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Showing 1 to 6 of 6 for “"few-shot classification"”.
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Fine-tuning generative models
… original dataset and in downstream tasks such as classification. Most current algorithms, however, require training a bespoke model from scratch, which can be both expensive and time-consuming. Instead, we propose various methods of fine-tuning pre-trained generative models to achieve these goals, …
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Learning from Weak Supervision: Theory, Methods, and Applications
… accelerated this trend because their zero- and few-shot classification performance enables them to serve as effective “synthetic annotators” for various tasks. In practice, the data generated by these weak annotators is imperfect, but it enables the training of strong models. However, …
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Image Classification With Unstructured Collections
… about a scene. Previous work in multi-view image classification typically focuses on classifying structured collection data. In this paradigm, the key object, feature, or perspective of each image is predetermined and uniform across all collections. Consequently, classification methods for …
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Generalizing Under Data Scarcity. Enhancing the representation capability from few samples.
… transformers for supervised and unsupervised few-shot tasks. We explore how transformers behave when trained on structured, multi-domain datasets under controlled conditions, where train/test contamination can be explicitly avoided. By reframing few-shot learning as a sequence modeling …
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Advances in Few-Shot Learning for Image Classification and Tabular Data
… data. These constraints underscore the need for few-shot learning methods, enabling models to adapt from just a handful of examples. In addition, many of these applications require updating only a small fraction of model parameters for adaptation -- a crucial form of parameter efficiency -- to …
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Meta-learning representations with relational structure
… in three ways. The first identifies polythetic classification as a natural setting and shows how the self-organisation of datapoints under attention can be used to empower meta-learning classifiers. The second uses explicit relational inference to modulate and recombine neural modules for fast …