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Found 9 papers, 5 papers with code
Exploring representation learning for flexible few-shot tasks
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.
Probing Few-Shot Generalization with Attributes
Despite impressive progress in deep learning, generalizing far beyond the training distribution is an important open challenge.
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes
Few-shot classification (FSC), the task of adapting a classifier to unseen classes given a small labeled dataset, is an important step on the path toward human-like machine learning.
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models
Most deep learning models rely on expressive high-dimensional representations to achieve good performance on tasks such as classification.
Learning Latent Subspaces in Variational Autoencoders
We consider the problem of unsupervised learning of features correlated to specific labels in a dataset.
Lorentzian Distance Learning
This formulation produces node representations close to the centroid of their descendants.
Meta-Learning for Semi-Supervised Few-Shot Classification
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.
Prototypical Networks for Few-shot Learning
We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class.
Learning to Generate Images with Perceptual Similarity Metrics
We propose instead to use a loss function that is better calibrated to human perceptual judgments of image quality: the multiscale structural-similarity score (MS-SSIM).
