Out-of-Distribution Detection Using Layerwise Uncertainty in Deep Neural Networks

no code implementations • ICLR 2020 • Hirono Okamoto, Masahiro Suzuki, Yutaka Matsuo

However, on difficult datasets or models with low classification ability, these methods incorrectly regard in-distribution samples close to the decision boundary as OOD samples.

Classification General Classification +1

Variational Domain Adaptation

no code implementations • ICLR 2019 • Hirono Okamoto, Shohei Ohsawa, Itto Higuchi, Haruka Murakami, Mizuki Sango, Zhenghang Cui, Masahiro Suzuki, Hiroshi Kajino, Yutaka Matsuo

It reformulates the posterior with a natural paring $\langle, \rangle: \mathcal{Z} \times \mathcal{Z}^* \rightarrow \Real$, which can be expanded to uncountable infinite domains such as continuous domains as well as interpolation.

Bayesian Inference Domain Adaptation +2

DUAL SPACE LEARNING WITH VARIATIONAL AUTOENCODERS

no code implementations • ICLR Workshop DeepGenStruct 2019 • Hirono Okamoto, Masahiro Suzuki, Itto Higuchi, Shohei Ohsawa, Yutaka Matsuo

However, when the dimension of multiclass labels is large, these models cannot change images corresponding to labels, because learning multiple distributions of the corresponding class is necessary to transfer an image.