278,032 research outputs found
Unsupervised Domain Adaptation by Backpropagation
Top-performing deep architectures are trained on massive amounts of labeled
data. In the absence of labeled data for a certain task, domain adaptation
often provides an attractive option given that labeled data of similar nature
but from a different domain (e.g. synthetic images) are available. Here, we
propose a new approach to domain adaptation in deep architectures that can be
trained on large amount of labeled data from the source domain and large amount
of unlabeled data from the target domain (no labeled target-domain data is
necessary).
As the training progresses, the approach promotes the emergence of "deep"
features that are (i) discriminative for the main learning task on the source
domain and (ii) invariant with respect to the shift between the domains. We
show that this adaptation behaviour can be achieved in almost any feed-forward
model by augmenting it with few standard layers and a simple new gradient
reversal layer. The resulting augmented architecture can be trained using
standard backpropagation.
Overall, the approach can be implemented with little effort using any of the
deep-learning packages. The method performs very well in a series of image
classification experiments, achieving adaptation effect in the presence of big
domain shifts and outperforming previous state-of-the-art on Office datasets
Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering
Domain adaptation (DA) aims to alleviate the domain shift between source
domain and target domain. Most DA methods require access to the source data,
but often that is not possible (e.g. due to data privacy or intellectual
property). In this paper, we address the challenging source-free domain
adaptation (SFDA) problem, where the source pretrained model is adapted to the
target domain in the absence of source data. Our method is based on the
observation that target data, which might not align with the source domain
classifier, still forms clear clusters. We capture this intrinsic structure by
defining local affinity of the target data, and encourage label consistency
among data with high local affinity. We observe that higher affinity should be
assigned to reciprocal neighbors. To aggregate information with more context,
we consider expanded neighborhoods with small affinity values. Furthermore, we
consider the density around each target sample, which can alleviate the
negative impact of potential outliers. In the experimental results we verify
that the inherent structure of the target features is an important source of
information for domain adaptation. We demonstrate that this local structure can
be efficiently captured by considering the local neighbors, the reciprocal
neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art
performance on several 2D image and 3D point cloud recognition datasets.Comment: Accepted by IEEE TPAMI, extended version of conference paper
arXiv:2110.0420
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation
Unsupervised domain adaptation (UDA) has been a vital protocol for migrating
information learned from a labeled source domain to facilitate the
implementation in an unlabeled heterogeneous target domain. Although UDA is
typically jointly trained on data from both domains, accessing the labeled
source domain data is often restricted, due to concerns over patient data
privacy or intellectual property. To sidestep this, we propose "off-the-shelf
(OS)" UDA (OSUDA), aimed at image segmentation, by adapting an OS segmentor
trained in a source domain to a target domain, in the absence of source domain
data in adaptation. Toward this goal, we aim to develop a novel batch-wise
normalization (BN) statistics adaptation framework. In particular, we gradually
adapt the domain-specific low-order BN statistics, e.g., mean and variance,
through an exponential momentum decay strategy, while explicitly enforcing the
consistency of the domain shareable high-order BN statistics, e.g., scaling and
shifting factors, via our optimization objective. We also adaptively quantify
the channel-wise transferability to gauge the importance of each channel, via
both low-order statistics divergence and a scaling factor.~Furthermore, we
incorporate unsupervised self-entropy minimization into our framework to boost
performance alongside a novel queued, memory-consistent self-training strategy
to utilize the reliable pseudo label for stable and efficient unsupervised
adaptation. We evaluated our OSUDA-based framework on both cross-modality and
cross-subtype brain tumor segmentation and cardiac MR to CT segmentation tasks.
Our experimental results showed that our memory consistent OSUDA performs
better than existing source-relaxed UDA methods and yields similar performance
to UDA methods with source data.Comment: Published in Medical Image Analysis (extension of MICCAI paper
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