Year: 2018

Abstract:
We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning scenarios. Compared to the well-studied many-class many-shot and few-class few-shot problems, MCFS problem commonly occurs in practical applications but is rarely studied. MCFS brings new challenges because it needs to distinguish between many classes, but only a few samples per class are available for training. In this paper, we propose “memory-augmented hierarchical-classification network (MahiNet)” for MCFS learning. It addresses the “many-class” problem by exploring the class hierarchy, e.g., the coarse-class label that covers a subset of fine classes, which helps to narrow down the candidates for the fine class and is cheaper to obtain. MahiNet uses a convolutional neural network (CNN) to extract features, and integrates a memory-augmented attention module with a multi-layer perceptron (MLP) to produce the probabilities over coarse and fine classes. While the MLP extends the linear classifier, the attention module extends a KNN classifier, both together targeting the “few-shot” problem. We design different training strategies of MahiNet for supervised learning and meta-learning. Moreover, we propose two novel benchmark datasets “mcfsImageNet” (as a subset of ImageNet) and “mcfsOmniglot” (re-splitted Omniglot) specifically for MCFS problem. In experiments, we show that MahiNet outperforms several state-of-the-art models on MCFS classification tasks in both supervised learning and meta-learning scenarios.

heading: 1 INTRODUCTION
text: The representation power of deep neural networks (DNN) has dramatically improved in recent years, as deeper, wider and more complicated DNN architectures (He et al., 2016; Huang et al., 2017) have emerged to match the increasing computation power of new hardwares. Although this brings hope for complex tasks that could be hardly solved by previous shallow models, more training data is usually required. Hence, the scarcity of annotated data has become a new bottleneck for training more powerful DNNs. For example, in image classification, the number of candidate classes can easily range from hundreds to tens of thousands (i.e., many-class), but the training samples available for each class can be less than 100 (i.e., few-shot). Additionally, in life-long learning, models are always updated once new training data becomes available, and those models are expected to quickly adapt to new classes with a few training samples. This “many-class few-shot” problem is very common in various applications, such as image search, robot navigation and video surveillance.
Although enormous previous works have shown the remarkable power of DNN when “many-class many-shot” training data is available, their performance degrades dramatically when each class only has a few samples available for training. In practical applications, acquiring samples of rare species is usually difficult and often expensive. In these few-shot scenarios, the model’s capacity cannot be fully utilized, and it becomes much harder to generalize the model to unseen data. Recently, several approaches have been proposed to address the few-shot learning problem. Most of them are based on the idea of “meta-learning”, which trains a meta-learner that can generalize to different tasks. For classification, each task targets a different set of classes. Meta-learning can be categorized into two types: methods based on “learning to optimize”, and methods based on metric learning. The former type adaptively modifies the optimizer (or some parts of it) applied to the training process. It includes methods that incorporate an RNN meta-learner (Andrychowicz et al., 2016; Li & Malik, 2017; Ravi & Larochelle, 2017), and model-agnostic meta-learning (MAML) methods aiming to learn a
generally compelling initialization (Finn et al., 2017). The latter type learns a similarity/distance metric (Vinyals et al., 2016) or a support set of samples (Snell et al., 2017) that can be generally used to build KNN classifiers for different tasks.
Instead of using meta-learning, some other approaches, such as Douze et al. (2018), address the few-shot learning problem through data augmentation by
generating artificial samples for each class. However, most existing few-shot learning approaches only focus on “few-class” case (e.g., 5 or 10) per task, and performance usually collapses when the number of classes grows to hundreds or thousands. This is because the samples per class no longer provide enough information to distinguish them from other possible samples within a large number of other classes. And, in real-world problems, tasks are usually complicated involving many classes.
Fortunately, in practice, class hierarchy is usually available or cheaper to obtain. As shown in Figure 1, coarse class labels might reveal the relationships among the targeted fine classes. Moreover, the samples per coarse class are sufficient to train a reliable coarse classifier, whose predictions are able to narrow down the candidates for fine classes. For example, a sheepdog with long hair could be easily mis-classified as mop when training samples of sheepdog are insufficient. However, if we could train a reliable dog classifier, it would be much simpler to predict an image as a sheepdog than a mop given a correct prediction of the coarse class as “dog”. Hence, class hierarchy might provide weakly supervised information to help solve the “many-class few-shot (MCFS)” problem.

