Learning Meta Model for Zero- and Few-Shot Face Anti-Spoofing

A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning

Prevalent techniques in zero-shot learning do not generalize well to other related problem scenarios. Here, we present a unified approach for conventional zero-shot, generalized zero-shot and few-shot learning problems. Our approach is based on a novel Class Adapting Principal Directions (CAPD) concept that allows multiple embeddings of image features into a semantic space. Given an image, our ...

Few-Shot Learning with Meta Metric Learners

Existing few-shot learning approaches are based on either meta-learning or metriclearning, which would suffer if the tasks have varying numbers of classes and/or the tasks diverge significantly. We propose meta metric learning to deal with the limitations of the existing few-shot learning approaches. Our meta metric learning approach consists of two components, task-specific learners that explo...

Meta-SGD: Learning to Learn Quickly for Few Shot Learning

Few-shot learning is challenging for learning algorithms that learn each task in isolation and from scratch. In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial. In this paper, we develop Meta-SGD, an SGD-like, easily trainable meta-learner that can initial...

Anti-spoofing, Face

Meta-Learning for Semi-Supervised Few-Shot Classification

In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Recent progress in few-shot classification has featured meta-learning, in which a parameterized model for a learning algorithm is defined and trained on episodes representing different classification problems, each with a small labeled training set and its corres...