7 research outputs found
Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation
Domain shift is a common problem in clinical applications, where the training
images (source domain) and the test images (target domain) are under different
distributions. Unsupervised Domain Adaptation (UDA) techniques have been
proposed to adapt models trained in the source domain to the target domain.
However, those methods require a large number of images from the target domain
for model training. In this paper, we propose a novel method for Few-Shot
Unsupervised Domain Adaptation (FSUDA), where only a limited number of
unlabeled target domain samples are available for training. To accomplish this
challenging task, first, a spectral sensitivity map is introduced to
characterize the generalization weaknesses of models in the frequency domain.
We then developed a Sensitivity-guided Spectral Adversarial MixUp (SAMix)
method to generate target-style images to effectively suppresses the model
sensitivity, which leads to improved model generalizability in the target
domain. We demonstrated the proposed method and rigorously evaluated its
performance on multiple tasks using several public datasets.Comment: Accepted by MICCAI 202
Look, Cast and Mold: Learning 3D Shape Manifold from Single-view Synthetic Data
Inferring the stereo structure of objects in the real world is a challenging
yet practical task. To equip deep models with this ability usually requires
abundant 3D supervision which is hard to acquire. It is promising that we can
simply benefit from synthetic data, where pairwise ground-truth is easy to
access. Nevertheless, the domain gaps are nontrivial considering the variant
texture, shape and context. To overcome these difficulties, we propose a
Visio-Perceptual Adaptive Network for single-view 3D reconstruction, dubbed
VPAN. To generalize the model towards a real scenario, we propose to fulfill
several aspects: (1) Look: visually incorporate spatial structure from the
single view to enhance the expressiveness of representation; (2) Cast:
perceptually align the 2D image features to the 3D shape priors with
cross-modal semantic contrastive mapping; (3) Mold: reconstruct stereo-shape of
target by transforming embeddings into the desired manifold. Extensive
experiments on several benchmarks demonstrate the effectiveness and robustness
of the proposed method in learning the 3D shape manifold from synthetic data
via a single-view. The proposed method outperforms state-of-the-arts on Pix3D
dataset with IoU 0.292 and CD 0.108, and reaches IoU 0.329 and CD 0.104 on
Pascal 3D+
Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation
Domain adaptation is to transfer the shared knowledge learned from the source
domain to a new environment, i.e., target domain. One common practice is to
train the model on both labeled source-domain data and unlabeled target-domain
data. Yet the learned models are usually biased due to the strong supervision
of the source domain. Most researchers adopt the early-stopping strategy to
prevent over-fitting, but when to stop training remains a challenging problem
since the lack of the target-domain validation set. In this paper, we propose
one efficient bootstrapping method, called Adaboost Student, explicitly
learning complementary models during training and liberating users from
empirical early stopping. Adaboost Student combines the deep model learning
with the conventional training strategy, i.e., adaptive boosting, and enables
interactions between learned models and the data sampler. We adopt one adaptive
data sampler to progressively facilitate learning on hard samples and aggregate
"weak" models to prevent over-fitting. Extensive experiments show that (1)
Without the need to worry about the stopping time, AdaBoost Student provides
one robust solution by efficient complementary model learning during training.
(2) AdaBoost Student is orthogonal to most domain adaptation methods, which can
be combined with existing approaches to further improve the state-of-the-art
performance. We have achieved competitive results on three widely-used scene
segmentation domain adaptation benchmarks.Comment: 10 pages, 7 tables, 5 figure