scholarly journals Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap

2021 ◽  
Vol 7 (10) ◽  
pp. 198
Author(s):  
Mattia Litrico ◽  
Sebastiano Battiato ◽  
Sotirios A. Tsaftaris ◽  
Mario Valerio Giuffrida
Keyword(s):  
Mean Squared Error ◽  
Domain Adaptation ◽  
Input Image ◽  
Cell Counting ◽  
Global Optimality ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Specific Domain ◽  
Counting Problem ◽  
Real World Datasets

This paper proposes a novel approach for semi-supervised domain adaptation for holistic regression tasks, where a DNN predicts a continuous value y∈R given an input image x. The current literature generally lacks specific domain adaptation approaches for this task, as most of them mostly focus on classification. In the context of holistic regression, most of the real-world datasets not only exhibit a covariate (or domain) shift, but also a label gap—the target dataset may contain labels not included in the source dataset (and vice versa). We propose an approach tackling both covariate and label gap in a unified training framework. Specifically, a Generative Adversarial Network (GAN) is used to reduce covariate shift, and label gap is mitigated via label normalisation. To avoid overfitting, we propose a stopping criterion that simultaneously takes advantage of the Maximum Mean Discrepancy and the GAN Global Optimality condition. To restore the original label range—that was previously normalised—a handful of annotated images from the target domain are used. Our experimental results, run on 3 different datasets, demonstrate that our approach drastically outperforms the state-of-the-art across the board. Specifically, for the cell counting problem, the mean squared error (MSE) is reduced from 759 to 5.62; in the case of the pedestrian dataset, our approach lowered the MSE from 131 to 1.47. For the last experimental setup, we borrowed a task from plant biology, i.e., counting the number of leaves in a plant, and we ran two series of experiments, showing the MSE is reduced from 2.36 to 0.88 (intra-species), and from 1.48 to 0.6 (inter-species).

scholarly journals Multi-Source Domain Adaptation for Visual Sentiment Classification

2020 ◽  
Vol 34 (03) ◽  
pp. 2661-2668
Author(s):  
Chuang Lin ◽  
Sicheng Zhao ◽  
Lei Meng ◽  
Tat-Seng Chua
Keyword(s):  
Domain Adaptation ◽  
Sentiment Classification ◽  
Similar Distribution ◽  
Single Source ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Source Domain ◽  
Adversarial Network ◽  
Latent Space ◽  
Benchmark Datasets

Existing domain adaptation methods on visual sentiment classification typically are investigated under the single-source scenario, where the knowledge learned from a source domain of sufficient labeled data is transferred to the target domain of loosely labeled or unlabeled data. However, in practice, data from a single source domain usually have a limited volume and can hardly cover the characteristics of the target domain. In this paper, we propose a novel multi-source domain adaptation (MDA) method, termed Multi-source Sentiment Generative Adversarial Network (MSGAN), for visual sentiment classification. To handle data from multiple source domains, it learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution. This is achieved via cycle consistent adversarial learning in an end-to-end manner. Extensive experiments conducted on four benchmark datasets demonstrate that MSGAN significantly outperforms the state-of-the-art MDA approaches for visual sentiment classification.

scholarly journals Unsupervised multi-source domain adaptation with no observable source data

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253415
Author(s):  
Hyunsik Jeon ◽  
Seongmin Lee ◽  
U Kang
Keyword(s):  
Real World ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Multiple Source ◽  
Multiple Sources ◽  
Target Domain ◽  
Source Domain ◽  
Source Data ◽  
Real World Datasets ◽  
Target Data

Given trained models from multiple source domains, how can we predict the labels of unlabeled data in a target domain? Unsupervised multi-source domain adaptation (UMDA) aims for predicting the labels of unlabeled target data by transferring the knowledge of multiple source domains. UMDA is a crucial problem in many real-world scenarios where no labeled target data are available. Previous approaches in UMDA assume that data are observable over all domains. However, source data are not easily accessible due to privacy or confidentiality issues in a lot of practical scenarios, although classifiers learned in source domains are readily available. In this work, we target data-free UMDA where source data are not observable at all, a novel problem that has not been studied before despite being very realistic and crucial. To solve data-free UMDA, we propose DEMS (Data-free Exploitation of Multiple Sources), a novel architecture that adapts target data to source domains without exploiting any source data, and estimates the target labels by exploiting pre-trained source classifiers. Extensive experiments for data-free UMDA on real-world datasets show that DEMS provides the state-of-the-art accuracy which is up to 27.5% point higher than that of the best baseline.

