Iterative joint classifier and domain adaptation for visual transfer learning

Saray, Shiva Noori; Tahmoresnezhad, Jafar

doi:10.1007/s13042-021-01428-z

Cited by 8 publications

(2 citation statements)

References 47 publications

(26 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

Self Cite

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“…Iterative joint classifier and domain adaptation for visual transfer learning (ICDAV) [29] uses the balanced maximum mean discrepancy to better domain adaptation. Moreover, for learning a robust classifier against domain shift, a set of graph manifold regularizers and modified joint probability maximum mean discrepancy is simultaneously applied to maintain the domain structures and adapt the distribution of projected samples during the model learning process.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Domain Adaptation for Image Classification Using Non-Euclidean Triplet Loss

Sarhan

Balafar²,

Feizi‐Derakhshi³

2022

Electronics

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In recent years, computer vision tasks have increasingly used deep learning techniques. In some tasks, however, due to insufficient data, the model is not properly trained, leading to a decrease in generalizability. When trained on a dataset and tested on another similar dataset, the model predicts near-random results. This paper presents an unsupervised multi-source domain adaptation that improves transfer learning and increases generalizability. In the proposed method, a new module infers the source of the input data based on its extracted features. By making the features extractor compete against this objective, the learned feature representation generalizes better across the sources. As a result, representations similar to those from different sources are learned. That is, the extracted representation is generic and independent of any particular domain. In the training stage, a non-Euclidean triplet loss function is also utilized. Similar representations for samples belonging to the same class can be learned more effectively using the proposed loss function. We demonstrate how the developed framework may be applied to enhance accuracy and outperform the outcomes of already effective transfer learning methodologies. We demonstrate how the proposed strategy performs particularly well when dealing with various dataset domains or when there are insufficient data.

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