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- W4386081567 abstract "Zero-shot learning models achieve remarkable results on image classification for samples from classes that were not seen during training. However, such models must be trained from scratch with specialised methods: therefore, access to a training dataset is required when the need for zero-shot classification arises. In this paper, we aim to equip pre-trained models with zero-shot classification capabilities without the use of image data. We achieve this with our proposed Image-free Classifier Injection with Semantics (ICIS) that injects classifiers for new, unseen classes into pre-trained classification models in a post-hoc fashion without relying on image data. Instead, the existing classifier weights and simple class-wise descriptors, such as class names or attributes, are used. ICIS has two encoder-decoder networks that learn to reconstruct classifier weights from descriptors (and vice versa), exploiting (cross-)reconstruction and cosine losses to regularise the decoding process. Notably, ICIS can be cheaply trained and applied directly on top of pre-trained classification models. Experiments on benchmark ZSL datasets show that ICIS produces unseen classifier weights that achieve strong (generalised) zero-shot classification performance. Code is available at https://github.com/ExplainableML/ImageFreeZSL ." @default.
- W4386081567 created "2023-08-23" @default.
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- W4386081567 date "2023-08-21" @default.
- W4386081567 modified "2023-09-27" @default.
- W4386081567 title "Image-free Classifier Injection for Zero-Shot Classification" @default.
- W4386081567 doi "https://doi.org/10.48550/arxiv.2308.10599" @default.
- W4386081567 hasPublicationYear "2023" @default.
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