PEIRA learns predictive encoders by optimizing the trace of the optimal inter-view linear regressor, with only nontrivial global minimizers as stable equilibria that recover leading nonlinear canonical correlation subspaces.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Randomly masking square regions of input images during CNN training yields new state-of-the-art test errors of 2.56% on CIFAR-10, 15.20% on CIFAR-100, and 1.30% on SVHN.
Fashion-MNIST is a new benchmark dataset of 70,000 fashion product images that serves as a direct drop-in replacement for the original MNIST dataset while being more challenging.
CovNorm reduces parameters in domain-adaptive layers via two PCAs and a mini-adaptation layer, enabling efficient multi-domain learning with performance close to full fine-tuning.
citing papers explorer
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PEIRA: Learning Predictive Encoders through Inter-View Regressor Alignment
PEIRA learns predictive encoders by optimizing the trace of the optimal inter-view linear regressor, with only nontrivial global minimizers as stable equilibria that recover leading nonlinear canonical correlation subspaces.
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Improved Regularization of Convolutional Neural Networks with Cutout
Randomly masking square regions of input images during CNN training yields new state-of-the-art test errors of 2.56% on CIFAR-10, 15.20% on CIFAR-100, and 1.30% on SVHN.
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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Fashion-MNIST is a new benchmark dataset of 70,000 fashion product images that serves as a direct drop-in replacement for the original MNIST dataset while being more challenging.
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Efficient Multi-Domain Network Learning by Covariance Normalization
CovNorm reduces parameters in domain-adaptive layers via two PCAs and a mini-adaptation layer, enabling efficient multi-domain learning with performance close to full fine-tuning.