Few shot image classification aims at learning a classifier from limited labeled data. Generating the classification weights has been applied in many meta-learning approaches for few shot image classification due to its simplicity and effectiveness. However, we argue that it is difficult to generate the exact and universal classification weights for all the diverse query samples from very few training samples. In this work, we introduce Attentive Weights Generation for few shot learning via Information Maximization (AWGIM), which addresses current issues by two novel contributions. i) AWGIM generates different classification weights for different query samples by letting each of query samples attends to the whole support set. ii) To guarantee the generated weights adaptive to different query sample, we re-formulate the problem to maximize the lower bound of mutual information between generated weights and query as well as support data. As far as we can see, this is the first attempt to unify information maximization into few shot learning. Both two contributions are proved to be effective in the extensive experiments and we show that AWGIM is able to achieve state-of-the-art performance on benchmark datasets. While deep learning methods achieve great success in domains such as computer vision (He et al., 2016) , natural language processing (Devlin et al., 2018) , reinforcement learning (Silver et al., 2018) , their hunger for large amount of labeled data limits the application scenarios where only a few data are available for training. Humans, in contrast, are able to learn from limited data, which is desirable for deep learning methods. Few shot learning is thus proposed to enable deep models to learn from very few samples (Fei-Fei et al., 2006) . Meta learning is by far the most popular and promising approach for few shot problems (Vinyals et al., 2016; Finn et al., 2017; Snell et al., 2017; Ravi & Larochelle, 2016; Rusu et al., 2019) . In meta learning approaches, the model extracts high level knowledge across different tasks so that it can adapt itself quickly to a new-coming task (Schmidhuber, 1987; Andrychowicz et al., 2016) . There are several kinds of meta learning methods for few shot learning, such as gradient-based (Finn et al., 2017; Ravi & Larochelle, 2016) and metric-based (Snell et al., 2017; Sung et al., 2018) . Weights generation, among these different methods, has shown effectiveness with simple formulation (Qi et al., 2018; Qiao et al., 2018; Gidaris & Komodakis, 2018; . In general, weights generation methods learn to generate the classification weights for different tasks conditioned on the limited labeled data. However, fixed classification weights for different query samples within one task might be sub-optimal, due to the few shot challenge. We introduce Attentive Weights Generation for few shot learning via Information Maximization (AWGIM) in this work to address these limitations. In AWGIM, the classification weights are generated for each query sample specifically. This is done by two encoding paths where the query sample attends to the task context. However, we show in experiments that simple cross attention between query samples and support set fails to guarantee classification weights fitted to diverse query data since the query-specific information is lost during weights generation. Therefore, we propose to maximize the lower bound of mutual information between generated weights and query, support data. As far as we know, AWGIM is the first work introducing Variational Information Maximization in few shot learning. The induced computational overhead is minimal due to the nature of few shot problems. Furthermore, by maximizing the lower bound of mutual information, AWGIM gets rid of inner update without compromising performance. AWGIM is evaluated on two benchmark datasets and shows state-of-the-art performance. We also conducted detailed analysis to validate the contribution of each component in AWGIM. 2 RELATED WORKS 2.1 FEW SHOT LEARNING Learning from few labeled training data has received growing attentions recently. Most successful existing methods apply meta learning to solve this problem and can be divided into several categories. In the gradient-based approaches, an optimal initialization for all tasks is learned (Finn et al., 2017) . Ravi & Larochelle (2016) learned a meta-learner LSTM directly to optimize the given fewshot classification task. Sun et al. (2019) learned the transformation for activations of each layer by gradients to better suit the current task. In the metric-based methods, a similarity metric between query and support samples is learned. (Koch et al., 2015; Vinyals et al., 2016; Snell et al., 2017; Sung et al., 2018; Li et al., 2019a) . Spatial information or local image descriptors are also considered in some works to compute richer similarities (Lifchitz et al., 2019; Li et al., 2019b; Wertheimer & Hariharan, 2019) . Generating the classification weights directly has been explored by some works. Gidaris & Komodakis (2018) generated classification weights as linear combinations of weights for base and novel classes. Similarly, Qiao et al. (2018) and Qi et al. (2018) both generated the classification weights from activations of a trained feature extractor. Graph neural network denoising autoencoders are used in (Gidaris & Komodakis, 2019) . Munkhdalai & Yu (2017) proposed to generate "fast weights" from the loss gradient for each task. All these methods do not consider generating different weights for different query examples, nor maximizing the mutual information. There are some other methods for few-shot classification. Generative models are used to generate or hallucinate more data in Wang et al., 2018; Chen et al., 2019) . Bertinetto et al. (2019) and used the closed-form solutions directly for few shot classification. integrated label propagation on a transductive graph to predict the query class label. In this work, we introduce Attentive Weights Generation via Information Maximization (AWGIM) for few shot image classification. AWGIM learns to generate optimal classification weights for each query sample within the task by two encoding paths. To guarantee this, the lower bound of mutual information between generated weights and query, support data is maximized. As far as we know, AWGIM is the first work utilizing mutual information techniques for few shot learning. The effectiveness of AWGIM is demonstrated by state-of-the-art performance on two benchmark datasets and extensive analysis.
A novel few shot learning method to generate query-specific classification weights via information maximization.
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