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Formal Specification for Deep Neural Networks.

, , , , , , , , and . ATVA, volume 11138 of Lecture Notes in Computer Science, page 20-34. Springer, (2018)

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SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud., , , , and . ICRA, page 4376-4382. IEEE, (2019)SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud., , , and . ICRA, page 1887-1893. IEEE, (2018)Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions., , , , , , , , and . CVPR, page 9127-9135. IEEE Computer Society, (2018)Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data., , , , , and . CoRR, (2019)A Novel Domain Adaptation Framework for Medical Image Segmentation., , , , , , , , and . BrainLes@MICCAI (2), volume 11384 of Lecture Notes in Computer Science, page 289-298. Springer, (2018)Counterexample-Guided Data Augmentation., , , , , and . IJCAI, page 2071-2078. ijcai.org, (2018)SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud., , , and . CoRR, (2017)SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud., , , , and . CoRR, (2018)A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving., , , , and . ICMR, page 458-464. ACM, (2018)A Review of Single-Source Deep Unsupervised Visual Domain Adaptation., , , , , , , , , and 1 other author(s). IEEE Trans. Neural Networks Learn. Syst., 33 (2): 473-493 (2022)