scholarly journals Category-Sensitive Domain Adaptation for Land Cover Mapping in Aerial Scenes

2019 ◽  
Vol 11 (22) ◽  
pp. 2631 ◽  
Author(s):  
Bo Fang ◽  
Rong Kou ◽  
Li Pan ◽  
Pengfei Chen
Keyword(s):  
Land Cover ◽  
Domain Adaptation ◽  
Feature Space ◽  
Aerial Images ◽  
Land Cover Mapping ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Source Domain ◽  
Adversarial Network ◽  
Semantic Labeling

Since manually labeling aerial images for pixel-level classification is expensive and time-consuming, developing strategies for land cover mapping without reference labels is essential and meaningful. As an efficient solution for this issue, domain adaptation has been widely utilized in numerous semantic labeling-based applications. However, current approaches generally pursue the marginal distribution alignment between the source and target features and ignore the category-level alignment. Therefore, directly applying them to land cover mapping leads to unsatisfactory performance in the target domain. In our research, to address this problem, we embed a geometry-consistent generative adversarial network (GcGAN) into a co-training adversarial learning network (CtALN), and then develop a category-sensitive domain adaptation (CsDA) method for land cover mapping using very-high-resolution (VHR) optical aerial images. The GcGAN aims to eliminate the domain discrepancies between labeled and unlabeled images while retaining their intrinsic land cover information by translating the features of the labeled images from the source domain to the target domain. Meanwhile, the CtALN aims to learn a semantic labeling model in the target domain with the translated features and corresponding reference labels. By training this hybrid framework, our method learns to distill knowledge from the source domain and transfers it to the target domain, while preserving not only global domain consistency, but also category-level consistency between labeled and unlabeled images in the feature space. The experimental results between two airborne benchmark datasets and the comparison with other state-of-the-art methods verify the robustness and superiority of our proposed CsDA.

scholarly journals Unsupervised Domain Adaptation by Matching Distributions Based on the Maximum Mean Discrepancy via Unilateral Transformations

2019 ◽  
Vol 33 ◽  
pp. 4106-4113 ◽  
Author(s):  
Atsutoshi Kumagai ◽  
Tomoharu Iwata
Keyword(s):  
Domain Adaptation ◽  
Feature Space ◽  
Classification Performance ◽  
Target Domain ◽  
Target Feature ◽  
Maximum Mean Discrepancy ◽  
Source Domain ◽  
Unsupervised Domain Adaptation ◽  
Real World Datasets ◽  
Target Data

We propose a simple yet effective method for unsupervised domain adaptation. When training and test distributions are different, standard supervised learning methods perform poorly. Semi-supervised domain adaptation methods have been developed for the case where labeled data in the target domain are available. However, the target data are often unlabeled in practice. Therefore, unsupervised domain adaptation, which does not require labels for target data, is receiving a lot of attention. The proposed method minimizes the discrepancy between the source and target distributions of input features by transforming the feature space of the source domain. Since such unilateral transformations transfer knowledge in the source domain to the target one without reducing dimensionality, the proposed method can effectively perform domain adaptation without losing information to be transfered. With the proposed method, it is assumed that the transformed features and the original features differ by a small residual to preserve the relationship between features and labels. This transformation is learned by aligning the higher-order moments of the source and target feature distributions based on the maximum mean discrepancy, which enables to compare two distributions without density estimation. Once the transformation is found, we learn supervised models by using the transformed source data and their labels. We use two real-world datasets to demonstrate experimentally that the proposed method achieves better classification performance than existing methods for unsupervised domain adaptation.

