252 research outputs found
Domain Adaptive Neural Networks for Object Recognition
We propose a simple neural network model to deal with the domain adaptation
problem in object recognition. Our model incorporates the Maximum Mean
Discrepancy (MMD) measure as a regularization in the supervised learning to
reduce the distribution mismatch between the source and target domains in the
latent space. From experiments, we demonstrate that the MMD regularization is
an effective tool to provide good domain adaptation models on both SURF
features and raw image pixels of a particular image data set. We also show that
our proposed model, preceded by the denoising auto-encoder pretraining,
achieves better performance than recent benchmark models on the same data sets.
This work represents the first study of MMD measure in the context of neural
networks
Learning the Roots of Visual Domain Shift
In this paper we focus on the spatial nature of visual domain shift,
attempting to learn where domain adaptation originates in each given image of
the source and target set. We borrow concepts and techniques from the CNN
visualization literature, and learn domainnes maps able to localize the degree
of domain specificity in images. We derive from these maps features related to
different domainnes levels, and we show that by considering them as a
preprocessing step for a domain adaptation algorithm, the final classification
performance is strongly improved. Combined with the whole image representation,
these features provide state of the art results on the Office dataset.Comment: Extended Abstrac
Towards learning free naive bayes nearest neighbor-based domain adaptation
As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful convolutional neural network we can train fails to perform satisfactorily when trained and tested on data from different databases. This issue, known as domain adaptation and/or dataset bias in the literature, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. Recent work showed that by casting the problem into the image-to-class recognition framework, the domain adaptation problem is significantly alleviated [23]. Here we follow this approach, and show how a very simple, learning free Naive Bayes Nearest Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate the distribution mismatch among source and target data, especially when the number of classes and the number of sources grow. Experiments on standard benchmarks used in the literature show that our approach (a) is competitive with the current state of the art on small scale problems, and (b) achieves the current state of the art as the number of classes and sources grows, with minimal computational requirements. © Springer International Publishing Switzerland 2015
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From Pixels to Physics: Probabilistic Color De-Rendering
Consumer digital cameras use tone-mapping to produce compact, narrow-gamut images that are nonetheless visually pleasing. In doing so, they discard or distort substantial radiometric signal that could otherwise be used for computer vision. Existing methods attempt to undo these effects through deterministic maps that de-render the reported narrow-gamut colors back to their original wide-gamut sensor measurements. Deterministic approaches are unreliable, however, because the reverse narrow-to-wide mapping is one-to-many and has inherent uncertainty. Our solution is to use probabilistic maps, providing uncertainty estimates useful to many applications. We use a non-parametric Bayesian regression technique - local Gaussian process regression - to learn for each pixel's narrow-gamut color a probability distribution over the scene colors that could have created it. Using a variety of consumer cameras we show that these distributions, once learned from training data, are effective in simple probabilistic adaptations of two popular applications: multi-exposure imaging and photometric stereo. Our results on these applications are better than those of corresponding deterministic approaches, especially for saturated and out-of-gamut colors.Engineering and Applied Science
Zero-Shot Deep Domain Adaptation
Domain adaptation is an important tool to transfer knowledge about a task
(e.g. classification) learned in a source domain to a second, or target domain.
Current approaches assume that task-relevant target-domain data is available
during training. We demonstrate how to perform domain adaptation when no such
task-relevant target-domain data is available. To tackle this issue, we propose
zero-shot deep domain adaptation (ZDDA), which uses privileged information from
task-irrelevant dual-domain pairs. ZDDA learns a source-domain representation
which is not only tailored for the task of interest but also close to the
target-domain representation. Therefore, the source-domain task of interest
solution (e.g. a classifier for classification tasks) which is jointly trained
with the source-domain representation can be applicable to both the source and
target representations. Using the MNIST, Fashion-MNIST, NIST, EMNIST, and SUN
RGB-D datasets, we show that ZDDA can perform domain adaptation in
classification tasks without access to task-relevant target-domain training
data. We also extend ZDDA to perform sensor fusion in the SUN RGB-D scene
classification task by simulating task-relevant target-domain representations
with task-relevant source-domain data. To the best of our knowledge, ZDDA is
the first domain adaptation and sensor fusion method which requires no
task-relevant target-domain data. The underlying principle is not particular to
computer vision data, but should be extensible to other domains.Comment: This paper is accepted to the European Conference on Computer Vision
(ECCV), 201
On the Effectiveness of Image Rotation for Open Set Domain Adaptation
Open Set Domain Adaptation (OSDA) bridges the domain gap between a labeled
source domain and an unlabeled target domain, while also rejecting target
classes that are not present in the source. To avoid negative transfer, OSDA
