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Found 6 papers, 1 papers with code
Federated Distillation: A Survey
Federated Learning (FL) seeks to train a model collaboratively without sharing private training data from individual clients.
Deep Dictionary Learning with An Intra-class Constraint
Specifically, we design the intra-class compactness constraint on the intermediate representation at different levels to encourage the intra-class representations to be closer to each other, and eventually the learned representation becomes more discriminative.~Unlike the traditional DDL methods, during the classification stage, our DDLIC performs a layer-wise greedy optimization in a similar way to the training stage.
Learning Canonical F-Correlation Projection for Compact Multiview Representation
With this framework as a tool, we propose a correlative covariation projection (CCP) method by using an explicit nonlinear mapping.
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer
However, most of the existing knowledge distillation methods consider only one type of knowledge learned from either instance features or instance relations via a specific distillation strategy in teacher-student learning.
Knowledge Distillation: A Survey
To this end, a variety of model compression and acceleration techniques have been developed.
Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image Classification
We propose a deep collaborative weight-based classification (DeepCWC) method to resolve this problem, by providing a novel option to fully take advantage of deep features in classic machine learning.
