5,048 research outputs found
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images
Semantic segmentation models based on convolutional neural networks (CNNs)
have gained much attention in relation to remote sensing and have achieved
remarkable performance for the extraction of buildings from high-resolution
aerial images. However, the issue of limited generalization for unseen images
remains. When there is a domain gap between the training and test datasets,
CNN-based segmentation models trained by a training dataset fail to segment
buildings for the test dataset. In this paper, we propose segmentation networks
based on a domain adaptive transfer attack (DATA) scheme for building
extraction from aerial images. The proposed system combines the domain transfer
and adversarial attack concepts. Based on the DATA scheme, the distribution of
the input images can be shifted to that of the target images while turning
images into adversarial examples against a target network. Defending
adversarial examples adapted to the target domain can overcome the performance
degradation due to the domain gap and increase the robustness of the
segmentation model. Cross-dataset experiments and the ablation study are
conducted for the three different datasets: the Inria aerial image labeling
dataset, the Massachusetts building dataset, and the WHU East Asia dataset.
Compared to the performance of the segmentation network without the DATA
scheme, the proposed method shows improvements in the overall IoU. Moreover, it
is verified that the proposed method outperforms even when compared to feature
adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure
Generate To Adapt: Aligning Domains using Generative Adversarial Networks
Domain Adaptation is an actively researched problem in Computer Vision. In
this work, we propose an approach that leverages unsupervised data to bring the
source and target distributions closer in a learned joint feature space. We
accomplish this by inducing a symbiotic relationship between the learned
embedding and a generative adversarial network. This is in contrast to methods
which use the adversarial framework for realistic data generation and
retraining deep models with such data. We demonstrate the strength and
generality of our approach by performing experiments on three different tasks
with varying levels of difficulty: (1) Digit classification (MNIST, SVHN and
USPS datasets) (2) Object recognition using OFFICE dataset and (3) Domain
adaptation from synthetic to real data. Our method achieves state-of-the art
performance in most experimental settings and by far the only GAN-based method
that has been shown to work well across different datasets such as OFFICE and
DIGITS.Comment: Accepted as spotlight talk at CVPR 2018. Code available here:
https://github.com/yogeshbalaji/Generate_To_Adap
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
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