Dean (2015) Distilling the knowledge in a neural network. Dietterich (2019)ĭeep anomaly detection with outlier exposure. In International Conference on Learning Representations, Gimpel (2017) A baseline for detecting misclassified and out-of-distribution examples in neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,
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Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,
Li (2019)īag of tricks for image classification with convolutional neural networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, Weinberger (2017) On calibration of modern neural networks. WRN-40-2 is used as the model architecture of both teacher and student models. (Bottom) teacher models of CIFAR-10 are finetuned with MNIST, TinyImageNet, and MNIST+TinyImageNet by outlier exposure. (Top) teacher models trained with the SVHN, CIFAR-10, and CIFAR-100 dataset and their student models. Figure 2: OOD detection AUROCs of a teacher model and its student model. When ECE starts to increase (after the red dot line), dramatic drops of AUROC are shown in the training datasets of SVHN (ID) and CIFAR-10 (ID). OOD detection is continuously deteriorated when label smoothing α increases. The red dot line represents label smoothing α minimizing ECE. Figure 1: Test accuracy and expected calibration error (top) and OOD detection AUROC (bottom) of WRN, trained with SVHN (left), CIFAR-10 (middle), and CIFAR-100 (right) respectively. WRN-OE, which is finetuned with TinyImageNet as OOD, is used as the teacher model for the two student models (WRN and DenseNet). Table 2: OOD detection performance of outlier exposure and outlier distillation. OD (Outlier Distillation) means the student model of OE model. TinyImageNet is used to train for OE (Outlier Exposure). Table 1: Test accuracy and expected calibration error (ECE) of WideResNet (Baseline) trained with SVHN, CIFAR10, and CIFAR-100. In addition, we follow the hyper-parameter settings of knowledge distillation in (Müller et al., 2019).įor evaluation of OOD detection, we use the MNIST, Fashion-MNIST, SVHN (or CIFAR-10), LSUN, and TinyImageNet datasets for OOD samples, and AUROC for the evaluation measure.
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We follow the experimental setting in the official code of outlier exposure 1 1 1 Įxcept that we use 150 epochs for training. “We feel confident that our labels will be best positioned for a healthy and prosperous future alongside The Orchard, who is dedicated to serving independents on a global level.In this paper, we train WRN-40-2 (Zagoruyko and Komodakis, 2016) with the SVHN, CIFAR-10, and CIFAR-100 datasets (ID). “Today is a big day for our finetunes family,” commented founder Oke Göttlich. In line with The Orchard’s acquisition, labels distributed by Finetunes and Phonofile will have access to company’s services in over 25 global territories, including physical sales and distribution, marketing, promotion, sync licensing and royalty collection processing.
Last year, Finetunes and Phonofile merged under the umbrella of SendR - a holding company predominantly owned by Göttlich and Thiess that also includes FONO (the Norwegian independent record labels’ association) and NOPA (the Norwegian association for music composers and lyricists) among its main shareholders. Phonofile AS was formed in 1999 and is the leading music aggregator in the Nordic region, representing over 1,000 record labels and with offices in New York, London, Oslo, Gothenburg and Copenhagen.
Pandora, UMG, Orchard Execs Talk Lower Streaming Prices (Surprise, They're Against It)įounded by Oke Göttlich and Henning Thiess in 2003, Finetunes represents more than 2,500 direct and indirect licensors worldwide, has a database of more than 1 million tracks and operates offices in the U.K., U.S., France and Germany, where the company is headquartered.
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