Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained Learning

The recognition ability of human beings is developed in a progressive way. Usually, children learn to discriminate various objects from coarse to fine-grained with limited supervision. Inspired by this learning process, we propose a simple yet effective model for the Few-Shot Fine-Grained (FSFG) recognition, which tries to tackle the challenging fine-grained recognition task using meta-learning. The proposed method, named Pairwise Alignment Bilinear Network (PABN), is an end-to-end deep neural network. Unlike traditional deep bilinear networks for fine-grained classification, which adopt the self-bilinear pooling to capture the subtle features of images, the proposed model uses a novel pairwise bilinear pooling to compare the nuanced differences between base images and query images for learning a deep distance metric. In order to match base image features with query image features, we design feature alignment losses before the proposed pairwise bilinear pooling. Experiment results on four fine-grained classification datasets and one generic few-shot dataset demonstrate that the proposed model outperforms both the state-ofthe-art few-shot fine-grained and general few-shot methods.Comment: ICME 2019 Ora

A Survey of Deep Meta-Learning

Deep neural networks can achieve great successes when presented with large data sets and sufficient computational resources. However, their ability to learn new concepts quickly is quite limited. Meta-learning is one approach to address this issue, by enabling the network to learn how to learn. The exciting field of Deep Meta-Learning advances at great speed, but lacks a unified, insightful overview of current techniques. This work presents just that. After providing the reader with a theoretical foundation, we investigate and summarize key methods, which are categorized into i) metric-, ii) model-, and iii) optimization-based techniques. In addition, we identify the main open challenges, such as performance evaluations on heterogeneous benchmarks, and reduction of the computational costs of meta-learning.Comment: Extended version of book chapter in 'Metalearning: Applications to Automated Machine Learning and Data Mining' (2nd edition, forthcoming

Meta learning for few shot learning

Few-shot learning aims to scale visual recognition to open-ended growth of new classes with limited labelled examples, thus alleviating data and computation bottleneck of conventional deep learning. This thesis proposes a meta learning (a.k.a. learning to learn), paradigm to tackle the real-world few shot learning challenges. Firstly, we present a parameterized multi-metric based meta learning algorithm (RelationNet2). Existing metric learning algorithms are always based on training a global deep embedding and metric to support image similarity matching, but we propose a deep comparison network comprised of embedding and relation modules learning multiple non-linear distance metrics based on different levels of features simultaneously. Furthermore, images are represented as \todo{a} distribution rather than vectors via learning parameterized Gaussian noise regularization, reducing overfitting and enable the use of deeper embeddings. We next consider the fact that several recent competitors develop effective few-shot learners through strong conventional representations in combination with very simple classifiers, questioning whether “meta-learning” is necessary or highly effective features are sufficient. To defend meta-learning, we take an approach agnostic to the off-the-shelf features, and focus exclusively on meta-learning the final classifier layer. Specifically, we introduce MetaQDA, a Bayesian meta-learning extension of quadratic discriminant analysis classifier, that is complementary to advances in feature representations, leading to high accuracy and state-of-the-art uncertainty calibration performance in predictions. Finally, we investigate the extension of MetaQDA to more generalized real-world scenarios beyond the narrow standard few-shot benchmarks. Our model achieves both many-shot and few-shot classification accuracy in generalized few-shot learning. In terms of few-shot class-incremental learning, MetaQDA is inherently suitable to novel classes growing \todo{scenarios}. As for open-set recognition, we calculate the probability belonging to novel class by Bayes' Rule, maintaining high accuracy in both close-set recognition and open-set rejection. Overall, our contributions in few-shot meta-learning advance state of the art under both accuracy and calibration metrics, explore a series of increasingly realistic problem settings, to support more researchers and practitioners in future exploration