「Deep Learning」Note on Benchmark Analysis of DNNs
[1] Benchmark Analysis of Representative Deep Neural Network Architectures IEEE Access 2018 [paper]
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「Deep Learning」Note on Benchmark Analysis of DNNs
[1] Benchmark Analysis of Representative Deep Neural Network Architectures IEEE Access 2018 [paper]
「Deep Learning」Note on Gather and Excite Network (GENet)
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「Deep Learning」Note on Deep Semantic Instance Segmentation of Tree-like Structures
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「Deep Learning」Note on Activation Functions
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「Deep Learning」Note on Deep Convolutinal Activation Feature (DeCAF)
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「Deep Learning」Note on LeNet-5
[1] Gradient-based Learning Applied to Document Recognition Proceedings of the IEEE 1998 [paper]
「Deep Learning」Note on Rethinking ImageNet Pre-training
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「Transfer Learning」Note on Strategic Curriculum of Proxy Labels
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「Deep Learning」Notes on Squeeze-and-Excitation Network (SENet)
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「Deep Learning」Notes on Bottleneck Attention Module (BAM)
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「Transfer Learning」Note on Geometrically Guided Input-Output Adaptation
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「Transfer Learning」Note on CyCADA: Cycle-Consistent Adversarial Domain Adaptation
雖然CyCADA[1]被AdaptSegNet超越了，但是兩者可以結合在一起，進一步提升域適配中語義分割的效能。在沒有AdaptSegnNet的時候，CyCADA是很好的模型。在CyCADA論文的第三個版本中，發現DRN-26[2]比AdaptSegNet中D
「Machine Learning」Note on MSL: Marginal Space Learning （投影空間學習）
[1] Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal
「Computer Vision」Note on Faster Training of Mask R-CNN
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「Deep Learning」理解PyTorch中的「torchvision.transforms」
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「Computer Vision」Note on Attention-guided Unified Network
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「Computer Vision」Note on Facial Landmark Machines
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「Computer Vision」Note on Feature Denoising
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「Computer Vision」Note on SlowFast Networks
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