19 research outputs found
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation
Unsupervised Domain Adaptation (UDA) aims to bridge the gap between a source
domain, where labelled data are available, and a target domain only represented
with unlabelled data. If domain invariant representations have dramatically
improved the adaptability of models, to guarantee their good transferability
remains a challenging problem. This paper addresses this problem by using
active learning to annotate a small budget of target data. Although this setup,
called Active Domain Adaptation (ADA), deviates from UDA's standard setup, a
wide range of practical applications are faced with this situation. To this
purpose, we introduce \textit{Stochastic Adversarial Gradient Embedding}
(SAGE), a framework that makes a triple contribution to ADA. First, we select
for annotation target samples that are likely to improve the representations'
transferability by measuring the variation, before and after annotation, of the
transferability loss gradient. Second, we increase sampling diversity by
promoting different gradient directions. Third, we introduce a novel training
procedure for actively incorporating target samples when learning invariant
representations. SAGE is based on solid theoretical ground and validated on
various UDA benchmarks against several baselines. Our empirical investigation
demonstrates that SAGE takes the best of uncertainty \textit{vs} diversity
samplings and improves representations transferability substantially
Multi-Domain Active Learning: A Comparative Study
Building classifiers on multiple domains is a practical problem in the real
life. Instead of building classifiers one by one, multi-domain learning (MDL)
simultaneously builds classifiers on multiple domains. MDL utilizes the
information shared among the domains to improve the performance. As a
supervised learning problem, the labeling effort is still high in MDL problems.
Usually, this high labeling cost issue could be relieved by using active
learning. Thus, it is natural to utilize active learning to reduce the labeling
effort in MDL, and we refer this setting as multi-domain active learning
(MDAL). However, there are only few works which are built on this setting. And
when the researches have to face this problem, there is no off-the-shelf
solutions. Under this circumstance, combining the current multi-domain learning
models and single-domain active learning strategies might be a preliminary
solution for MDAL problem. To find out the potential of this preliminary
solution, a comparative study over 5 models and 4 selection strategies is made
in this paper. To the best of our knowledge, this is the first work provides
the formal definition of MDAL. Besides, this is the first comparative work for
MDAL problem. From the results, the Multinomial Adversarial Networks (MAN)
model with a simple best vs second best (BvSB) uncertainty strategy shows its
superiority in most cases. We take this combination as our off-the-shelf
recommendation for the MDAL problem
UniDA3D: Unified Domain Adaptive 3D Semantic Segmentation Pipeline
State-of-the-art 3D semantic segmentation models are trained on off-the-shelf
public benchmarks, but they will inevitably face the challenge of recognition
accuracy drop when these well-trained models are deployed to a new domain. In
this paper, we introduce a Unified Domain Adaptive 3D semantic segmentation
pipeline (UniDA3D) to enhance the weak generalization ability, and bridge the
point distribution gap between domains. Different from previous studies that
only focus on a single adaptation task, UniDA3D can tackle several adaptation
tasks in 3D segmentation field, by designing a unified source-and-target active
sampling strategy, which selects a maximally-informative subset from both
source and target domains for effective model adaptation. Besides, benefiting
from the rise of multi-modal 2D-3D datasets, UniDA3D investigates the
possibility of achieving a multi-modal sampling strategy, by developing a
cross-modality feature interaction module that can extract a representative
pair of image and point features to achieve a bi-directional image-point
feature interaction for safe model adaptation. Experimentally, UniDA3D is
verified to be effective in many adaptation tasks including: 1) unsupervised
domain adaptation, 2) unsupervised few-shot domain adaptation; 3) active domain
adaptation. Their results demonstrate that, by easily coupling UniDA3D with
off-the-shelf 3D segmentation baselines, domain generalization ability of these
baselines can be enhanced