# An empirical study of pretrained representations for few-shot classification Source: [An empirical study of pretrained representations for few-shot classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:50:28.993Z Authors: Tiago Ramalho, Thierry Sousbie, Stefano Peluchetti Published: 2019-10-03T06:31:58.000Z ## Abstract Recent algorithms with state-of-the-art few-shot classification results start their procedure by computing data features output by a large pretrained model. In this paper we systematically investigate which models provide the best representations for a few-shot image classification task when pretrained on the Imagenet dataset. We test their representations when used as the starting point for different few-shot classification algorithms. We observe that models trained on a supervised classification task have higher performance than models trained in an unsupervised manner even when transferred to out-of-distribution datasets. Models trained with adversarial robustness transfer better, while having slightly lower accuracy than supervised models. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()