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**References**

http://arxiv.org/abs/1605.06065

One-shot Learning with Memory-Augmented Neural Networks

Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of “one-shot learning.”

http://arxiv.org/abs/1606.04080v1 Matching Networks for One Shot Learning

Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data. In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories. Our framework learns a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types.

There are a few key insights in this work. Firstly, one-shot learning is much easier if you train the network to do one-shot learning. Secondly, non-parametric structures in a neural network make it easier for networks to remember and adapt to new training sets in the same tasks.

http://arxiv.org/abs/1603.05106v2 One-Shot Generalization in Deep Generative Models

We develop a class of sequential generative models that are built on the principles of feedback and attention. These two characteristics lead to generative models that are among the state-of-the art in density estimation and image generation. We demonstrate the one-shot generalization ability of our models using three tasks: unconditional sampling, generating new exemplars of a given concept, and generating new exemplars of a family of concepts. In all cases our models are able to generate compelling and diverse samples—having seen new examples just once—providing an important class of general-purpose models for one-shot machine learning.

Stochastic computational graph showing conditional probabilities and computational steps for sequential generative models. A represents an attentional mechanism that uses function fw for writings and function fr for reading.

http://www.cs.toronto.edu/~rsalakhu/papers/oneshot1.pdf Siamese Neural Networks for One-shot Image Recognition

We hypothesize that networks which do well at at verification should generalize to one-shot classification. The verification model learns to identify input pairs according to the probability that they belong to the same class or different classes. This model can then be used to evaluate new images, exactly one per novel class, in a pairwise manner against the test image. The pairing with the highest score according to the verification network is then awarded the highest probability for the one-shot task. If the features learned by the verification model are sufficient to confirm or deny the identity of characters from one set of alphabets, then they ought to be sufficient for other alphabets, provided that the model has been exposed to a variety of alphabets to encourage variance amongst the learned features.

Our general strategy. 1) Train a model to discriminate between a collection of same/different pairs. 2) Generalize to evaluate new categories based on learned feature mappings for verification.

A simple 2 hidden layer siamese network for binary classification with logistic prediction p. The structure of the network is replicated across the top and bottom sections to form twin networks, with shared weight matrices at each layer.

https://arxiv.org/pdf/1603.05106.pdf One-Shot Generalization in Deep Generative Models

We develop machine learning systems with this important capacity by developing new deep generative models, models that combine the representational power of deep learning with the inferential power of Bayesian reasoning. We develop a class of sequential generative models that are built on the principles of feedback and attention. These two characteristics lead to generative models that are among the state-of-the art in density estimation and image generation.

https://arxiv.org/abs/1611.03199 Low Data Drug Discovery with One-shot Learning

We introduce a new architecture, the residual LSTM embedding, that, when combined with graph convolutional neural networks, significantly improves the ability to learn meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery.

https://openreview.net/pdf?id=rJY0-Kcll OPTIMIZATION AS A MODEL FOR FEW-SHOT LEARNING

Though deep neural networks have shown great success in the large data domain, they generally perform poorly on few-shot learning tasks, where a classifier has to quickly generalize after seeing very few examples from each class. The general belief is that gradient-based optimization in high capacity classifiers requires many iterative steps over many examples to perform well. Here, we propose an LSTMbased meta-learner model to learn the exact optimization algorithm used to train another learner neural network classifier in the few-shot regime. The parametrization of our model allows it to learn appropriate parameter updates specifically for the scenario where a set amount of updates will be made, while also learning a general initialization of the learner (classifier) network that allows for quick convergence of training. We demonstrate that this meta-learning model is competitive with deep metric-learning techniques for few-shot learning.

https://arxiv.org/abs/1703.00837v1 Meta Networks

Meta Networks (MetaNet), that acquires a meta-level knowledge across tasks and shifts its inductive bias via fast parameterization for the rapid generalization. When tested on the standard one-shot learning benchmarks, our MetaNet models achieved near human-level accuracy. We demonstrated several appealing properties of MetaNet relating to generalization and continual learning.

https://arxiv.org/abs/1703.00767v1 Attentive Recurrent Comparators

Many problems in Artificial Intelligence and Machine Learning can be reduced to the problem of quantitative comparison of two entities. In Deep Learning the ubiquitous architecture used for this task is the Siamese Neural Network which maps each entity to a representation through a learnable function and expresses similarity through the distances among the entities in the representation space. In this paper, we argue that such a static and invariant mapping is both naive and unnatural. We develop a novel neural model called Attentive Recurrent Comparators (ARCs) that dynamically compares two entities and test the model extensively on the Omniglot dataset. In the task of similarity learning, our simplistic model that does not use any convolutions performs on par with Deep Convolutional Siamese Networks and significantly better when convolutional layers are also used. In the challenging task of one-shot learning on the same dataset, an ARC based model achieves the first super-human performance for a neural method with an error rate of 1.5\%.

