Search Results for author:
Found 11 papers, 4 papers with code
Boosting ship detection in SAR images with complementary pretraining techniques
In this paper, to resolve the problem of inconsistent imaging perspective between ImageNet and earth observations, we propose an optical ship detector (OSD) pretraining technique, which transfers the characteristics of ships in earth observations to SAR images from a large-scale aerial image dataset.
The QXS-SAROPT Dataset for Deep Learning in SAR-Optical Data Fusion
We obtain the SAR patches from SAR satellite GaoFen-3 images and the optical patches from Google Earth images.
Training few-shot classification via the perspective of minibatch and pretraining
Few-shot classification is a challenging task which aims to formulate the ability of humans to learn concepts from limited prior data and has drawn considerable attention in machine learning.
Transfer Learning with Dynamic Adversarial Adaptation Network
In this paper, we propose a novel Dynamic Adversarial Adaptation Network (DAAN) to dynamically learn domain-invariant representations while quantitatively evaluate the relative importance of global and local domain distributions.
Transfer Learning with Dynamic Distribution Adaptation
Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions.
Ranked #7 on
Domain Adaptation
on ImageCLEF-DA
Task-Driven Common Representation Learning via Bridge Neural Network
The experiments on the tasks, including pair matching, canonical correlation analysis, transfer learning, and reconstruction demonstrate the state-of-the-art performance of BNN, which may provide new insights into the aspect of common representation learning.
Easy Transfer Learning By Exploiting Intra-domain Structures
In this paper, we propose a practically Easy Transfer Learning (EasyTL) approach which requires no model selection and hyperparameter tuning, while achieving competitive performance.
Ranked #4 on
Transfer Learning
on Office-Home
Stochastic Model Pruning via Weight Dropping Away and Back
Addressing these two issues, this paper proposes the Drop Pruning approach, which leverages stochastic optimization in the pruning process by introducing a drop strategy at each pruning step, namely, drop away, which stochastically deletes some unimportant weights, and drop back, which stochastically recovers some pruned weights.
Deep Transfer Learning for Cross-domain Activity Recognition
In this paper, we propose an effective Unsupervised Source Selection algorithm for Activity Recognition (USSAR).
Cross-Domain Activity Recognition
Human Activity Recognition
+1
Visual Domain Adaptation with Manifold Embedded Distribution Alignment
Existing methods either attempt to align the cross-domain distributions, or perform manifold subspace learning.
Ranked #1 on
Domain Adaptation
on Office-Caltech-10
Cross-position Activity Recognition with Stratified Transfer Learning
STL consists of two components: Stratified Domain Selection (STL-SDS) can select the most similar source domain to the target domain; Stratified Activity Transfer (STL-SAT) is able to perform accurate knowledge transfer.
