Search Results for author: Eleni Triantafillou

Found 9 papers, 3 papers with code

Learning a Universal Template for Few-shot Dataset Generalization

1 code implementation14 May 2021 Eleni Triantafillou, Hugo Larochelle, Richard Zemel, Vincent Dumoulin

Few-shot dataset generalization is a challenging variant of the well-studied few-shot classification problem where a diverse training set of several datasets is given, for the purpose of training an adaptable model that can then learn classes from new datasets using only a few examples.

Learning Flexible Classifiers with Shot-CONditional Episodic (SCONE) Training

no code implementations1 Jan 2021 Eleni Triantafillou, Vincent Dumoulin, Hugo Larochelle, Richard Zemel

We discover that fine-tuning on episodes of a particular shot can specialize the pre-trained model to solving episodes of that shot at the expense of performance on other shots, in agreement with a trade-off recently observed in the context of end-to-end episodic training.

General Classification

Exploring representation learning for flexible few-shot tasks

no code implementations1 Jan 2021 Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard Zemel

In this work, we consider a realistic setting where the relationship between examples can change from episode to episode depending on the task context, which is not given to the learner.

Few-Shot Learning Representation Learning

Probing Few-Shot Generalization with Attributes

no code implementations10 Dec 2020 Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard Zemel

Despite impressive progress in deep learning, generalizing far beyond the training distribution is an important open challenge.

Few-Shot Learning Zero-Shot Learning

Out-of-distribution Detection in Few-shot Classification

no code implementations25 Sep 2019 Kuan-Chieh Wang, Paul Vicol, Eleni Triantafillou, Chia-Cheng Liu, Richard Zemel

In this work, we propose tasks for out-of-distribution detection in the few-shot setting and establish benchmark datasets, based on four popular few-shot classification datasets.

Classification Out-of-Distribution Detection

Meta-Learning for Semi-Supervised Few-Shot Classification

8 code implementations ICLR 2018 Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel

To address this paradigm, we propose novel extensions of Prototypical Networks (Snell et al., 2017) that are augmented with the ability to use unlabeled examples when producing prototypes.

Classification General Classification +1

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