supervision.Comment: 14 pages, 5 figure
Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis
Operators from various industries have been pushing the adoption of wireless
sensing nodes for industrial monitoring, and such efforts have produced
sizeable condition monitoring datasets that can be used to build diagnosis
algorithms capable of warning maintenance engineers of impending failure or
identifying current system health conditions. However, single operators may not
have sufficiently large fleets of systems or component units to collect
sufficient data to develop data-driven algorithms. Collecting a satisfactory
quantity of fault patterns for safety-critical systems is particularly
difficult due to the rarity of faults. Federated learning (FL) has emerged as a
promising solution to leverage datasets from multiple operators to train a
decentralized asset fault diagnosis model while maintaining data
confidentiality. However, there are still considerable obstacles to overcome
when it comes to optimizing the federation strategy without leaking sensitive
data and addressing the issue of client dataset heterogeneity. This is
particularly prevalent in fault diagnosis applications due to the high
diversity of operating conditions and system configurations. To address these
two challenges, we propose a novel clustering-based FL algorithm where clients
are clustered for federating based on dataset similarity. To quantify dataset
similarity between clients without explicitly sharing data, each client sets
aside a local test dataset and evaluates the other clients' model prediction
accuracy and uncertainty on this test dataset. Clients are then clustered for
FL based on relative prediction accuracy and uncertainty
Data Optimization in Deep Learning: A Survey
Large-scale, high-quality data are considered an essential factor for the
successful application of many deep learning techniques. Meanwhile, numerous
real-world deep learning tasks still have to contend with the lack of
sufficient amounts of high-quality data. Additionally, issues such as model
robustness, fairness, and trustworthiness are also closely related to training
data. Consequently, a huge number of studies in the existing literature have
focused on the data aspect in deep learning tasks. Some typical data
optimization techniques include data augmentation, logit perturbation, sample
weighting, and data condensation. These techniques usually come from different
deep learning divisions and their theoretical inspirations or heuristic
motivations may seem unrelated to each other. This study aims to organize a
wide range of existing data optimization methodologies for deep learning from
the previous literature, and makes the effort to construct a comprehensive
taxonomy for them. The constructed taxonomy considers the diversity of split
dimensions, and deep sub-taxonomies are constructed for each dimension. On the
basis of the taxonomy, connections among the extensive data optimization
methods for deep learning are built in terms of four aspects. We probe into
rendering several promising and interesting future directions. The constructed
taxonomy and the revealed connections will enlighten the better understanding
of existing methods and the design of novel data optimization techniques.
Furthermore, our aspiration for this survey is to promote data optimization as
an independent subdivision of deep learning. A curated, up-to-date list of
resources related to data optimization in deep learning is available at
\url{https://github.com/YaoRujing/Data-Optimization}
VR-PMS: a new approach for performance measurement and management of industrial systems
A new performance measurement and management framework based on value and risk is proposed. The proposed framework is applied to the modelling and evaluation of the a priori performance evaluation of manufacturing processes and to deciding on their alternatives. For this reason, it consistently integrates concepts relevant to objectives, activity, and risk in a single framework comprising a conceptual value/risk model, and it conceptualises the idea of value- and risk based performance management in a process context. In addition, a methodological framework is developed to provide guidelines for the decision-makers or performance evaluators of the processes. To facilitate the performance measurement and management process, this latter framework is organized in four phases: context establishment, performance modelling, performance assessment, and decision-making. Each phase of the framework is then instrumented with state of-the-art quantitative analysis tools and methods. For process design and evaluation, the deliverable of the value- and risk-based performance measurement and management system (VR-PMS) is a set of ranked solutions (i.e. alternative business processes) evaluated against the developed value and risk indicators. The proposed VR-PMS is illustrated with a case study from discrete parts manufacturing but is indeed applicable to a wide range of processes or systems
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