29,765 research outputs found
Domain Conditioned Adaptation Network
Tremendous research efforts have been made to thrive deep domain adaptation
(DA) by seeking domain-invariant features. Most existing deep DA models only
focus on aligning feature representations of task-specific layers across
domains while integrating a totally shared convolutional architecture for
source and target. However, we argue that such strongly-shared convolutional
layers might be harmful for domain-specific feature learning when source and
target data distribution differs to a large extent. In this paper, we relax a
shared-convnets assumption made by previous DA methods and propose a Domain
Conditioned Adaptation Network (DCAN), which aims to excite distinct
convolutional channels with a domain conditioned channel attention mechanism.
As a result, the critical low-level domain-dependent knowledge could be
explored appropriately. As far as we know, this is the first work to explore
the domain-wise convolutional channel activation for deep DA networks.
Moreover, to effectively align high-level feature distributions across two
domains, we further deploy domain conditioned feature correction blocks after
task-specific layers, which will explicitly correct the domain discrepancy.
Extensive experiments on three cross-domain benchmarks demonstrate the proposed
approach outperforms existing methods by a large margin, especially on very
tough cross-domain learning tasks.Comment: Accepted by AAAI 202
Residual Parameter Transfer for Deep Domain Adaptation
The goal of Deep Domain Adaptation is to make it possible to use Deep Nets
trained in one domain where there is enough annotated training data in another
where there is little or none. Most current approaches have focused on learning
feature representations that are invariant to the changes that occur when going
from one domain to the other, which means using the same network parameters in
both domains. While some recent algorithms explicitly model the changes by
adapting the network parameters, they either severely restrict the possible
domain changes, or significantly increase the number of model parameters.
By contrast, we introduce a network architecture that includes auxiliary
residual networks, which we train to predict the parameters in the domain with
little annotated data from those in the other one. This architecture enables us
to flexibly preserve the similarities between domains where they exist and
model the differences when necessary. We demonstrate that our approach yields
higher accuracy than state-of-the-art methods without undue complexity
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