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Epoch-Evolving Gaussian Process Guided Learning for Classification
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DOI：10.1109/TNNLS.2022.3174207.png)
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摘要
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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.
Keyword:
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
期刊
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8.9
论文数:
7.6K
被引数:
7.2W
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