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In this paper, we propose a novel active learning\nframework, which is capable of building a competitive classifier with optimal\nfeature representation via a limited amount of labeled training instances in an\nincremental learning manner. Our approach advances the existing active learning\nmethods in two aspects. First, we incorporate deep convolutional neural\nnetworks into active learning. Through the properly designed framework, the\nfeature representation and the classifier can be simultaneously updated with\nprogressively annotated informative samples. Second, we present a\ncost-effective sample selection strategy to improve the classification\nperformance with less manual annotations. Unlike traditional methods focusing\non only the uncertain samples of low prediction confidence, we especially\ndiscover the large amount of high confidence samples from the unlabeled set for\nfeature learning. Specifically, these high confidence samples are automatically\nselected and iteratively assigned pseudo-labels. We thus call our framework\n\"Cost-Effective Active Learning\" (CEAL) standing for the two\nadvantages.Extensive experiments demonstrate that the proposed CEAL framework\ncan achieve promising results on two challenging image classification datasets,\ni.e., face recognition on CACD database [1] and object categorization on\nCaltech-256 [2].","url_abs":"http://arxiv.org/abs/1701.03551v1","url_pdf":"http://arxiv.org/pdf/1701.03551v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cost-effective-active-learning-for-deep-image","repo_url":"https://github.com/tueboesen/Active-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"cost-effective-active-learning-for-deep-image","repo_url":"https://github.com/tueboesen/a-optimal-active-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"cost-effective-active-learning-for-deep-image","repo_url":"https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"cost-effective-active-learning-for-deep-image","repo_url":"https://github.com/dhaalves/CEAL_keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"incremental-learning","task_name":"Incremental Learning"},{"task_slug":"object-categorization","task_name":"Object Categorization"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.03551","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1701.03551"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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