heading: 1.1 OUR APPROACH
text: In this paper, we study how to explore the class hierarchy to solve MCFS problem in both traditional supervised learning and in meta-learning. We develop a DNN architecture “memory-augmented hierarchical-classification networks (MahiNet)” that can be applied to both learning scenarios. MahiNet uses a CNN, i.e., ResNet by He et al. (2016), as a backbone network to extract features from raw images. The CNN feeds features into coarse-class and fine-class classifiers, and the results are combined to produce the final prediction according to fine classes as probabilities. In this way, both the coarse-class and the fine-class classifiers mutually help each other within MahiNet: the former helps to narrow down the candidates for the latter, while the latter provides multiple attributes per coarse class that can regularize the former. This design leverages the relationship between fine classes, and mitigates the difficulty caused by “many class” problem. To the best of our knowledge, we are the first to successfully employ the class hierarchy information to improve few-shot learning. Previous works (Ren et al., 2018) cannot achieve improvement after using the same information.
To address the “few-shot” problem, we apply two types of classifiers in MahiNet, i.e., MLP and Knearest neighbor (KNN), which have advantages in many-shot and few-shot situations, respectively. We always use MLP for coarse classification, and KNN for fine classification. With a sufficient amount of data in supervised learning, MLP is combined with KNN for fine classification; and in
meta-learning when less data is available, we also use KNN for coarse classification to assist MLP. In Table 1, we provide a brief comparison of MahiNet with other popular models on the learning scenarios they excel.
To make the KNN learnable and more adaptive to classes with few-shot data, we use an attention module to learn the similarity/distance metric used in KNN, and a re-writable memory of limited size to store and update KNN support set during training. In supervised learning, it is necessary to maintain and update a relatively small memory (7.2% of the dataset) by selecting a few samples, because conducting a KNN search over all available training samples is too computationally expensive in computation. In meta-learning, the attention module can be treated as a meta-learner that learns a universal similarity metric for different tasks.
We extract a large subset of ImageNet (Deng et al., 2009) “mcfsImageNet” as a benchmark dataset specifically designed for MCFS problem. It contains 139,346 images from 77 non-overlapping coarse classes composed of 754 randomly sampled fine classes, each has only ∼ 180 images. Imbalance between the different classes are preserved to reflect the imbalance in practical problems. We further extract “mcfsOmniglot” from Omniglot (Lake et al., 2011) for the same purpose. We will make them publicly available later. In experiments on these two datasets, MahiNet outperforms the widely used ResNet (He et al., 2016) in supervised learning. In meta-learning scenario where each task convers many classes, it shows more promising performance than popular few-shot methods including prototypical networks (Snell et al., 2017) and relation networks (Yang et al., 2018).

