这篇论文也是在该团队 MIM 宇宙基础上提出的新架构 DeepMIM。主要内容是在 Masked Image Modeling 训练过程中加上 Deep Supervision,可以促进浅层学习更有意义的表示,加快模型收敛速度并扩大注意力的多样性。 Deep Supervision(即在神经网络的中间层引入额外的监督)在早期的深度学习中,Deep Supervision 可以解决深度神经...
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深度监督Deep Supervision 其中CE指的是交叉熵损失函数。 对deep supervision进行优化,针对中间层,借鉴知识蒸馏中对中间特征的蒸馏思想, 对中间的特征映射向量和最后一层的向量做KL散度损失,可以看作是最后一层是教师,中间层是学生,公式如下: 对比深度监督Contrastive Deep Supervision 本文方法和deep supervision的主要不...
Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing Chi Li1, M. Zeeshan Zia2, Quoc-Huy Tran2, Xiang Yu2, Gregory D. Hager1 and Manmohan Chandraker2,3 1Johns Hopkins University 2NEC Labs America 3UC San Diego Abstract Monocular 3D object parsing is highly desirable ...
Monocular 3D object parsing is highly desirable in various scenarios including occlusion reasoning and holistic scene interpretation. We present a deep convolutional neural network (CNN) architecture to localize semantic parts in 2D image and 3D space while inferring their visibility states, given a ...
To determine more information from extracted feature maps in a hidden layer, a deep supervision model is introduced in up-sampling to enhance feature representation. The spatial attention mechanism with attention gates is utilized to highlight significant regions and suppress task-independent feature ...
predicts improved generalization via this deep supervision method. One advantage of this approach is that we are able to train only from synthetic CAD renderings of cluttered scenes, where concept values can be extracted, but apply the results to real images. Our implementation achieves the state-...
Hi, Fabian I want to try the 'deep_supervision' in nnunet, I set do_ds = True in 'generic_UNet.py' . I got the error: Traceback (most recent call last): File "run/run_training.py", line 108, in trainer.run_training() File "D:\DeepLearnin...
The parameter α is the deep supervision coefficient. Since the deep layers usually contain more complex features than the shallow layers, we can improve the segmentation accuracy by assigning a higher weight to the loss of the deep network output. In this paper, parameter α is updated every ...
as “DISCO” short for Deep supervision with Intermediate Shape COncepts. At test time, DISCO trained on only synthetic images generalizes well to real images. In particular, it empirically outperforms single-task architectures without supervision for intermediate shape concepts and multitask networks wh...