Search Results for author:
Found 5 papers, 0 papers with code
Locally Linear Region Knowledge Distillation
To the end, the student is able to better capture the local shape of the teacher function and thus achieves a better performance.
Deep Collective Learning: Learning Optimal Inputs and Weights Jointly in Deep Neural Networks
It is well observed that in deep learning and computer vision literature, visual data are always represented in a manually designed coding scheme (eg., RGB images are represented as integers ranging from 0 to 255 for each channel) when they are input to an end-to-end deep neural network (DNN) for any learning task.
Zero-Shot Learning via Latent Space Encoding
Instead of requiring a projection function to transfer information across different modalities like most previous work, LSE per- forms the interactions of different modalities via a feature aware latent space, which is learned in an implicit way.
Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality
The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing.
Boosted Zero-Shot Learning with Semantic Correlation Regularization
For adaptable knowledge transfer, we devise a Semantic Correlation Regularization (SCR) approach to regularize the boosted model to be consistent with the inter-class semantic correlations.
