1,721,137 research outputs found
Le Maglie scappate: nota su determinismo e indeterminismo nella fisica moderna
Get PDFIn the source of the ancient science as well as in the classica physics there is an unexpressed principle, for which the objects of knowledge are required to be completely and invariably determined. Contemporary physics, with quantum theory but especialy in the thermodynamics of irreversible processes and dissipative structures, seems to make into account as well vague determinations and weak stability
Addressing Distributional Shift challenges in Computer Vision for Real-World Applications
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Rethinking Domain Generalization Baselines
Get PDFDespite being very powerful in standard learning settings, deep learning models can be extremely brittle when deployed in scenarios different from those on which they were trained. Domain generalization methods investigate this problem and data augmentation strategies have shown to be helpful tools to increase data variability, supporting model robustness across domains. In our work we focus on style transfer data augmentation and we present how it can be implemented with a simple and inexpensive strategy to improve generalization. Moreover, we analyze the behavior of current state of the art domain generalization methods when integrated with this augmentation solution: our thorough experimental evaluation shows that their original effect almost always disappears with respect to the augmented baseline. This issue open new scenarios for domain generalization research, highlighting the need of novel methods properly able to take advantage of the introduced data variability
Foundation Models and Fine-Tuning: A Benchmark for Out of Distribution Detection
Get PDFThe rise of foundation models is pushing Computer Vision research towards a paradigm shift, in the wake of what already happened in the Natural Language Processing field. These models, trained at scale on huge data collections, provide high-quality representations that generalize well enough to be applied directly to downstream tasks, often outperforming task-specific models. The Out Of Distribution (OOD) detection problem, which involves the ability to recognize when test samples come from a previously unseen semantic category, represents one of the research fields in which this paradigm shift could have the greatest impact. However, existing testbeds are limited in scale and scope and get easily saturated when adopting foundation-based pretrainings. With this work, we introduce a new benchmark covering realistic yet harder OOD detection tasks to properly assess the performance of large pretrained models. We design an experimental framework to analyze specific choices in the model learning and use (which dataset, pretraining objective, OOD scoring function) and extensively evaluate the comparison to standard approaches that leverage a training phase on the available In Distribution (ID) data. The results highlight the actual performance benefits of leveraging foundation models in this context without any further learning effort, and identify situations where task-specific fine-tuning remains the best choice
3DOS: Towards 3D Open Set Learning - Benchmarking and Understanding Semantic Novelty Detection on Point Clouds
No full textIn recent years there has been significant progress in the field of 3D learning on classification, detection and segmentation problems. The vast majority of the existing studies focus on canonical closed-set conditions, neglecting the intrinsic open nature of the real-world. This limits the abilities of robots and autonomous systems involved in safety-critical applications that require managing novel and unknown signals. In this context exploiting 3D data can be a valuable asset since it provides rich information about the geometry of perceived objects and scenes. With this paper we provide the first broad study on 3D Open Set learning. We introduce 3DOS: a novel testbed for semantic novelty detection that considers several settings with increasing difficulties in terms of semantic (category) shift, and covers both in-domain (synthetic-to-synthetic, real-to-real) and cross-domain (synthetic-to-real) scenarios. Moreover, we investigate the related 2D Open Set literature to understand if and how its recent improvements are effective on 3D data. Our extensive benchmark positions several algorithms in the same coherent picture, revealing their strengths and limitations. The results of our analysis may serve as a reliable foothold for future tailored 3D Open Set methods
A non linear modification of Wood's equation fitted to lactation curves of Sardinian dairy ewes
No full textEnzymes (Isoenzyme system) as homreostatic mechanism the isoenzyme (ADA2) of adenosine deaminase of human monocytes-macrophages as a regulator of the 2'deoxinadenosine
No full textDomain Generalization vs Data Augmentation: An Unbiased Perspective
Get PDFIn domain generalization the target domain is not known at training time. We show that a style transfer based data augmentation strategy can be implemented easily and outperforms the current state of the art domain generalization methods. Moreover, we observe that those methods, even if combined with the described data augmentation, do not take advantage of it, indicating the need of new generalization solutions
Mixed linear model analysis of factors affecting the evolution of milk electrical conductivity along lactation in dairy cattle
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