What is an intermediate supervision?
ds2268 commented on Oct 4, 2017. Intermediate supervision is implemented such that at the end of each stage you have loss to be computed instead of just at the end.
What is deep supervision in deep learning?
Deep Supervision. Deep supervision (Kayalibay et al., 2017) is the design where multiple segmentation maps are generated at different resolutions levels. The feature maps from each network level are transposed by 1 × 1 × 1 convolutions to create secondary segmentation maps.
What is deep supervision in CNN?
Deep supervision was firstly introduced by Lee et al. (2015) as a way to deal with the problem of the vanishing gradient in training deeper CNN for image classification. This method adds companion objective functions at each hidden layer in addition to the overall objective function at the output layer.
What are neural networks?
Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.
What is the importance of neural networks?
Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning.
Why is deep supervision with intermediate concepts important?
We find deep supervision with intermediate concepts to be a critical element to bridge the synthetic and real world. In particular, our deep supervision scheme empirically outperforms the single-task architecture, and multi-task networks which supervise all the concepts at the final layer.
Which is an application of a deep supervision scheme?
We present a deep supervision scheme with intermediate concepts for deep neural networks. One application of our deep supervision is 3D object structure inference which is linked to recent advances including reconstruction, alignment and pose estimation. We review related work on these problems in the following: Multi-task Learning.
How to train deeper convolutional networks with deep supervision?
They show that this deep supervision yields an improved convergence rate, but their experiments are limited with a not-so-deep network structure. In this work, to train deeper networks more efficiently, we also adopt the idea of adding auxiliary classifiers after some of the intermediate convolutional layers.
Which is better deep supervision or single task architecture?
In particular, our deep supervision scheme empirically outperforms the single-task architecture, and multi-task networks which supervise all the concepts at the final layer.