In deep metric learning (DML), high-level input data are represented in a lower-level representation (embedding) space, such that samples from the same class are mapped close together, while samples from disparate classes are mapped further apart. In this lower-level representation, only a single inference sample from each known class is required to discriminate between classes accurately. The features a DML model uses to discriminate between classes and the importance of each feature in the training process are unknown. To investigate this, this study takes embeddings trained to discriminate faces (identities) and uses unsupervised clustering to identify the features involved in facial identity discrimination by examining their representation within the embedded space. This study is split into two cases; intra class sub-discrimination, where attributes that differ between a single identity are considered; such as beards and emotions; and extra class sub-discrimination, where attributes which differ between different identities/people, are considered; such as gender, skin tone and age. In the intra class scenario, the inference process distinguishes common attributes between single identities, achieving 90.0\% and 76.0\% accuracy for beards and glasses, respectively. The system can also perform extra class sub-discrimination with a high accuracy rate, notably 99.3\%, 99.3\% and 94.1\% for gender, skin tone, and age, respectively.
The purpose of this paper is to draw together in one place knowledge that is relevant to the possible role of RFID (radio frequency identification) in contractor monitoring. The paper uses multiple case studies and internet survey methods to explore several issues in RFID-enabled monitoring of contractors. It also offers some conceptual frameworks to help decision makers think through ways RFID might emerge as a contractor monitoring technology as well as some of the key reasons for using this mechanism of monitoring. The paper concludes with research challenges and key issues for practitioners. ^Åèìáëáíáçå=oÉëÉ~êÅÜ=moldo^j= do^ar^qb=pèlli=lc=_rpfkbpp=C=mr_if`=mlif`v= =-iii-k^s^i=mlpqdo^ar^qb=pèlli= THIS PAGE INTENTIONALLY LEFT BLANK ^Åèìáëáíáçå=oÉëÉ~êÅÜ=moldo^j= do^ar^qb=pèlli=lc=_rpfkbpp=C=mr_if`=mlif`v= =-iv-k^s^i=mlpqdo^ar^qb=pèlli=
Deep metric learning is a technique used in a variety of discriminative tasks to achieve zero-shot, one-shot or few-shot learning. When applied, the system learns an embedding space where a non-parametric approach, such as Knearest neighbor (KNN), can be used to discriminate features during test time.This work focuses on investigating to what extent feature information contained within this embedding space can be used to carry out sub-discrimination in the feature space. The study shows that within a discrimination embedding, the information on the salient attributes needed to solve the problem of subdiscrimination is saved within the embedding and that this inherent information can be used to carry out sub-discriminative tasks. To demonstrate this, an embedding designed initially to discriminate faces is used to differentiate several attributes such as gender, age and skin tone, without any additional training.The study is split into two study cases: intra class discrimination where all the embeddings took into consideration are from the same identity; and extra class discrimination where the embeddings represent different identities. After the study, it is shown that it is possible to infer common attributes to different identities. The system can also perform extra class sub-discrimination with
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