A Subspace Based Transfer Joint Matching with Laplacian Regularization for Visual Domain Adaptation

Sanodiya, Rakesh Kumar; Yao, Leehter

doi:10.3390/s20164367

Cited by 10 publications

(3 citation statements)

References 36 publications

(96 reference statements)

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“…To demonstrate the efficiency of our VTL, the results of our experiments are compared with several unsupervised domain adaptation methods including EMFS (2018) [40], EasyTL (2019) [41], STJML (2020) [42], GEF (2019) [43], DWDA (2021) [44], CDMA (2020) [45], ALML (2022) [46], TTLC (2021) [33], SGA-MDAP (2020) [47], NSO (2020) [48], FSUTL (2020) [49], PLC (2021) [50], GSI (2021) [51] and ICDAV (2022) [52]. In the experiments, VTL begins with learning a domain invariant and class discriminative latent feature space according to Equation (18).…”

Section: Resultsmentioning

confidence: 99%

Visual Transductive learning via Iterative Label Correction

Rezaie,

Ahmadvand,

Tahmoresnezhad

2023

Preprint

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Unsupervised domain adaptation (UDA) aims to transfer knowledge across domains when there is no labeled data available in the target domain. In this way, UDA methods attempt to utilize pseudo-labeled target samples to align distribution across the source and target domains. To mitigate the negative impact of inaccurate pseudo-labeling on knowledge transfer, we propose a novel UDA method known as visual transductive learning via iterative label correction (VTL) that benefits from a novel label correction paradigm. Specifically, we learn a domain invariant and class discriminative latent feature representation for samples of both source and target domains. Simultaneously, VTL attempts to locally align the information of samples by learning the geometric structure of samples. In the new shared feature space, the novel label correction paradigm is utilized to effectively maximize the reliability of pseudo labels by correcting inaccurate pseudo-labeled target samples, which significantly improves VTL classification performance. The experimental results on several object recognition tasks verified our proposed VTL superiority in comparison with other state-of-the-arts.

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Section: Resultsmentioning

confidence: 99%

Visual Transductive learning via Iterative Label Correction

Rezaie,

Ahmadvand,

Tahmoresnezhad

2023

Preprint

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“…Specifically, domain adaptation, as a classical method of transfer learning, exhibits remarkable properties in addressing cross-domain distribution differences, which is exactly one of the challenges we are aiming to conquer in FIPS. Typically, transfer component analysis (TCA) [ 47 ] and joint distribution adaptation (JDA) [ 48 , 49 ] are effective at narrowing down the distribution discrepancies between fingerprint databases and target samples, facilitating the subsequent high-accuracy positioning. In [ 50 ], a transfer learning-based approach was employed to learn common patterns in fingerprint data from different Wi-Fi RSS indoor positioning datasets, and smaller positioning errors were achieved.…”

Section: Related Workmentioning

confidence: 99%

Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning

Huang

Guo

et al. 2022

Sensors

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Distribution discrepancy is an intrinsic challenge in existing fingerprint-based indoor positioning system(s) (FIPS) due to real-time environmental variations; thus, the positioning model needs to be reconstructed frequently based on newly collected training data. However, it is expensive or impossible to collect adequate training samples to reconstruct the fingerprint database. Fortunately, transfer learning has proven to be an effective solution to mitigate the distribution discrepancy, enabling us to update the positioning model using newly collected training data in real time. However, in practical applications, traditional transfer learning algorithms no longer act well to feature space heterogeneity caused by different types or holding postures of fingerprint collection devices (such as smartphones). Moreover, current heterogeneous transfer methods typically require enough accurately labeled samples in the target domain, which is practically expensive and even unavailable. Aiming to solve these problems, a heterogeneous transfer learning framework based on co-occurrence data (HTL-CD) is proposed for FIPS, which can realize higher positioning accuracy and robustness against environmental changes without reconstructing the fingerprint database repeatedly. Specifically, the source domain samples are mapped into the feature space in the target domain, then the marginal and conditional distributions of the source and target samples are aligned in order to minimize the distribution divergence caused by collection device heterogeneity and environmental changes. Moreover, the utilized co-occurrence fingerprint data enables us to calculate correlation coefficients between heterogeneous samples without accurately labeled target samples. Furthermore, by resorting to the adopted correlation restriction mechanism, more valuable knowledge will be transferred to the target domain if the source samples are related to the target ones, which remarkably relieves the “negative transfer" issue. Real-world experimental performance implies that, even without accurately labeled samples in the target domain, the proposed HTL-CD can obtain at least 17.15% smaller average localization errors (ALEs) than existing transfer learning-based positioning methods, which further validates the effectiveness and superiority of our algorithm.

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“…Nowadays, transfer learning has gained significant attention and has been successfully applied in various domains, including precision agriculture, natural language processing, computer vision, and robotics [26]- [28]. Its application has shown promising results in addressing real-world problems across multiple fields such as industrial, security and surveillance, healthcare, agriculture, automobile, and finance.…”

Section: Introductionmentioning

confidence: 99%

Zero-Shot Transfer Learning Framework for Plant Leaf Disease Classification

Satya Rajendra Singh,

Sanodiya

2023

IEEE Access

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Agriculture's pivotal role in sustaining livelihoods and driving economic growth is widely recognized, yet various challenges like the adverse effects of climate change and limited resource availability hinder its productivity. Notably, plants are susceptible to various viruses and bacteria, impacting yield and food security. The emergence of deep learning, particularly convolutional neural networks (CNNs), has transformed agriculture by facilitating tasks such as disease detection. However, a significant challenge arises from the often unrealistic assumption that training and testing data share the same distribution. To address this, domain adaptation and transfer learning techniques have been employed, bridging the gap between different data distributions. Therefore, a novel framework named 'Zero-Shot Transfer Learning' is introduced. This addresses the challenge of improving classifier performance when trained on a source domain with different classes and tested on a target domain, exemplified by tomato and potato datasets. More specifically, in this framework, we include different CNN models along with techniques such as data augmentation, synthetic data generation, and robust discriminative losses, enhancing classifier performance in zero-shot scenarios. Extensive experiments on plant leaf disease classification under the zero-shot Transfer Learning assumption demonstrate the superiority of the proposed framework for effective disease classification. Ultimately, this framework holds the potential to promote crop yield optimization and ensure food security.

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A Subspace Based Transfer Joint Matching with Laplacian Regularization for Visual Domain Adaptation

Cited by 10 publications

References 36 publications

Visual Transductive learning via Iterative Label Correction

Visual Transductive learning via Iterative Label Correction

Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning

Zero-Shot Transfer Learning Framework for Plant Leaf Disease Classification

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