Multiple Graphs and Low-Rank Embedding for Multi-Source Heterogeneous Domain Adaptation

2022 ◽  
Vol 16 (4) ◽  
pp. 1-25
Author(s):  
Hanrui Wu ◽  
Michael K. Ng
Keyword(s):  
Domain Adaptation ◽  
Low Rank ◽  
Multiple Sources ◽  
Target Domain ◽  
Structure Information ◽  
Learning Procedure ◽  
Original Target ◽  
Real World Datasets ◽  
Iterative Optimization Algorithm ◽  
Multiple Domains

Multi-source domain adaptation is a challenging topic in transfer learning, especially when the data of each domain are represented by different kinds of features, i.e., Multi-source Heterogeneous Domain Adaptation (MHDA). It is important to take advantage of the knowledge extracted from multiple sources as well as bridge the heterogeneous spaces for handling the MHDA paradigm. This article proposes a novel method named Multiple Graphs and Low-rank Embedding (MGLE), which models the local structure information of multiple domains using multiple graphs and learns the low-rank embedding of the target domain. Then, MGLE augments the learned embedding with the original target data. Specifically, we introduce the modules of both domain discrepancy and domain relevance into the multiple graphs and low-rank embedding learning procedure. Subsequently, we develop an iterative optimization algorithm to solve the resulting problem. We evaluate the effectiveness of the proposed method on several real-world datasets. Promising results show that the performance of MGLE is better than that of the baseline methods in terms of several metrics, such as AUC, MAE, accuracy, precision, F1 score, and MCC, demonstrating the effectiveness of the proposed method.

Adversarial Domain Adaptation of Asymmetric Mapping with Coral Alignment for Intelligent Fault Diagnosis

2021 ◽  
Author(s):  
Ranran Li ◽  
Shunming Li ◽  
Kun Xu ◽  
Xianglian Li ◽  
Jiantao Lu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Vital Role ◽  
Variable Speed ◽  
Target Domain ◽  
Rolling Bearings ◽  
Specific Domain ◽  
Class Level ◽  
Decision Boundaries ◽  
Transfer Accuracy

Abstract Rolling bearings play a vital role in the overall operation of rotating machineries. In practical diagnosis, many learning methods for variable speed fault diagnosis ignore task-specific decision boundaries, which make it very difficult to match feature distributions between different domains completely. Therefore, an adversarial domain adaptation of asymmetric mapping with coral alignment (ADA-AMCA) is presented to dispose this problem. By using the asymmetric mapping feature extractor, more features of specific domain with obvious distinction can be extracted. Meanwhile, combining the maximum classifier discrepancy of deep transfer to form an adversarial approach, and the task-specific decision boundary is taken into account, the class-level alignment between the features of source domain and target domain is attempted. For the sake of preventing degenerate learning which is possibly caused by asymmetric mapping and adversarial learning, the model is constrained by deep coral to extract more domain invariant features. Experimental results show that the proposed method can solve the variable speed fault diagnosis problem well, with high transfer accuracy and strong generalization.

scholarly journals Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation

2018 ◽  
Author(s):  
Yuguang Yan ◽  
Wen Li ◽  
Hanrui Wu ◽  
Huaqing Min ◽  
Mingkui Tan ◽  
...  
Keyword(s):  
Metric Spaces ◽  
Optimal Transport ◽  
Domain Adaptation ◽  
Learning Performance ◽  
Target Domain ◽  
Source Domain ◽  
Feature Spaces ◽  
Label Information ◽  
Real World Datasets ◽  
Heterogeneous Source

Heterogeneous domain adaptation (HDA) aims to exploit knowledge from a heterogeneous source domain to improve the learning performance in a target domain. Since the feature spaces of the source and target domains are different, the transferring of knowledge is extremely difficult. In this paper, we propose a novel semi-supervised algorithm for HDA by exploiting the theory of optimal transport (OT), a powerful tool originally designed for aligning two different distributions. To match the samples between heterogeneous domains, we propose to preserve the semantic consistency between heterogeneous domains by incorporating label information into the entropic Gromov-Wasserstein discrepancy, which is a metric in OT for different metric spaces, resulting in a new semi-supervised scheme. Via the new scheme, the target and transported source samples with the same label are enforced to follow similar distributions. Lastly, based on the Kullback-Leibler metric, we develop an efficient algorithm to optimize the resultant problem. Comprehensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our proposed method.

scholarly journals AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach

2022 ◽  
Author(s):  
Xiao Song ◽  
Guorun Yang ◽  
Xinge Zhu ◽  
Hui Zhou ◽  
Yuexin Ma ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Input Image ◽  
Target Domain ◽  
Color Transfer ◽  
Cross Domain ◽  
Domain Performance ◽  
Effective Domain ◽  
Adaptation Ability

