Sentence examples for deep supervised from inspiring English sources

The experimental results showed the deep supervised MTRNN to be more robust and to outperform the single layer supervised MTRNN model for detecting human intention.

In this paper, a novel supervised-binarization method is proposed, in which a hierarchical deep supervised network (DSN) architecture is learned for the prediction of the text pixels at different feature levels.

Moreover, Li et al. propose a deep pairwise-supervised hashing to firstly perform simultaneous feature learning and hash-code learning for applications with pairwise labels [71].

Compared with [28, 70], the main difference of the deep pairwise-supervised hashing is that the triplet ranking loss is replaced by the pairwise ranking loss, which is similar to Siamese Neural Network.

This paper presents an in-field automatic wheat disease diagnosis system based on a weakly supervised deep learning framework, i.e. deep multiple instance learning, which achieves an integration of identification for wheat diseases and localization for disease areas with only image-level annotation for training images in wild conditions.

In the spirit of deep belief networks, supervised centroid-encoding is proposed as a way to exploit class label information.

Unsupervised learning of feature hierarchies is often a good strategy to initialize deep architectures for supervised learning.

"The future of artificial intelligence is in machine learning and in how our brain works," says Valpola. "[So far] the successful stories have been about supervised deep learning.

To solve these problems, we design a supervised deep auto-encoder (AE) model to complete the semantic segmentation of road environment images.

Aim to jointly learn the feature and hashing function simultaneously, Xia et al. [22] propose a supervised deep learning hashing to integrate image feature and hashing value learning into a joint learning model.

The novel feature representation method for bearing data is first discussed using supervised deep learning with the goal of identifying more robust and salient feature representations to reduce information loss.