PlaStIL: Plastic and Stable Memory-Free Class-Incremental Learning

Plasticity and stability are needed in class-incremental learning in order to learn from new data while preserving past knowledge. Due to catastrophic forgetting, finding a compromise between these two properties is particularly challenging when no memory buffer is available. Mainstream methods need to store two deep models since they integrate new classes using fine-tuning with knowledge distillation from the previous incremental state. We propose a method which has similar number of parameters but distributes them differently in order to find a better balance between plasticity and stability. Following an approach already deployed by transfer-based incremental methods, we freeze the feature extractor after the initial state. Classes in the oldest incremental states are trained with this frozen extractor to ensure stability. Recent classes are predicted using partially fine-tuned models in order to introduce plasticity. Our proposed plasticity layer can be incorporated to any transfer-based method designed for exemplar-free incremental learning, and we apply it to two such methods. Evaluation is done with three large-scale datasets. Results show that performance gains are obtained in all tested configurations compared to existing methods

FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning

Exemplar-free class-incremental learning is very challenging due to the negative effect of catastrophic forgetting. A balance between stability and plasticity of the incremental process is needed in order to obtain good accuracy for past as well as new classes. Existing exemplar-free class-incremental methods focus either on successive fine tuning of the model, thus favoring plasticity, or on using a feature extractor fixed after the initial incremental state, thus favoring stability. We introduce a method which combines a fixed feature extractor and a pseudo-features generator to improve the stability-plasticity balance. The generator uses a simple yet effective geometric translation of new class features to create representations of past classes, made of pseudo-features. The translation of features only requires the storage of the centroid representations of past classes to produce their pseudo-features. Actual features of new classes and pseudo-features of past classes are fed into a linear classifier which is trained incrementally to discriminate between all classes. The incremental process is much faster with the proposed method compared to mainstream ones which update the entire deep model. Experiments are performed with three challenging datasets, and different incremental settings. A comparison with ten existing methods shows that our method outperforms the others in most cases

Castrum Novum (Santa Marinella, prov. de Rome)

Nel corso della campagna di scavi 2015, condotta nel mese di settembre, si è proseguito le ricerche nell’area situata all’altezza del km 64 della via Aurelia, in località « Le Guardiole ». Inoltre, in considerazione di avvenute attività di scavo abusivo e di ricerche clandestine nell’area urbana antica, sulla collina dell’antica Castrum Novum, sono stati condotti importanti interventi di emergenza e di recupero delle zone interessate da tali attività (fig. 1, Zona D). Fig. 1 – Localizzazione ..

Castrum Novum (Santa Marinella, prov. de Rome)

Au mois de septembre 2012, s’est poursuivie l’étude des ruines du balneum des Guardiole (zone A, secteurs 1 e 2 et sondage I) et des restes en mer visibles en section sur la portion de côte comprise entre « Torre Chiaruccia » et « Casale Alibrandi » (zone B). La zone A – secteur 1 : le balneum des Guardiole (responsable Sara Nardi Combescure) L’enquête conduite en septembre 2012 a été consacrée au nettoyage et à l’étude stratigraphique des murs du balneum, en particulier de la pièce 7, situé..

Castrum Novum (Santa Marinella, prov. de Rome)

Introduction(F. Enei, S. Nardi Combescure, G. Poccardi) Au cours du mois de septembre 2016, les recherches de terrain entreprises l’année précédente ont été poursuivies sur la colline du « Casale Alibrandi » (zone D) qui correspond au cœur de la colonie de Castrum Novum. Trois nouveaux sondages ont été ouverts : le sondage IV a permis de dégager une partie importante des remparts du IIIe siècle av. J. -C. ; le sondage V a intéressé des édifices relatifs à l’époque de fondation de la colonie ;..

Castrum Novum (Santa Marinella, prov. de Rome)

Zona A, settore 1 : il balneum de « le Guardiole ». Lo scavo(Sara Nardi-Combescure e David Vattier) Con il toponimo « le Guardiole » si distingue un’area, situata in corrispondenza del km 64, 4 della via Aurelia, a circa 200 m in direzione nord dell’area del « casale Alibrandi », dove è stato localizzato l’abitato di Castrum Novum (fig. 1). Fig. 1 - Castrum Novum. L’area de « Le Guardiole », prima dello sviluppo urbanistico degli anni 1960 e 1970. Da Bianchi – Giacomelli, p. 77. Stando a G. ..

Les secteurs extra-muros du castrum : zone B. Castrum Novum (Santa Marinella, prov. de Rome) : Chronique de la campagne de septembre 2017

International audienc

CASTRUM NOVUM. Storia e archeologia di una colonia romana nel territorio di Santa Marinella

International audienc

CASTRUM NOVUM. Storia e archeologia di una colonia romana nel territorio di Santa Marinella

International audienc

FeTrIL: feature translation for exemplar-free class-incremental learning

International audienceExemplar-free class-incremental learning is very challenging due to the negative effect of catastrophic forgetting. A balance between stability and plasticity of the incremental process is needed in order to obtain good accuracy for past as well as new classes. Existing exemplar-free class-incremental methods focus either on successive fine tuning of the model, thus favoring plasticity, or on using a feature extractor fixed after the initial incremental state, thus favoring stability. We introduce a method which combines a fixed feature extractor and a pseudo-features generator to improve the stability-plasticity balance. The generator uses a simple yet effective geometric translation of new class features to create representations of past classes, made of pseudo-features. The translation of features only requires the storage of the centroid representations of past classes to produce their pseudo-features. Actual features of new classes and pseudo-features of past classes are fed into a linear classifier which is trained incrementally to discriminate between all classes. The incremental process is much faster with the proposed method compared to mainstream ones which update the entire deep model. Experiments are performed with three challenging datasets, and different incremental settings. A comparison with ten existing methods shows that our method outperforms the others in most cases