heading: 2 MEMORY-AUGMENTED HIERARCHICAL-CLASSIFICATION NETWORK

heading: 2.1 PROBLEM FORMULATION
text: We study supervised learning and meta-learning given a training set of n samples D = {(xi, yi, zi)}ni=1, where each sample xi ∈ X is associated with a fine-class label yi ∈ Y and a coarse-class label zi ∈ Z , and is sampled from a data distribution D, i.e., (xi, yi, zi) ∼ D. Here, Y denotes the set of all the fine classes, and Z denotes the set of all the coarse classes. To define a class hierarchy for Y and Z , we further assume that each coarse class z ∈ Z covers a subset of fine classes Yz , and that distinct coarse classes are associated with disjoint subsets of fine classes, i.e., for any z1, z2 ∈ Z , we have Yz1 ∩ Yz2 = ∅. Our goal is fine-class classification by using the class hierarchy information. In particular, the supervised learning in this case can be formulated as:
min Θ E(x,y,z)∼D − log Pr(y|x; Θ), (1) where Θ is the model parameters. In practice, we solve the corresponding empirical risk minimization (ERM) during training, i.e.,
min Θ n∑ i=1 − log Pr(yi|xi; Θ). (2)
In contrast, meta-learning aims to maximize the expectation of the prediction likelihood of a task drawn from a distribution of tasks. Specifically, we assume that the subset of fine classes T for each task is sampled from a distribution T , and the problem is formulated as
min Θ
ET∼T [ E(x,y,z)∼DT − log Pr(y|x; Θ) ] , (3)
where DT refers to the distribution of samples with label yi ∈ T . The corresponding ERM is
min Θ ∑ T [∑ i∈DT − log Pr(yi|xi; Θ) ] , (4)
where T is a task (defined by a subset of fine classes) sampled from distribution T , and DT is a training set sampled from DT . To leverage the coarse class information of z, we write Pr(y|x; Θ) in Eq. (1) and Eq. (3) as
Pr(y|x; Θ) = ∑ z∈Z Pr(y|z, x; Θf ) Pr(z|x; Θc), (5)
where Θf and Θc are the model parameters for fine classifier and coarse classifier, respectively1. Accordingly, given a specific sample (xi, yi, zi) with its ground truth labels for coarse and fine
1For simplicity, we neglect model parameters θCNN for feature extraction here.
classes, we can write Pr(yi|xi; Θ) in Eq. (2) and Eq. (4) as follows. Pr(yi|xi; Θ) = Pr(yi|zi, xi; Θf ) Pr(zi|xi; Θc). (6) Suppose that a DNN model already produces a logit ay for each fine class y, and a logit bz for each coarse class z, the two probabilities in the right hand side of Eq. (6) are computed by applying softmax function to the logit values in the following way.
Pr(yi|zi, xi; Θf ) = exp(ayi)∑
y∈Yzi exp(ay)
, Pr(zi|xi; Θc) = exp(bzi)∑ z∈Z exp(az) . (7)
Therefore, we integrate both the fine-class label and coarse-class label in an ERM, whose goal is to maximize the likelihood of the ground truth fine-class label. Given a DNN that can produce two logit vectors a and b for fine class and coarse class, we can train it for supervised learning or meta-learning by solving the ERM problems in Eq. (2) or Eq. (4) (with Eq. (6) and Eq. (7) plugged in).