https://arxiv.org/abs/1703.03400v1 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on a few-shot image classification benchmark, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.

https://arxiv.org/abs/1703.04394v1 Zero-Shot Learning - The Good, the Bad and the Ugly

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. 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. 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 limitations of the current status of the area which can be taken as a basis for advancing it.

https://arxiv.org/abs/1703.05175v1 Prototypical Networks for Few-shot Learning

Prototypical networks learn a metric space in which classification can be performed by computing Euclidean distances to prototype representations of each class. Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve state-of-the-art results.

https://arxiv.org/pdf/1703.07464v1.pdf No Fuss Distance Metric Learning using Proxies

We address the problem of distance metric learning (DML), defined as learning a distance consistent with a notion of semantic similarity. Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship – an anchor point x is similar to a set of positive points Y, and dissimilar to a set of negative points Z, and a loss defined over these distances is minimized. While the specifics of the optimization differ, in this work we collectively call this type of supervision Triplets and all methods that follow this pattern Triplet-Based methods. These methods are challenging to optimize. A main issue is the need for finding informative triplets, which is usually achieved by a variety of tricks such as increasing the batch size, hard or semi-hard triplet mining, etc, but even with these tricks, the convergence rate of such methods is slow. In this paper we propose to optimize the triplet loss on a different space of triplets, consisting of an anchor data point and similar and dissimilar proxy points. These proxies approximate the original data points, so that a triplet loss over the proxies is a tight upper bound of the original loss. This proxy-based loss is empirically better behaved. As a result, the proxy-loss improves on state-of-art results for three standard zero-shot learning datasets, by up to 15% points, while converging three times as fast as other triplet-based losses.

https://arxiv.org/abs/1703.03400v1 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on a few-shot image classification benchmark, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.

https://arxiv.org/abs/1707.00600v1 Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly

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.

https://arxiv.org/pdf/1703.05175.pdf Prototypical Networks for Few-shot Learning

Prototypical networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning.

We have proposed a simple method called prototypical networks for few-shot learning based on the idea that we can represent each class by the mean of its examples in a representation space learned by a neural network. We train these networks to specifically perform well in the few-shot setting by using episodic training. The approach is far simpler and more efficient than recent meta-learning approaches, and produces state-of-the-art results even without sophisticated extensions developed for matching networks (although these can be applied to prototypical nets as well).

https://openreview.net/pdf?id=BJ_QxP1AZ UNLEASHING THE POTENTIAL OF CNNS FOR INTERPRETABLE FEW-SHOT LEARNING

https://arxiv.org/pdf/1711.06025.pdf Learning to Compare: Relation Network for Few-Shot Learning

We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each. Our method, called the Relation Network (RN), is trained end-to-end from scratch. During meta-learning, it learns to learn a deep distance metric to compare a small number of images within episodes, each of which is designed to simulate the few-shot setting. Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network. Besides providing improved performance on few-shot learning, our framework is easily extended to zero-shot learning. Extensive experiments on four datasets demonstrate that our simple approach provides a unified and effective approach for both of these two tasks.

https://arxiv.org/pdf/1711.04043.pdf FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS

https://github.com/vgsatorras/few-shot-gnn

We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-passing inference algorithms with their neural-network counterparts, we define a graph neural network architecture that generalizes several of the recently proposed few-shot learning models. Besides providing improved numerical performance, our framework is easily extended to variants of few-shot learning, such as semi-supervised or active learning, demonstrating the ability of graph-based models to operate well on ‘relational’ tasks.

https://arxiv.org/pdf/1603.05106.pdf One-Shot Generalization in Deep Generative Models

We develop machine learning systems with this important capacity by developing new deep generative models, models that combine the representational power of deep learning with the inferential power of Bayesian reasoning. We develop a class of sequential generative models that are built on the principles of feedback and attention. These two characteristics lead to generative models that are among the state-of-the art in density estimation and image generation. We demonstrate the one-shot generalization ability of our models using three tasks: unconditional sampling, generating new exemplars of a given concept, and generating new exemplars of a family of concepts.