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transductive learning setting

The transductive learning setting is a machine learning paradigm in which a model has access to both labeled training data and the specific unlabeled test data during the training phase, aiming directly to predict labels for those given test instances. Unlike inductive learning, which seeks to construct a generalized decision rule capable of classifying any arbitrary unseen data point in the future, transductive learning leverages the feature distributions and structural relationships of the known test set to improve prediction accuracy on those specific samples. This approach is widely used in semi-supervised learning and zero-shot learning to help mitigate domain shift and align feature representations, though the resulting predictions are strictly tailored to the provided test set and do not automatically generalize to new, out-of-sample data points without incorporating them into the training process.

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Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly

Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly

Yongqin Xian, Christoph H. Lampert, Bernt Schiele, Zeynep Akata

OrganizationsInstitute of Science and Technology AustriaMax Planck Institute for InformaticsUniversity of Amsterdam

Why you should read this

Establishes a unified evaluation benchmark and standardized data splits to resolve widespread test-set overlap in zero-shot learning, while introducing the Animals with Attributes 2 (AWA2) dataset and systematically comparing leading methods under both standard and generalized settings.

Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it.

Added

2026-09-18