heading: 2.2 NETWORK ARCHITECTURE
text: To address MCFS problem in both supervised learning and meta-learning scenarios, we developed a universal model, MahiNet, as in Figure 2. MahiNet uses a CNN to extract features from raw inputs, and then applies two modules to produce coarse-class prediction and fine-class prediction, respectively. Each module includes one or two classifiers: either an MLP or an attention-based KNN classifier or both. Intuitively, MLP performs better when data is sufficient, while the KNN classifier is more stable in few-shot scenario. Hence, we always apply MLP to coarse prediction and apply KNN to fine prediction. In addition, we use KNN to assist MLP for coarse module in metalearning, and use MLP to assist KNN for fine module in supervised learning. In the attention-based KNN classifier, an attention module is trained to compute the similarity between two samples, and a re-writable memory is maintained with a highly representative support set for KNN prediction.
Our method for learning a KNN classifier combines the ideas from two popular meta-learning methods, i.e., matching networks (Vinyals et al., 2016) that aims to learn a similarity metric, and prototypical networks (Snell et al., 2017) that aims to find a representative center per class for NN search. However, our method relies on an augmented memory rather than a bidirectional RNN for retrieving of NN in matching networks. In contrast to prototypical networks, that only have one prototype per class, we allow multiple prototypes as long as they can fit in the memory budget. Together these two mechanisms prevent the confusion caused by subtle differences between classes in many-class scenario. Notably, MahiNet can also be extended to “life-long learning” given this memory updat-
Algorithm 1 Training of MahiNet for Supervised Learning Input: Training set D = {(xi, yi, zi)}ni=1;
Randomly initialized θKNNf , pre-trained θ CNN , θMLPc and θMLPf ;
Hyper-parameters: memory update parameters r, γ, µ and η; learning rate and its scheduler; 1: while no converge do 2: for mini-batch {(xi, yi, zi)}i∈B in D do 3: Compute fine-class logits a and coarse-class logits b from the outputs of MLP/KNN classifiers; 4: Apply one step of mini-batch SGD for ERM in Eq. (2) (with Eq. (6) and Eq. (7) plugged in); 5: for sample in the mini-batch do 6: Update the memory M according to Eq. (11); 7: Update the utility rate U according to Eq. (13); 8: Expand the feature cache C according to Eq. (12); 9: end for
10: end for 11: for each fine class j in Y do 12: Fine the indexes of the r smallest values in Uj , denoted as {k1, k2, ..., kr}; 13: Clustering of the feature vectors within cache Cj to r clusters with centroids {c1, c2, ..., cr}; 14: Replace the r memory slots by centroids: Mj,ki = ci for i ∈ [r]; 15: end for 16: end while
ing mechanism. We do not adopt the architecture used in Mishra et al. (2018) since it requires the representations of all historical data to be stored.

heading: 2.3 LEARNING A KNN SIMILARITY METRIC WITH AN ATTENTION MODULE
text: In MahiNet, we train an attention module to compute the similarity used in the KNN classifier. The attention module learns a distance metric between the feature vector fi of a given sample xi and any feature vector from the support set stored in the memory. Specifically, we use the dot product attention similar to the one adopted in Vaswani et al. (2017) for supervised learning, and use an Euclidean distance based attention for meta-learning, following the instruction from Snell et al. (2017). Given a sample xi, we compute a feature vector fi ∈ Rd by applying a backbone CNN to xi. In the memory, we maintain a support set of m feature vectors for each class, i.e., M ∈ RC×m×d, where C is the number of classes. The KNN classifier produces the class probabilities of xi by first calculating the attention scores between fi and each feature vector in the memory, as follows.
a(fi,Mj,k) = g(fi)·h(Mj,k) ‖g(fi)‖‖h(Mj,k)‖ or − ‖g(fi)− h(Mj,k)‖2, ∀j ∈ [C], k ∈ [m], (8)
where g and h are learnable transformations for fi and the feature vectors in the memory. We select the K nearest neighbors of fi among the m feature vectors for each class j, and compute the sum of their similarity scores as the attention score of fi to class j, i.e.,
s(fi,Mj) = max N⊆[m],|N |≤K ∑ k∈N a(fi,Mj,k), ∀j ∈ [C]. (9)
We usually find K = 1 is sufficient in practice. The predicted class probability is derived by applying a softmax function to the attention scores of fi over all C classes, i.e.,
Pr(yi = j) , exp (s(fi,Mj))∑C
j′=1 exp (s(fi,Mj′)) , ∀j ∈ [C]. (10)

