Semanlink - [1503.08677] Label-Embedding for Image Classification

Tags:

Au sujet de ce document

sl:arxiv_author :
sl:arxiv_firstAuthor : Zeynep Akata
sl:arxiv_num : 1503.08677
sl:arxiv_published : 2015-03-30T14:04:34Z
sl:arxiv_summary : Attributes act as intermediate representations that enable parameter sharing between classes, a must when training data is scarce. We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. We introduce a function that measures the compatibility between an image and a label embedding. The parameters of this function are learned on a training set of labeled samples to ensure that, given an image, the correct classes rank higher than the incorrect ones. Results on the Animals With Attributes and Caltech-UCSD-Birds datasets show that the proposed framework outperforms the standard Direct Attribute Prediction baseline in a zero-shot learning scenario. Label embedding enjoys a built-in ability to leverage alternative sources of information instead of or in addition to attributes, such as e.g. class hierarchies or textual descriptions. Moreover, label embedding encompasses the whole range of learning settings from zero-shot learning to regular learning with a large number of labeled examples.@en
sl:arxiv_title : Label-Embedding for Image Classification@en
sl:arxiv_updated : 2015-10-01T10:48:38Z
sl:bookmarkOf : https://arxiv.org/abs/1503.08677
sl:creationDate : 2020-02-18
sl:creationTime : 2020-02-18T15:00:20Z

Infos sur le fichier

Bookmark of: https://arxiv.org/abs/1503.08677

Documents with similar tags (experimental)

[1911.11506] Word-Class Embeddings for Multiclass Text Classification

Tags:

> In supervised tasks such as multiclass
text classification (the focus of this article) it seems appealing to enhance word representations
with ad-hoc embeddings that encode task-specific information. We propose (supervised) word-class
embeddings (WCEs), and show that, when concatenated to (unsupervised) pre-trained word embeddings,
they substantially facilitate the training of deep-learning models in multiclass classification by
topic.
>
> A differentiating aspect of our method is that it keeps the modelling of word-class interactions separate from the
original word embedding. Word-class correlations are confined in a dedicated vector space, whose vectors enhance
(by concatenation) the unsupervised representations. The net effect is an embedding matrix that is better suited to
classification, and imposes no restriction to the network architecture using it.

[github](https://github.com/AlexMoreo/word-class-embeddings). Refers to [LEAM](doc:2020/02/joint_embedding_of_words_and_la) :

> [in LEAM] Once words and labels are embedded in a common vector space, word-label
compatibility is measured via cosine similarity. Our method instead models these compatibilities directly, without
generating intermediate embeddings for words or labels.

2020-10-11 A propos

[1805.04174] Joint Embedding of Words and Labels for Text Classification (ACL Anthology 2018)

Tags:

2020-02-18 A propos