Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark. Meta and transfer learning are two successful families of approaches to few-shot learning. Despite highly related goals, state-of-the-art advances in each family are measured largely in isolation of each other.
Apr 6, 2021
We introduce a few-shot classification evaluation protocol named VTAB+MD with the explicit goal of facilitating sharing of insights from each community.
We find that, on average, large-scale transfer methods (Big Transfer, BiT) outperform competing approaches on MD, even when trained only on ImageNet. In ...
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Dec 6, 2021 · A unified few-shot classification benchmark to compare transfer and meta learning approaches. Dec 6, 2021
A unified few-shot classifi- cation benchmark to compare transfer and meta learning approaches. ... Meta- learning for semi-supervised few-shot classification.
Apr 7, 2021 · Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark Finds that large-scale transfer methods (e.g. ...
A new benchmark for training and evaluating models that is large-scale, consists of diverse datasets, and presents more realistic tasks.
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Jun 29, 2023 · In this survey, we illuminate one of the key paradigms in few-shot learning called meta-learning. These meta-learning methods, by simulating the tasks which ...
We showcase our new meta-dataset by performing an experimental evaluation for several use cases, including transfer learning, few-shot meta-learning, and cross- ...
Few-shot classification aims to solve classification tasks on unseen classes with only a few available examples [4]. It is necessary to ensure that the model ...