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Titlebook: Hyperion — terra incognita; Expeditionen in H?ld Hansj?rg Bay Book 1998 Springer Fachmedien Wiesbaden 1998 Beziehung.H?lderlin.Liebe.Litera

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發(fā)表于 2025-3-21 16:56:53 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Hyperion — terra incognita
副標(biāo)題Expeditionen in H?ld
編輯Hansj?rg Bay
視頻videohttp://file.papertrans.cn/431/430651/430651.mp4
概述Neue Wege in der H?lderlin-Forschung
圖書封面Titlebook: Hyperion — terra incognita; Expeditionen in H?ld Hansj?rg Bay Book 1998 Springer Fachmedien Wiesbaden 1998 Beziehung.H?lderlin.Liebe.Litera
出版日期Book 1998
關(guān)鍵詞Beziehung; H?lderlin; Liebe; Literatur; Literaturgeschichte; Literaturwissenschaft; Moderne; Musik; Phantasi
版次1
doihttps://doi.org/10.1007/978-3-322-87304-0
isbn_softcover978-3-531-13075-0
isbn_ebook978-3-322-87304-0
copyrightSpringer Fachmedien Wiesbaden 1998
The information of publication is updating

書目名稱Hyperion — terra incognita影響因子(影響力)




書目名稱Hyperion — terra incognita影響因子(影響力)學(xué)科排名




書目名稱Hyperion — terra incognita網(wǎng)絡(luò)公開度




書目名稱Hyperion — terra incognita網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Hyperion — terra incognita被引頻次




書目名稱Hyperion — terra incognita被引頻次學(xué)科排名




書目名稱Hyperion — terra incognita年度引用




書目名稱Hyperion — terra incognita年度引用學(xué)科排名




書目名稱Hyperion — terra incognita讀者反饋




書目名稱Hyperion — terra incognita讀者反饋學(xué)科排名




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發(fā)表于 2025-3-21 23:56:04 | 只看該作者
Hansj?rg Bayeceives the 3C image as input is fine-tuned and the best fine-tune manner is determined. Finally, a cascade fine-tune framework is designed to integrate the difference images and 3C image. In this paper, the size of SqueezeNet which is cascade fine-tuned on the basis of the pre-training weights is 5
板凳
發(fā)表于 2025-3-22 01:58:21 | 只看該作者
Jürgen Link the functional connectivity network for AD classification and functional connectivity analysis. The experimental results demonstrate the proposed method not only improves the classification performance, but also found alteration functional connectivity.
地板
發(fā)表于 2025-3-22 07:59:53 | 只看該作者
Ute Guzzonieceives the 3C image as input is fine-tuned and the best fine-tune manner is determined. Finally, a cascade fine-tune framework is designed to integrate the difference images and 3C image. In this paper, the size of SqueezeNet which is cascade fine-tuned on the basis of the pre-training weights is 5
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發(fā)表于 2025-3-22 10:17:10 | 只看該作者
Harald Weilnb?ckeceives the 3C image as input is fine-tuned and the best fine-tune manner is determined. Finally, a cascade fine-tune framework is designed to integrate the difference images and 3C image. In this paper, the size of SqueezeNet which is cascade fine-tuned on the basis of the pre-training weights is 5
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發(fā)表于 2025-3-22 20:15:35 | 只看該作者
Wolfram Groddeckss to facial bounding box variations, we randomly generate multiple bounding boxes according to the statistical distributions of bounding boxes and use them for initialization during training. Extensive experiments on public databases prove the superiority of our proposed method over state-of-the-ar
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發(fā)表于 2025-3-23 01:32:10 | 只看該作者
Wolf Kittler palmprint images. Lastly, we reconstruct the super-resolution palmprint images with clear palmprint-specific texture and edge characteristics via two convolutional layers with embedding a PixelShuffle. Experimental results on three public palmprint databases clearly show the effectiveness of the pr
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發(fā)表于 2025-3-23 06:09:23 | 只看該作者
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