1,587 research outputs found
Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks
(Unsupervised) Domain Adaptation (DA) seeks for classifying target instances
when solely provided with source labeled and target unlabeled examples for
training. Learning domain-invariant features helps to achieve this goal,
whereas it underpins unlabeled samples drawn from a single or multiple explicit
target domains (Multi-target DA). In this paper, we consider a more realistic
transfer scenario: our target domain is comprised of multiple sub-targets
implicitly blended with each other, so that learners could not identify which
sub-target each unlabeled sample belongs to. This Blending-target Domain
Adaptation (BTDA) scenario commonly appears in practice and threatens the
validities of most existing DA algorithms, due to the presence of domain gaps
and categorical misalignments among these hidden sub-targets.
To reap the transfer performance gains in this new scenario, we propose
Adversarial Meta-Adaptation Network (AMEAN). AMEAN entails two adversarial
transfer learning processes. The first is a conventional adversarial transfer
to bridge our source and mixed target domains. To circumvent the intra-target
category misalignment, the second process presents as ``learning to adapt'': It
deploys an unsupervised meta-learner receiving target data and their ongoing
feature-learning feedbacks, to discover target clusters as our
``meta-sub-target'' domains. These meta-sub-targets auto-design our
meta-sub-target DA loss, which empirically eliminates the implicit category
mismatching in our mixed target. We evaluate AMEAN and a variety of DA
algorithms in three benchmarks under the BTDA setup. Empirical results show
that BTDA is a quite challenging transfer setup for most existing DA
algorithms, yet AMEAN significantly outperforms these state-of-the-art
baselines and effectively restrains the negative transfer effects in BTDA.Comment: CVPR-19 (oral). Code is available at
http://github.com/zjy526223908/BTD
Domain Generalization via Ensemble Stacking for Face Presentation Attack Detection
Face Presentation Attack Detection (PAD) plays a pivotal role in securing
face recognition systems against spoofing attacks. Although great progress has
been made in designing face PAD methods, developing a model that can generalize
well to unseen test domains remains a significant challenge. Moreover, due to
different types of spoofing attacks, creating a dataset with a sufficient
number of samples for training deep neural networks is a laborious task. This
work proposes a comprehensive solution that combines synthetic data generation
and deep ensemble learning to enhance the generalization capabilities of face
PAD. Specifically, synthetic data is generated by blending a static image with
spatiotemporal encoded images using alpha composition and video distillation.
This way, we simulate motion blur with varying alpha values, thereby generating
diverse subsets of synthetic data that contribute to a more enriched training
set. Furthermore, multiple base models are trained on each subset of synthetic
data using stacked ensemble learning. This allows the models to learn
complementary features and representations from different synthetic subsets.
The meta-features generated by the base models are used as input to a new model
called the meta-model. The latter combines the predictions from the base
models, leveraging their complementary information to better handle unseen
target domains and enhance the overall performance. Experimental results on
four datasets demonstrate low half total error rates (HTERs) on three benchmark
datasets: CASIA-MFSD (8.92%), MSU-MFSD (4.81%), and OULU-NPU (6.70%). The
approach shows potential for advancing presentation attack detection by
utilizing large-scale synthetic data and the meta-model
Deep Domain-Adversarial Image Generation for Domain Generalisation
Machine learning models typically suffer from the domain shift problem when
trained on a source dataset and evaluated on a target dataset of different
distribution. To overcome this problem, domain generalisation (DG) methods aim
to leverage data from multiple source domains so that a trained model can
generalise to unseen domains. In this paper, we propose a novel DG approach
based on \emph{Deep Domain-Adversarial Image Generation} (DDAIG). Specifically,
DDAIG consists of three components, namely a label classifier, a domain
classifier and a domain transformation network (DoTNet). The goal for DoTNet is
to map the source training data to unseen domains. This is achieved by having a
learning objective formulated to ensure that the generated data can be
correctly classified by the label classifier while fooling the domain
classifier. By augmenting the source training data with the generated unseen
domain data, we can make the label classifier more robust to unknown domain
changes. Extensive experiments on four DG datasets demonstrate the
effectiveness of our approach.Comment: 8 page
Causality-inspired Single-source Domain Generalization for Medical Image Segmentation
Deep learning models usually suffer from domain shift issues, where models
trained on one source domain do not generalize well to other unseen domains. In
this work, we investigate the single-source domain generalization problem:
training a deep network that is robust to unseen domains, under the condition
that training data is only available from one source domain, which is common in
medical imaging applications. We tackle this problem in the context of
cross-domain medical image segmentation. Under this scenario, domain shifts are
mainly caused by different acquisition processes. We propose a simple
causality-inspired data augmentation approach to expose a segmentation model to
synthesized domain-shifted training examples. Specifically, 1) to make the deep
model robust to discrepancies in image intensities and textures, we employ a
family of randomly-weighted shallow networks. They augment training images
using diverse appearance transformations. 2) Further we show that spurious
correlations among objects in an image are detrimental to domain robustness.
These correlations might be taken by the network as domain-specific clues for
making predictions, and they may break on unseen domains. We remove these
spurious correlations via causal intervention. This is achieved by resampling
the appearances of potentially correlated objects independently. The proposed
approach is validated on three cross-domain segmentation tasks: cross-modality
(CT-MRI) abdominal image segmentation, cross-sequence (bSSFP-LGE) cardiac MRI
segmentation, and cross-center prostate MRI segmentation. The proposed approach
yields consistent performance gains compared with competitive methods when
tested on unseen domains.Comment: Preprin
Domain-Specific Bias Filtering for Single Labeled Domain Generalization
Conventional Domain Generalization (CDG) utilizes multiple labeled source
datasets to train a generalizable model for unseen target domains. However, due
to expensive annotation costs, the requirements of labeling all the source data
are hard to be met in real-world applications. In this paper, we investigate a
Single Labeled Domain Generalization (SLDG) task with only one source domain
being labeled, which is more practical and challenging than the CDG task. A
major obstacle in the SLDG task is the discriminability-generalization bias:
the discriminative information in the labeled source dataset may contain
domain-specific bias, constraining the generalization of the trained model. To
tackle this challenging task, we propose a novel framework called
Domain-Specific Bias Filtering (DSBF), which initializes a discriminative model
with the labeled source data and then filters out its domain-specific bias with
the unlabeled source data for generalization improvement. We divide the
filtering process into (1) feature extractor debiasing via k-means
clustering-based semantic feature re-extraction and (2) classifier
rectification through attention-guided semantic feature projection. DSBF
unifies the exploration of the labeled and the unlabeled source data to enhance
the discriminability and generalization of the trained model, resulting in a
highly generalizable model. We further provide theoretical analysis to verify
the proposed domain-specific bias filtering process. Extensive experiments on
multiple datasets show the superior performance of DSBF in tackling both the
challenging SLDG task and the CDG task.Comment: Accepted by International Journal of Computer Vision (IJCV
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