Temperature network for few-shot learning with distribution-aware large-margin metric
• A simple and general approach is proposed to enhance the prototype-based few-shot learning methods, which can theoretically lead to compact intra-class distributions. • We propose the Temperature Network which can implicitly generate query-specific prototypes. Moreover, in order to best utilize li...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhu, Wei [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Übergeordnetes Werk: |
Enthalten in: Association between dopa decarboxylase gene variants and borderline personality disorder - Mobascher, Arian ELSEVIER, 2014, the journal of the Pattern Recognition Society, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:112 ; year:2021 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.patcog.2020.107797 |
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| 520 | |a • A simple and general approach is proposed to enhance the prototype-based few-shot learning methods, which can theoretically lead to compact intra-class distributions. • We propose the Temperature Network which can implicitly generate query-specific prototypes. Moreover, in order to best utilize limited training samples, we further propose to train in a hard mode to exhaustively mine the large-margin metric. • We conduct comprehensive experiments on several publicly available datasets as well as the proposed Dermnet skin disease dataset to validate the proposed method. | ||
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10.1016/j.patcog.2020.107797 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001299.pica (DE-627)ELV052912612 (ELSEVIER)S0031-3203(20)30600-2 DE-627 ger DE-627 rakwb eng Zhu, Wei verfasserin aut Temperature network for few-shot learning with distribution-aware large-margin metric 2021 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A simple and general approach is proposed to enhance the prototype-based few-shot learning methods, which can theoretically lead to compact intra-class distributions. • We propose the Temperature Network which can implicitly generate query-specific prototypes. Moreover, in order to best utilize limited training samples, we further propose to train in a hard mode to exhaustively mine the large-margin metric. • We conduct comprehensive experiments on several publicly available datasets as well as the proposed Dermnet skin disease dataset to validate the proposed method. Li, Wenbin oth Liao, Haofu oth Luo, Jiebo oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:112 year:2021 pages:0 https://doi.org/10.1016/j.patcog.2020.107797 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 112 2021 0 |
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10.1016/j.patcog.2020.107797 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001299.pica (DE-627)ELV052912612 (ELSEVIER)S0031-3203(20)30600-2 DE-627 ger DE-627 rakwb eng Zhu, Wei verfasserin aut Temperature network for few-shot learning with distribution-aware large-margin metric 2021 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A simple and general approach is proposed to enhance the prototype-based few-shot learning methods, which can theoretically lead to compact intra-class distributions. • We propose the Temperature Network which can implicitly generate query-specific prototypes. Moreover, in order to best utilize limited training samples, we further propose to train in a hard mode to exhaustively mine the large-margin metric. • We conduct comprehensive experiments on several publicly available datasets as well as the proposed Dermnet skin disease dataset to validate the proposed method. Li, Wenbin oth Liao, Haofu oth Luo, Jiebo oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:112 year:2021 pages:0 https://doi.org/10.1016/j.patcog.2020.107797 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 112 2021 0 |
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Temperature network for few-shot learning with distribution-aware large-margin metric |
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• A simple and general approach is proposed to enhance the prototype-based few-shot learning methods, which can theoretically lead to compact intra-class distributions. • We propose the Temperature Network which can implicitly generate query-specific prototypes. Moreover, in order to best utilize limited training samples, we further propose to train in a hard mode to exhaustively mine the large-margin metric. • We conduct comprehensive experiments on several publicly available datasets as well as the proposed Dermnet skin disease dataset to validate the proposed method. |
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• A simple and general approach is proposed to enhance the prototype-based few-shot learning methods, which can theoretically lead to compact intra-class distributions. • We propose the Temperature Network which can implicitly generate query-specific prototypes. Moreover, in order to best utilize limited training samples, we further propose to train in a hard mode to exhaustively mine the large-margin metric. • We conduct comprehensive experiments on several publicly available datasets as well as the proposed Dermnet skin disease dataset to validate the proposed method. |
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• A simple and general approach is proposed to enhance the prototype-based few-shot learning methods, which can theoretically lead to compact intra-class distributions. • We propose the Temperature Network which can implicitly generate query-specific prototypes. Moreover, in order to best utilize limited training samples, we further propose to train in a hard mode to exhaustively mine the large-margin metric. • We conduct comprehensive experiments on several publicly available datasets as well as the proposed Dermnet skin disease dataset to validate the proposed method. |
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