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Deep Learning on Small Datasets without Pre-Training using Cosine Loss.

, and . WACV, page 1360-1369. IEEE, (2020)

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On the Suitability of Different Features for Anomaly Detection in Wire Ropes., , , , and . VISIGRAPP (Selected Papers), volume 68 of Communications in Computer and Information Science, page 296-308. Springer, (2009)Extrinsic Self-Calibration of Time-of-Flight Cameras using a Combination of 3D and Intensity Descriptors., , and . VMV, page 269-276. Eurographics Association, (2011)Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images., , , , , and . IV, page 675-682. IEEE, (2019)Neither Quick Nor Proper - Evaluation of QuickProp for Learning Deep Neural Networks., , , , and . CoRR, (2016)Self-supervised Data Bootstrapping for Deep Optical Character Recognition of Identity Documents., and . CoRR, (2019)On Optimal Camera Parameter Selection in Kalman Filter Based Object Tracking., , and . DAGM-Symposium, volume 2449 of Lecture Notes in Computer Science, page 17-25. Springer, (2002)MB-Net: MergeBoxes for Real-Time 3D Vehicles Detection., , , , and . Intelligent Vehicles Symposium, page 2117-2124. IEEE, (2018)Active and Incremental Learning with Weak Supervision., , and . Künstliche Intell., 34 (2): 165-180 (2020)A Strong Baseline for the VIPriors Data-Efficient Image Classification Challenge., , , and . CoRR, (2021)The Whole Is More Than Its Parts? From Explicit to Implicit Pose Normalization., , , and . IEEE Trans. Pattern Anal. Mach. Intell., 42 (3): 749-763 (2020)