Contemporary Art Authentication With Large-Scale Classification

Art authentication is the process of identifying the artist who created a piece of artwork and is manifested through events of provenance, such as art gallery exhibitions and financial transactions. Art authentication has visual influence via the uniqueness of the artist’s style in contrast to the style of another artist. The significance of this contrast is proportional to the number of artists involved and the degree of uniqueness of an artist’s collection. This visual uniqueness of style can be captured in a mathematical model produced by a machine learning (ML) algorithm on painting images. Art authentication is not always possible as provenance can be obscured or lost through anonymity, forgery, gifting, or theft of artwork. This paper presents an image-only art authentication attribute marker of contemporary art paintings for a very large number of artists. The experiments in this paper demonstrate that it is possible to use ML-generated models to authenticate contemporary art from 2368 to 100 artists with an accuracy of 48.97% to 91.23%, respectively. This is the largest effort for image-only art authentication to date, with respect to the number of artists involved and the accuracy of authentication

Multi-View, Generative, Transfer Learning for Distributed Time Series Classification

In this paper, we propose an effective, multi-view, generative, transfer learning framework for multivariate timeseries data. While generative models are demonstrated effective for several machine learning tasks, their application to time-series classification problems is underexplored. The need for additional exploration is motivated when data are large, annotations are unbalanced or scarce, or data are distributed and fragmented. Recent advances in computer vision attempt to use synthesized samples with system generated annotations to overcome the lack or imbalance of annotated data. However, in multi-view problem settings, view mismatches between the synthetic data and real data pose additional challenges against harnessing new annotated data collections. The proposed method offers important contributions to facilitate knowledge sharing, while simultaneously ensuring an effective solution for domain-specific, finelevel categorizations. We propose a principled way to perform view adaptation in a cross-view learning environment, wherein pairwise view similarity is identified by a smaller subset of source samples that closely resemble the target data patterns. This approach integrates generative models within a deep classification framework to minimize the gap between source and target data. More precisely, we design category specific conditional, generative models to update the source generator in order for transforming source features so that they appear as target features and simultaneously tune the associated discriminative model to distinguish these features. During each learning iteration, the source generator is conditioned by a source training set represented as some target-like features. This transformation in appearance was performed via a target generator specifically learned for targetspecific customization per category. Afterward, a smaller source training set, indicating close target pattern resemblance in terms of the corresponding generative and discriminative loss, is used to fine-tune the source classification model parameters. Experiments show that compared to existing approaches, our proposed multiview, generative, transfer learning framework improves timeseries classification performance by around 4% in the UCI multiview activity recognition dataset, while also showing a robust, generalized representation capacity in classifying several largescale multi-view light curve collections

Multi-View, Generative, Transfer Learning for Distributed Time Series Classification

In this paper, we propose an effective, multi-view, generative, transfer learning framework for multivariate timeseries data. While generative models are demonstrated effective for several machine learning tasks, their application to time-series classification problems is underexplored. The need for additional exploration is motivated when data are large, annotations are unbalanced or scarce, or data are distributed and fragmented. Recent advances in computer vision attempt to use synthesized samples with system generated annotations to overcome the lack or imbalance of annotated data. However, in multi-view problem settings, view mismatches between the synthetic data and real data pose additional challenges against harnessing new annotated data collections. The proposed method offers important contributions to facilitate knowledge sharing, while simultaneously ensuring an effective solution for domain-specific, finelevel categorizations. We propose a principled way to perform view adaptation in a cross-view learning environment, wherein pairwise view similarity is identified by a smaller subset of source samples that closely resemble the target data patterns. This approach integrates generative models within a deep classification framework to minimize the gap between source and target data. More precisely, we design category specific conditional, generative models to update the source generator in order for transforming source features so that they appear as target features and simultaneously tune the associated discriminative model to distinguish these features. During each learning iteration, the source generator is conditioned by a source training set represented as some target-like features. This transformation in appearance was performed via a target generator specifically learned for targetspecific customization per category. Afterward, a smaller source training set, indicating close target pattern resemblance in terms of the corresponding generative and discriminative loss, is used to fine-tune the source classification model parameters. Experiments show that compared to existing approaches, our proposed multiview, generative, transfer learning framework improves timeseries classification performance by around 4% in the UCI multiview activity recognition dataset, while also showing a robust, generalized representation capacity in classifying several largescale multi-view light curve collections