5 research outputs found
Privacy Preserving Domain Adaptation for Semantic Segmentation of Medical Images
Convolutional neural networks (CNNs) have led to significant improvements in
tasks involving semantic segmentation of images. CNNs are vulnerable in the
area of biomedical image segmentation because of distributional gap between two
source and target domains with different data modalities which leads to domain
shift. Domain shift makes data annotations in new modalities necessary because
models must be retrained from scratch. Unsupervised domain adaptation (UDA) is
proposed to adapt a model to new modalities using solely unlabeled target
domain data. Common UDA algorithms require access to data points in the source
domain which may not be feasible in medical imaging due to privacy concerns. In
this work, we develop an algorithm for UDA in a privacy-constrained setting,
where the source domain data is inaccessible. Our idea is based on encoding the
information from the source samples into a prototypical distribution that is
used as an intermediate distribution for aligning the target domain
distribution with the source domain distribution. We demonstrate the
effectiveness of our algorithm by comparing it to state-of-the-art medical
image semantic segmentation approaches on two medical image semantic
segmentation datasets
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation
In few-shot domain adaptation (FDA), classifiers for the target domain are
trained with accessible labeled data in the source domain (SD) and few labeled
data in the target domain (TD). However, data usually contain private
information in the current era, e.g., data distributed on personal phones.
Thus, the private information will be leaked if we directly access data in SD
to train a target-domain classifier (required by FDA methods). In this paper,
to thoroughly prevent the privacy leakage in SD, we consider a very challenging
problem setting, where the classifier for the TD has to be trained using few
labeled target data and a well-trained SD classifier, named few-shot hypothesis
adaptation (FHA). In FHA, we cannot access data in SD, as a result, the private
information in SD will be protected well. To this end, we propose a target
orientated hypothesis adaptation network (TOHAN) to solve the FHA problem,
where we generate highly-compatible unlabeled data (i.e., an intermediate
domain) to help train a target-domain classifier. TOHAN maintains two deep
networks simultaneously, where one focuses on learning an intermediate domain
and the other takes care of the intermediate-to-target distributional
adaptation and the target-risk minimization. Experimental results show that
TOHAN outperforms competitive baselines significantly
Watch, read and lookup: learning to spot signs from multiple supervisors
The focus of this work is sign spotting - given a video of an isolated sign,
our task is to identify whether and where it has been signed in a continuous,
co-articulated sign language video. To achieve this sign spotting task, we
train a model using multiple types of available supervision by: (1) watching
existing sparsely labelled footage; (2) reading associated subtitles (readily
available translations of the signed content) which provide additional
weak-supervision; (3) looking up words (for which no co-articulated labelled
examples are available) in visual sign language dictionaries to enable novel
sign spotting. These three tasks are integrated into a unified learning
framework using the principles of Noise Contrastive Estimation and Multiple
Instance Learning. We validate the effectiveness of our approach on low-shot
sign spotting benchmarks. In addition, we contribute a machine-readable British
Sign Language (BSL) dictionary dataset of isolated signs, BSLDict, to
facilitate study of this task. The dataset, models and code are available at
our project page.Comment: Appears in: Asian Conference on Computer Vision 2020 (ACCV 2020) -
Oral presentation. 29 page