We formalize the notion of a pseudo-ensemble, a (possibly infinite) collection of child models spawned from a parent model by perturbing it according to some noise process. E.g., dropout (Hinton et. al, 2012) in a deep neural network trains a pseudo-ensemble of child subnetworks generated by randomly masking nodes in t…
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3 results for “pseudo-ensembling”
Paper proposes a new framework for semi-supervised learning.
problem Limited annotated data in supervised learning.
method Fuzzy domain constraint-based framework for semi-supervised learning.
result Enhances model quality for semi-supervised learning.
TentacleNet improves binarized CNNs, reducing accuracy loss and memory usage.
problem Excessive accuracy loss in binarized CNNs.
method Parallelization inspired by ensemble learning theory, end-to-end trainable compact topology.
result Significant memory savings compared to state-of-the-art binary ensemble methods.