2,461 research outputs found
SEA++: Multi-Graph-based High-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA) methods have been successful in reducing
label dependency by minimizing the domain discrepancy between a labeled source
domain and an unlabeled target domain. However, these methods face challenges
when dealing with Multivariate Time-Series (MTS) data. MTS data typically
consist of multiple sensors, each with its own unique distribution. This
characteristic makes it hard to adapt existing UDA methods, which mainly focus
on aligning global features while overlooking the distribution discrepancies at
the sensor level, to reduce domain discrepancies for MTS data. To address this
issue, a practical domain adaptation scenario is formulated as Multivariate
Time-Series Unsupervised Domain Adaptation (MTS-UDA). In this paper, we propose
SEnsor Alignment (SEA) for MTS-UDA, aiming to reduce domain discrepancy at both
the local and global sensor levels. At the local sensor level, we design
endo-feature alignment, which aligns sensor features and their correlations
across domains. To reduce domain discrepancy at the global sensor level, we
design exo-feature alignment that enforces restrictions on global sensor
features. We further extend SEA to SEA++ by enhancing the endo-feature
alignment. Particularly, we incorporate multi-graph-based high-order alignment
for both sensor features and their correlations. Extensive empirical results
have demonstrated the state-of-the-art performance of our SEA and SEA++ on
public MTS datasets for MTS-UDA
Recommended from our members
Utilizing Graph Structure for Machine Learning
The information age has led to an explosion in the size and availability of data. This data often exhibits graph-structure that is either explicitly defined, as in the web of a social network, or is implicitly defined and can be determined by measuring similarity between objects. Utilizing this graph-structure allows for the design of machine learning algorithms that reflect not only the attributes of individual objects but their relationships to every other object in the domain as well. This thesis investigates three machine learning problems and proposes novel methods that leverage the graph-structure inherent in the tasks. Quantum walk neural networks are classical neural nets that use quantum random walks for classifying and regressing on graphs. Asymmetric directed node embeddings are another neural network architecture designed to embed the nodes of a directed graph into a vector space. Filtered manifold alignment is a novel two-step approach to domain adaptation
Relating Events and Frames Based on Self-Supervised Learning and Uncorrelated Conditioning for Unsupervised Domain Adaptation
Event-based cameras provide accurate and high temporal resolution
measurements for performing computer vision tasks in challenging scenarios,
such as high-dynamic range environments and fast-motion maneuvers. Despite
their advantages, utilizing deep learning for event-based vision encounters a
significant obstacle due to the scarcity of annotated data caused by the
relatively recent emergence of event-based cameras. To overcome this
limitation, leveraging the knowledge available from annotated data obtained
with conventional frame-based cameras presents an effective solution based on
unsupervised domain adaptation. We propose a new algorithm tailored for
adapting a deep neural network trained on annotated frame-based data to
generalize well on event-based unannotated data. Our approach incorporates
uncorrelated conditioning and self-supervised learning in an adversarial
learning scheme to close the gap between the two source and target domains. By
applying self-supervised learning, the algorithm learns to align the
representations of event-based data with those from frame-based camera data,
thereby facilitating knowledge transfer.Furthermore, the inclusion of
uncorrelated conditioning ensures that the adapted model effectively
distinguishes between event-based and conventional data, enhancing its ability
to classify event-based images accurately.Through empirical experimentation and
evaluation, we demonstrate that our algorithm surpasses existing approaches
designed for the same purpose using two benchmarks. The superior performance of
our solution is attributed to its ability to effectively utilize annotated data
from frame-based cameras and transfer the acquired knowledge to the event-based
vision domain
Deep Feature Learning and Adaptation for Computer Vision
We are living in times when a revolution of deep learning is taking place. In general, deep learning models have a backbone that extracts features from the input data followed by task-specific layers, e.g. for classification. This dissertation proposes various deep feature extraction and adaptation methods to improve task-specific learning, such as visual re-identification, tracking, and domain adaptation. The vehicle re-identification (VRID) task requires identifying a given vehicle among a set of vehicles under variations in viewpoint, illumination, partial occlusion, and background clutter. We propose a novel local graph aggregation module for feature extraction to improve VRID performance. We also utilize a class-balanced loss to compensate for the unbalanced class distribution in the training dataset. Overall, our framework achieves state-of-the-art (SOTA) performance in multiple VRID benchmarks. We further extend our VRID method for visual object tracking under occlusion conditions. We motivate visual object tracking from aerial platforms by conducting a benchmarking of tracking methods on aerial datasets. Our study reveals that the current techniques have limited capabilities to re-identify objects when fully occluded or out of view. The Siamese network based trackers perform well compared to others in overall tracking performance. We utilize our VRID work in visual object tracking and propose Siam-ReID, a novel tracking method using a Siamese network and VRID technique. In another approach, we propose SiamGauss, a novel Siamese network with a Gaussian Head for improved confuser suppression and real time performance. Our approach achieves SOTA performance on aerial visual object tracking datasets. A related area of research is developing deep learning based domain adaptation techniques. We propose continual unsupervised domain adaptation, a novel paradigm for domain adaptation in data constrained environments. We show that existing works fail to generalize when the target domain data are acquired in small batches. We propose to use a buffer to store samples that are previously seen by the network and a novel loss function to improve the performance of continual domain adaptation. We further extend our continual unsupervised domain adaptation research for gradually varying domains. Our method outperforms several SOTA methods even though they have the entire domain data available during adaptation
Concept Drift Detection in Data Stream Mining: The Review of Contemporary Literature
Mining process such as classification, clustering of progressive or dynamic data is a critical objective of the information retrieval and knowledge discovery; in particular, it is more sensitive in data stream mining models due to the possibility of significant change in the type and dimensionality of the data over a period. The influence of these changes over the mining process termed as concept drift. The concept drift that depict often in streaming data causes unbalanced performance of the mining models adapted. Hence, it is obvious to boost the mining models to predict and analyse the concept drift to achieve the performance at par best. The contemporary literature evinced significant contributions to handle the concept drift, which fall in to supervised, unsupervised learning, and statistical assessment approaches. This manuscript contributes the detailed review of the contemporary concept-drift detection models depicted in recent literature. The contribution of the manuscript includes the nomenclature of the concept drift models and their impact of imbalanced data tuples
- …