Task-Adaptive Feature Reweighting for Few Shot Classification

Few shot classification remains a quite challenging problem due to lacking data to train an effective classifier. Lately a few works employ the meta learning schema to learn a generalized feature encoder or distance metric, which is directly used for those unseen classes. In these approaches, the feature representation of a class remains the same even in different tasks (In meta learning, a task of few shot classification involves a set of labeled examples (support set) and a set of unlabeled examples (query set) to be classified. The goal is to get a classifier for the classes in the support set.), i.e. the feature encoder cannot adapt to different tasks. As well known, when distinguishing a class from different classes, the most discriminative feature may be different. Following this intuition, this work proposes a task-adaptive feature reweighting strategy within the framework of recently proposed prototypical network [6]. By considering the relationship between classes in a task, our method generates a feature weight for each class to highlight those features that can better distinguish it from the rest ones. As a result, each class has its own specific feature weight, and this weight is adaptively different in different tasks. The proposed method is evaluated on two few shot classification benchmarks, miniImageNet and tieredImageNet. The experiment results show that our method outperforms the state-of-the-art works demonstrating its effectiveness.

[1]  Yi Yang,et al.  Few-Shot Object Recognition from Machine-Labeled Web Images , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Sergey Levine,et al.  Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.

[3]  Bharath Hariharan,et al.  Low-Shot Visual Recognition by Shrinking and Hallucinating Features , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[4]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[6]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[7]  Andrew Zisserman,et al.  Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.

[8]  Alan L. Yuille,et al.  One Shot Learning via Compositions of Meaningful Patches , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[9]  Daan Wierstra,et al.  Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.

[10]  Oriol Vinyals,et al.  Matching Networks for One Shot Learning , 2016, NIPS.

[11]  Joshua B. Tenenbaum,et al.  Meta-Learning for Semi-Supervised Few-Shot Classification , 2018, ICLR.

[12]  Pietro Perona,et al.  One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Vishnu Naresh Boddeti,et al.  Efficient K-Shot Learning with Regularized Deep Networks , 2017, AAAI.

[14]  Hang Li,et al.  Meta-SGD: Learning to Learn Quickly for Few Shot Learning , 2017, ArXiv.

[15]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[16]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Raymond Y. K. Lau,et al.  Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[18]  Bartunov Sergey,et al.  Meta-Learning with Memory-Augmented Neural Networks , 2016 .

[19]  Richard S. Zemel,et al.  Prototypical Networks for Few-shot Learning , 2017, NIPS.

[20]  Lei Zhang,et al.  One-shot Face Recognition by Promoting Underrepresented Classes , 2017, ArXiv.

[21]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Tao Xiang,et al.  Learning to Compare: Relation Network for Few-Shot Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[23]  Joan Bruna,et al.  Few-Shot Learning with Graph Neural Networks , 2017, ICLR.

[24]  Joshua B. Tenenbaum,et al.  Human-level concept learning through probabilistic program induction , 2015, Science.

[25]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[26]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[27]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[28]  Joshua B. Tenenbaum,et al.  One-Shot Learning with a Hierarchical Nonparametric Bayesian Model , 2011, ICML Unsupervised and Transfer Learning.

[29]  Gregory R. Koch,et al.  Siamese Neural Networks for One-Shot Image Recognition , 2015 .

[30]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.