Ear Recognition Based on Deep Unsupervised Active Learning

International audienceCooperative machine learning has many applications, such as data annotation, where an initial model trained with partially labeled data is used to predict labels for unseen data continuously. Predicted labels with a low confidence value are manually revised to allow the model to be retrained with the predicted and revised data. In this paper, we propose an alternative to this approach: an initial training process called Deep Unsupervised Active Learning. Using the proposed training scheme, a classification model can incrementally acquire new knowledge during the testing phase without manual guidance or correction of decision making. The training process consists of two stages: the first stage of supervised training using a classification model, and an unsupervised active learning stage during the test phase. The labels predicted during the test phase, with high confidence, are continuously used to extend the knowledge base of the model. To optimize the proposed method, the model must have a high initial recognition rate. To this end, we exploited the Visual Geometric Group (VGG16) pre-trained model applied to three datasets: Mathematical Image Analysis (AMI), University of Science and Technology Beijing (USTB2), and Annotated Web Ears (AWE). This approach achieved impressive performance that shows a significant improvement in the recognition rate of the USTB2 dataset by coloring its images using a Generative Adversarial Network (GAN). The obtained performances are interesting compared to the current methods: the recognition rates are 100.00%, 98.33%, and 51.25% for the USTB2, AMI, and AWE datasets, respectively

Supp. Table 1

Results of angular comparisons between species PLS1 and the common PLS for each species (sample size >20), on the cephalon (top of the table) and the pygidium (bottom of the table)

Supplementary Information - cephala procrusted landmarks

Procrusted landmarks from the cephalon of each individual

Supplementary Information - pygidia procrusted landmarks

Procrusted landmarks from the pygidium of each individual

Data from: Functional integration for enrolment constrains evolutionary variation of phacopid trilobites despite developmental modularity

Modularity and integration are variational properties expressed at various levels of the biological hierarchy. Mismatches among these levels, for example developmental modules that are integrated in a functional unit, could be informative of how evolutionary processes and trade‐offs have shaped organismal morphologies as well as clade diversification. In the present study, we explored the full, integrated and modular spaces of two developmental modules in phacopid trilobites, the cephalon and the pygidium, and highlight some differences among them. Such contrasts reveal firstly that evolutionary processes operating in the modular spaces are stronger in the cephalon, probably due to a complex regime of selection related to the numerous functions ensured by this module. Secondly, we demonstrate that the same pattern of covariation is shared among species, which also differentiate along this common functional integration. This common pattern might be the result of stabilizing selection acting on the enrolment and implying a coordinate variation between the cephalon and the pygidium in a certain direction of the morphospace. Finally, we noticed that Austerops legrandi differs slightly from other species in that its integration is partly restructured in the way the two modules interact. Such a divergence can result from the involvement of the cephalon in several vital functions that may have constrained the response of the features involved in enrolment and reorganized the covariation of the pygidium with the cephalon. Therefore, it is possible that important evolutionary trade‐offs between enrolment and other functions on the cephalon might have partly shaped the diversification of trilobites

Middle Devonian trilobites of the Saoura Valley, Algeria: insights into their biodiversity and Moroccan affinities.

30 pagesInternational audienceTrilobites are important elements of the Devonian macrobenthos; some of them were collected in the Chefar el Ahmar Formation, from two sections located near Béni Abbès in the Saoura Valley (Ougarta Basin, Saharan Algeria). This formation is characterized by alternations of claystones and limestones, and it is considered to be late Emsian to early Frasnian in age. Only the lower part of this formation has yielded trilobites so far; their presence has been known for a long time. Phacopines clearly dominate the trilobite assemblages, with Austerops, Barrandeops, Chotecops and Phacops s.l. as the main genera. Two new species are described (Austerops salamandaroides sp. nov. and Phacops ouarouroutensis sp. nov.), while some other taxa are presented in open nomenclature. Comparisons are made with closely allied species. These new trilobite occurrences have been analysed in terms of their intra- and interspecific variability and biodiversity. The occurrence of Struveaspis maroccanica, previously known from the Saoura Valley, provides an early Eifelian age, which is also confirmed by the presence of trilobites Thysanopeltis and Koneprusites, and ostracods Bairdiocypris devonica and Bufina ?subovalis