29,742 research outputs found
A Domain Agnostic Normalization Layer for Unsupervised Adversarial Domain Adaptation
We propose a normalization layer for unsupervised domain adaption in semantic
scene segmentation. Normalization layers are known to improve convergence and
generalization and are part of many state-of-the-art fully-convolutional neural
networks. We show that conventional normalization layers worsen the performance
of current Unsupervised Adversarial Domain Adaption (UADA), which is a method
to improve network performance on unlabeled datasets and the focus of our
research. Therefore, we propose a novel Domain Agnostic Normalization layer and
thereby unlock the benefits of normalization layers for unsupervised
adversarial domain adaptation. In our evaluation, we adapt from the synthetic
GTA5 data set to the real Cityscapes data set, a common benchmark experiment,
and surpass the state-of-the-art. As our normalization layer is domain agnostic
at test time, we furthermore demonstrate that UADA using Domain Agnostic
Normalization improves performance on unseen domains, specifically on
Apolloscape and Mapillary
Constraining Implicit Space with Minimum Description Length: An Unsupervised Attention Mechanism across Neural Network Layers
Inspired by the adaptation phenomenon of neuronal firing, we propose the
regularity normalization (RN) as an unsupervised attention mechanism (UAM)
which computes the statistical regularity in the implicit space of neural
networks under the Minimum Description Length (MDL) principle. Treating the
neural network optimization process as a partially observable model selection
problem, UAM constrains the implicit space by a normalization factor, the
universal code length. We compute this universal code incrementally across
neural network layers and demonstrated the flexibility to include data priors
such as top-down attention and other oracle information. Empirically, our
approach outperforms existing normalization methods in tackling limited,
imbalanced and non-stationary input distribution in image classification,
classic control, procedurally-generated reinforcement learning, generative
modeling, handwriting generation and question answering tasks with various
neural network architectures. Lastly, UAM tracks dependency and critical
learning stages across layers and recurrent time steps of deep networks
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