Joint category-level and discriminative feature learning networks for unsupervised domain adaptation

Zhang, Pengyu; Huang, Junchu; Zhou, Zhiheng; Chen, Zengqun; Shang, Junyuan; Niu, Chang; Yang, Zhiwei

doi:10.3233/jifs-191136

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Store a copy of the user's visited content in the cache. When the content is accessed next time, it does not need to connect to the hosted website, but is provided by the copy retained in the cache, so as to reduce the response time of the customer's request [6,7]. Web content can be cached on the client, proxy, and server side.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of Low Delay Deterministic Network Based on Data Mining Association Analysis

Gong

2021

JWE

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The purpose of this paper is to research on the design and analysis of low delay deterministic network based on data mining association. This paper studies and implements the algorithm of mining page association rules. A session recognition algorithm based on log reference page and request time is proposed by using the time probability relationship of continuous requests. This method improves the accuracy of log data preprocessing and page association rules mining. This paper studies and tests the efficiency, prefetch timing and cache organization of the two page association rules. The results show that in this prefetch scheme, the prefetch performance of the association rules of unordered pages is better than that of the association rules of ordered pages. The prefetch performance when cache hits is better than that when cache fails and the cache fails to hit has better performance. In the case of a certain size of cache space, reasonable organization of cache space can further improve the cache hit rate and reduce the network delay.

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Section: Introductionmentioning

confidence: 99%

Design and Analysis of Low Delay Deterministic Network Based on Data Mining Association Analysis

Gong

2021

JWE

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Inter-class distribution alienation and inter-domain distribution alignment based on manifold embedding for domain adaptation

Zhu

et al. 2020

IFS

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Domain adaptation (DA) aims to train a robust predictor by transferring rich knowledge from a well-labeled source domain to annotate a newly coming target domain; however, the two domains are usually drawn from very different distributions. Most current methods either learn the common features by matching inter-domain feature distributions and training the classifier separately or align inter-domain label distributions to directly obtain an adaptive classifier based on the original features despite feature distortion. Moreover, intra-domain information may be greatly degraded during the DA process; i.e., the source data samples from different classes might grow closer. To this end, this paper proposes a novel DA approach, referred to as inter-class distribution alienation and inter-domain distribution alignment based on manifold embedding (IDAME). Specifically, IDAME commits to adapting the classifier on the Grassmann manifold by using structural risk minimization, where inter-domain feature distributions are aligned to mitigate feature distortion, and the target pseudo labels are exploited using the distances on the Grassmann manifold. During the classifier adaptation process, we simultaneously consider the inter-class distribution alienation, the inter-domain distribution alignment, and the manifold consistency. Extensive experiments validate that IDAME can outperform several comparative state-of-the-art methods on real-world cross-domain image datasets.

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Joint category-level and discriminative feature learning networks for unsupervised domain adaptation

Cited by 2 publications

References 29 publications

Design and Analysis of Low Delay Deterministic Network Based on Data Mining Association Analysis

Design and Analysis of Low Delay Deterministic Network Based on Data Mining Association Analysis

Inter-class distribution alienation and inter-domain distribution alignment based on manifold embedding for domain adaptation

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