AbstractRecently, records on stereo matching benchmarks are constantly broken by end-to-end disparity networks. However, the domain adaptation ability of these deep models is quite limited. Addressing such problem, we present a novel domain-adaptive approach called AdaStereo that aims to align multi-level representations for deep stereo matching networks. Compared to previous methods, our AdaStereo realizes a more standard, complete and effective domain adaptation pipeline. Firstly, we propose a non-adversarial progressive color transfer algorithm for input image-level alignment. Secondly, we design an efficient parameter-free cost normalization layer for internal feature-level alignment. Lastly, a highly related auxiliary task, self-supervised occlusion-aware reconstruction is presented to narrow the gaps in output space. We perform intensive ablation studies and break-down comparisons to validate the effectiveness of each proposed module. With no extra inference overhead and only a slight increase in training complexity, our AdaStereo models achieve state-of-the-art cross-domain performance on multiple benchmarks, including KITTI, Middlebury, ETH3D and DrivingStereo, even outperforming some state-of-the-art disparity networks finetuned with target-domain ground-truths. Moreover, based on two additional evaluation metrics, the superiority of our domain-adaptive stereo matching pipeline is further uncovered from more perspectives. Finally, we demonstrate that our method is robust to various domain adaptation settings, and can be easily integrated into quick adaptation application scenarios and real-world deployments.

scholarly journals Visual Domain Adaptation by Consensus-Based Transfer to Intermediate Domain

2020 ◽  
Vol 34 (07) ◽  
pp. 10655-10662
Author(s):  
Jongwon Choi ◽  
Youngjoon Choi ◽  
Jihoon Kim ◽  
Jinyeop Chang ◽  
Ilhwan Kwon ◽  
...  
Keyword(s):  
Real World ◽  
Domain Adaptation ◽  
Ensemble Classifiers ◽  
Training Algorithm ◽  
Target Domain ◽  
Intermediate Domain ◽  
Previous State ◽  
Real World Datasets ◽  
Visual Domain ◽  
Adaptation Scenarios

We describe an unsupervised domain adaptation framework for images by a transform to an abstract intermediate domain and ensemble classifiers seeking a consensus. The intermediate domain can be thought as a latent domain where both the source and target domains can be transferred easily. The proposed framework aligns both domains to the intermediate domain, which greatly improves the adaptation performance when the source and target domains are notably dissimilar. In addition, we propose an ensemble model trained by confusing multiple classifiers and letting them make a consensus alternately to enhance the adaptation performance for ambiguous samples. To estimate the hidden intermediate domain and the unknown labels of the target domain simultaneously, we develop a training algorithm using a double-structured architecture. We validate the proposed framework in hard adaptation scenarios with real-world datasets from simple synthetic domains to complex real-world domains. The proposed algorithm outperforms the previous state-of-the-art algorithms on various environments.

scholarly journals Multi-Path and Group-Loss-Based Network for Speech Emotion Recognition in Multi-Domain Datasets

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1579 ◽  
Author(s):  
Kyoung Ju Noh ◽  
Chi Yoon Jeong ◽  
Jiyoun Lim ◽  
Seungeun Chung ◽  
Gague Kim ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Short Term Memory ◽  
Domain Adaptation ◽  
Classification Model ◽  
Speech Emotion Recognition ◽  
Target Domain ◽  
Model Generalization ◽  
Speech Database ◽  
Emotion Labels ◽  
Temporal Feature

Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labels and the weak generalization of the SER model for an unseen target domain. This study proposes a multi-path and group-loss-based network (MPGLN) for SER to support multi-domain adaptation. The proposed model includes a bidirectional long short-term memory-based temporal feature generator and a transferred feature extractor from the pre-trained VGG-like audio classification model (VGGish), and it learns simultaneously based on multiple losses according to the association of emotion labels in the discrete and dimensional models. For the evaluation of the MPGLN SER as applied to multi-cultural domain datasets, the Korean Emotional Speech Database (KESD), including KESDy18 and KESDy19, is constructed, and the English-speaking Interactive Emotional Dyadic Motion Capture database (IEMOCAP) is used. The evaluation of multi-domain adaptation and domain generalization showed 3.7% and 3.5% improvements, respectively, of the F1 score when comparing the performance of MPGLN SER with a baseline SER model that uses a temporal feature generator. We show that the MPGLN SER efficiently supports multi-domain adaptation and reinforces model generalization.

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