6,770 research outputs found
Multi-component Image Translation for Deep Domain Generalization
Domain adaption (DA) and domain generalization (DG) are two closely related
methods which are both concerned with the task of assigning labels to an
unlabeled data set. The only dissimilarity between these approaches is that DA
can access the target data during the training phase, while the target data is
totally unseen during the training phase in DG. The task of DG is challenging
as we have no earlier knowledge of the target samples. If DA methods are
applied directly to DG by a simple exclusion of the target data from training,
poor performance will result for a given task. In this paper, we tackle the
domain generalization challenge in two ways. In our first approach, we propose
a novel deep domain generalization architecture utilizing synthetic data
generated by a Generative Adversarial Network (GAN). The discrepancy between
the generated images and synthetic images is minimized using existing domain
discrepancy metrics such as maximum mean discrepancy or correlation alignment.
In our second approach, we introduce a protocol for applying DA methods to a DG
scenario by excluding the target data from the training phase, splitting the
source data to training and validation parts, and treating the validation data
as target data for DA. We conduct extensive experiments on four cross-domain
benchmark datasets. Experimental results signify our proposed model outperforms
the current state-of-the-art methods for DG.Comment: Accepted in WACV 201
Self-Supervised Deep Visual Odometry with Online Adaptation
Self-supervised VO methods have shown great success in jointly estimating
camera pose and depth from videos. However, like most data-driven methods,
existing VO networks suffer from a notable decrease in performance when
confronted with scenes different from the training data, which makes them
unsuitable for practical applications. In this paper, we propose an online
meta-learning algorithm to enable VO networks to continuously adapt to new
environments in a self-supervised manner. The proposed method utilizes
convolutional long short-term memory (convLSTM) to aggregate rich
spatial-temporal information in the past. The network is able to memorize and
learn from its past experience for better estimation and fast adaptation to the
current frame. When running VO in the open world, in order to deal with the
changing environment, we propose an online feature alignment method by aligning
feature distributions at different time. Our VO network is able to seamlessly
adapt to different environments. Extensive experiments on unseen outdoor
scenes, virtual to real world and outdoor to indoor environments demonstrate
that our method consistently outperforms state-of-the-art self-supervised VO
baselines considerably.Comment: Accepted by CVPR 2020 ora
Incremental Adversarial Domain Adaptation for Continually Changing Environments
Continuous appearance shifts such as changes in weather and lighting
conditions can impact the performance of deployed machine learning models.
While unsupervised domain adaptation aims to address this challenge, current
approaches do not utilise the continuity of the occurring shifts. In
particular, many robotics applications exhibit these conditions and thus
facilitate the potential to incrementally adapt a learnt model over minor
shifts which integrate to massive differences over time. Our work presents an
adversarial approach for lifelong, incremental domain adaptation which benefits
from unsupervised alignment to a series of intermediate domains which
successively diverge from the labelled source domain. We empirically
demonstrate that our incremental approach improves handling of large appearance
changes, e.g. day to night, on a traversable-path segmentation task compared
with a direct, single alignment step approach. Furthermore, by approximating
the feature distribution for the source domain with a generative adversarial
network, the deployment module can be rendered fully independent of retaining
potentially large amounts of the related source training data for only a minor
reduction in performance.Comment: International Conference on Robotics and Automation 201
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