.png){kind=icon}
.png){kind=icon}
Return
Epoch-Evolving Gaussian Process Guided Learning for Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2022.3174207.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The conventional mini-batch gradient descent algorithms are usually trapped in the local batch-level distribution information, resulting in the ``zig-zag'' effect in the learning process. To characterize the correlation information between the batch-level distribution and the global data distribution, we propose a novel learning scheme called epoch-evolving Gaussian process guided learning (GPGL) to encode the global data distribution information in a non-parametric way. Upon a set of class-aware anchor samples, our GP model is built to estimate the class distribution for each sample in mini-batch through label propagation from the anchor samples to the batch samples. The class distribution, also named the context label, is provided as a complement for the ground-truth one-hot label. Such a class distribution structure has a smooth property and usually carries a rich body of contextual information that is capable of speeding up the convergence process. With the guidance of the context label and ground-truth label, the GPGL scheme provides a more efficient optimization through updating the model parameters with a triangle consistency loss. Furthermore, our GPGL scheme can be generalized and naturally applied to the current deep models, outperforming the state-of-the-art optimization methods on six benchmark datasets.
Keywords:
Computational modeling
Pipelines
Deep learning
Context modeling
Predictive models
Feature extraction
Data models
Gaussian process (GP)
global distribution-aware learning
non-parametric modeling
top-down strategy
Journal
IF:
8.9
Papers:
7.5K
Citations:
7.2W