Results 41 to 50 of about 762,608 (309)
, 2022 The DARPA Lifelong Learning Machines (L2M) program seeks to yield advances in artificial intelligence (AI) systems so that they are capable of learning (and improving) continuously, leveraging data on one task to improve performance on another, and doing so in a computationally sustainable way.
New, Alexander +3 more
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metric-learn: Metric Learning Algorithms in Python
, 2019 metric-learn is an open source Python package implementing supervised and weakly-supervised distance metric learning algorithms. As part of scikit-learn-contrib, it provides a unified interface compatible with scikit-learn which allows to easily perform cross-validation, model selection, and pipelining with other machine learning estimators.
de Vazelhes, William +4 more
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Deep Transform and Metric Learning Networks [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 Accepted by ICASSP 2021.
Tang, Wen +3 more
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Attribute-enhanced metric learning for face retrieval
EURASIP Journal on Image and Video Processing, 2018 Metric learning is a significant factor for media retrieval. In this paper, we propose an attribute label enhanced metric learning model to assist face image retrieval.
Yuchun Fang, Qiulong Yuan
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Adversarial Similarity Metric Learning for Kinship Verification
IEEE Access, 2019 Given a pair of facial images, it is an interesting yet challenging problem to determine if there is a kin relation between them. Recent research on that topic has made encouraging progress by learning a kin similarity metric from kinship data.
Zeqiang Wei +4 more
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Research Progress on Few-Shot Learning for Remote Sensing Image Interpretation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 The rapid development of deep learning brings effective solutions for remote sensing image interpretation. Training deep neural network models usually require a large number of manually labeled samples. However, there is a limitation to obtain sufficient
Xian Sun +5 more
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Multi-Sensors System and Deep Learning Models for Object Tracking
Sensors, 2023 Autonomous navigation relies on the crucial aspect of perceiving the environment to ensure the safe navigation of an autonomous platform, taking into consideration surrounding objects and their potential movements. Consequently, a fundamental requirement
Ghina El Natour +2 more
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Robustness and generalization for metric learning [PDF]
Neurocomputing, 2015 Metric learning has attracted a lot of interest over the last decade, but the generalization ability of such methods has not been thoroughly studied. In this paper, we introduce an adaptation of the notion of algorithmic robustness (previously introduced by Xu and Mannor) that can be used to derive generalization bounds for metric learning.
Bellet, Aurélien, Habrard, Amaury
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FRDet: Few‐shot object detection via feature reconstruction
IET Image Processing, 2023 State‐of‐the‐art object detection models rely on large‐scale datasets for training to achieve good precision. Without sufficient samples, the model can suffer from severe overfitting.
Zhihao Chen +4 more
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Information-theoretic metric learning [PDF]
Proceedings of the 24th international conference on Machine learning, 2007 In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the distance function. We express this problem as a particular Bregman optimization problem---that of minimizing
Davis, J. +4 more
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