can be tackled by first separating the known/unknown target samples and then
aligning known target samples with the source data. We propose a novel method
to addresses both these problems using the self-supervised task of rotation
recognition. Moreover, we assess the performance with a new open set metric
that properly balances the contribution of recognizing the known classes and
rejecting the unknown samples. Comparative experiments with existing OSDA
methods on the standard Office-31 and Office-Home benchmarks show that: (i) our
method outperforms its competitors, (ii) reproducibility for this field is a
crucial issue to tackle, (iii) our metric provides a reliable tool to allow
fair open set evaluation.Comment: accepted at ECCV 202
A unified framework for domain adaptive pose estimation
While pose estimation is an important computer vision task, it requires expensive annotation and suffers from domain shift. In this paper, we investigate the problem of domain adaptive 2D pose estimation that transfers knowledge learned on a synthetic source domain to a target domain without supervision. While several domain adaptive pose estimation models have been proposed recently, they are not generic but only focus on either human pose or animal pose estimation, and thus their effectiveness is somewhat limited to specific scenarios. In this work, we propose a unified framework that generalizes well on various domain adaptive pose estimation problems. We propose to align representations using both input-level and output-level cues (pixels and pose labels, respectively), which facilitates the knowledge transfer from the source domain to the unlabeled target domain. Our experiments show that our method achieves state-of-the-art performance under various domain shifts. Our method outperforms existing baselines on human pose estimation by up to 4.5 percent points (pp), hand pose estimation by up to 7.4 pp, and animal pose estimation by up to 4.8 pp for dogs and 3.3 pp for sheep. These results suggest that our method is able to mitigate domain shift on diverse tasks and even unseen domains and objects (e.g., trained on horse and tested on dog). Our code will be publicly available at: https://github.com/VisionLearningGroup/UDA_PoseEstimation.N00014-19-1-2571 - Department of Defense/ONRhttps://doi.org/10.1007/978-3-031-19827-4_35First author draf
Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation
Unsupervised domain adaptation has caught appealing attentions as it facilitates the unlabeled target learning by borrowing existing well-established source domain knowledge. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target domain to better solve cross-domain distribution divergences. However, existing approaches separate target label optimization and domain-invariant feature learning as different steps. To address that issue, we develop a novel Graph Adaptive Knowledge Transfer (GAKT) model to jointly optimize target labels and domain-free features in a unified framework. Specifically, semi-supervised knowledge adaptation and label propagation on target data are coupled to benefit each other, and hence the marginal and conditional disparities across different domains will be better alleviated. Experimental evaluation on two cross-domain visual datasets demonstrates the effectiveness of our designed approach on facilitating the unlabeled target task learning, compared to the state-of-the-art domain adaptation approaches
Active Sampling Based on MMD for Model Adaptation
© 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. In this paper, we demonstrate a method for transfer learning with minimal supervised information. Recently, researchers have proposed various algorithms to solve transfer learning problems, especially the unsupervised domain adaptation problem. They mainly focus on how to learn a good common representation and use it directly for downstream task. Unfortunately, they ignore the fact that this representation may not capture target-specific feature for target task well. In order to solve this problem, this paper attempts to capture target-specific feature by utilizing labeled data in target domain. Now it’s a challenge that how to seek as little supervised information as possible to achieve good results. To overcome this challenge, we actively select instances for training and model adaptation based on MMD method. In this process, we try to label some valuable target data to capture target-specific feature and fine-tune the classifier networks. We choose a batch of data in target domain far from common representation space and having maximum entropy. The first requirement is helpful to learn a good representation for target domain and the second requirement tries to improve the classifier performance. Finally, we experiment with our method on several datasets which shows significant improvement and competitive advantage against common methods
Scalable Unsupervised Domain Adaptation for Electron Microscopy
While Machine Learning algorithms are key to automating organelle segmentation in large EM stacks, they require annotated data, which is hard to come by in sufficient quantities. Furthermore, images acquired from one part of the brain are not always representative of another due to the variability in the acquisition and staining processes. Therefore, a classifier trained on the first may perform poorly on the second and additional annotations may be required. To remove this cumbersome requirement, we introduce an Unsupervised Domain Adaptation approach that can leverage annotated data from one brain area to train a classifier that applies to another for which no labeled data is available. To this end, we establish noisy visual correspondences between the two areas and develop a Multiple Instance Learning approach to exploiting them. We demonstrate the benefits of our approach over several baselines for the purpose of synapse and mitochondria segmentation in EM stacks of different parts of mouse brains
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