heading: 2.4 MEMORY MECHANISM UPDATING SUPPORT SET OF KNN
text: Ideally, the memory M ∈ RC×m×d can store all available training samples as the support set of the KNN classifier. In meta learning, in each episode, we sample a task with C classes and m training samples per class, and store them in the memory. Due to the small amount of training data for each task, we can store all data in the memory. In supervised learning, we only focus on one task, which is possible to have a large training set that cannot be entirely stored in the memory. Hence, we set up a budget hyper-parameter m for each class. m is the maximal number of feature vectors to be stored for one class. Moreover, we develop a memory update mechanism to maintain a small memory with diverse and representative feature vectors (t-SNE visualization can be found in Figure 4 in Appendix E). Intuitively, it can choose to forget or merge feature vectors that are no longer representative, and select new important feature vectors into memory.
Algorithm 2 Training of MahiNet for Meta-Learning Input: Training set D = {(xi, yi, zi)}ni=1 and fine class set Y;
Parameters: randomly initialized θCNN , θMLPc , θMLPf , and θ KNN f ;
Hyper-parameters: learning rate, scheduler; for each class, number of queries ns, support set size nS ; 1: while not converge do 2: Sample a task T ∼ T as a subset of fine classes T ⊆ Y . 3: for class j in T do 4: Randomly sample ns data points of class j from D to be the support set Sj of class j. 5: Randomly sample nq data points of class j from D\Sj to be the query set Qj of class j. 6: end for 7: for mini-batch from Q do 8: Compute fine-class logits a and coarse-class logits b from the outputs of MLP/KNN classifiers; 9: Apply one step of mini-batch SGD for ERM in Eq. (4) (with Eq. (6) and Eq. (7) plugged in);
10: end for 11: end while
We will show later in experiments that a small memory can result in sufficient improvement, while the time cost of memory updating is negligible. During training, for the data that can be correctly predicted by the KNN classifier, we merge its feature with corresponding slots in the memory by computing their convex combination, i.e.,
Mj,k = { γ ×Mj,k + (1− γ)× fi, if ŷi = yi Mj,k, otherwise , (11)
where yi is the ground truth label, and γ = 0.95 is a combination weight that works well in most of our empirical studies; for input feature vector that cannot be correctly predicted, we write it to a cache C = {C1, ...,CC} that stores the candidates written into the memory for the next epoch, i.e.,
Cj = { Cj , if ŷi = yi Cj ∪ {fi}, otherwise , (12)
Concurrently, we record the utility rate of the feature vectors in the memory, i.e., how many times each feature vector being selected into theK nearest neighbor during the epoch. The rates are stored in a matrix U ∈ RC×m, and we update it as follows.
Uj,k = { Uj,k × µ, if ŷi = yi Uj,k × η, otherwise , (13)
where µ ∈ (1, 2) and η ∈ (0, 1) are hyper-parameters. At the end of each epoch, we cluster the feature vectors per class in the cache, and obtain r cluster centroids as the candidates for the memory update in the next epoch. Then, for each class, we replace r feature vectors in the memory that have the smallest utility rate with the r cluster centroids.

heading: 3 TRAINING STRATEGIES
text: As shown in the network structure in Figure 2, in supervised learning and meta learning, we use different combinations of MLP and KNN to produce fine-class and coarse-class predictions. The classifiers are combined by summing up their logits for each class, and a softmax function is used to generate the class probabilities. Assume the MLP classifiers for the coarse classes and the fine classes are φ(·; θMLPc ) and φ(·; θMLPf ), the KNN classifiers for the coarse classes and the fine classes are φ(·; θKNNc ) and φ(·; θKNNf ). In supervised learning, the model parameters are θCNN , Θc = θ MLP c and Θf = {θMLPf , θKNNf }; in meta-learning setting, the model parameters are θCNN , Θc = {θMLPc , θKNNc } and Θf = θKNNf .
According to Sec. 2.1, we train MahiNet for supervised learning by solving the ERM problems in Eq. (2) and by solving Eq. (4) for meta-learning. As previously mentioned, the logits (for either fine classes or coarse classes) used in those ERM problems are obtained by summing up the logits produced by the corresponding combination of classifiers.
Training MahiNet for Supervised learning. In supervised learning, the memory update relies heavily on the clustering of the merged feature vectors in the cache. To achieve relatively highquality feature vectors, We first pretrain the CNN+MLP model by using the standard backpropagation to minimize the sum of cross entropy loss on both coarse-classes and fine-classes and then fine-tune the whole model (including the fine-class KNN classifier) with memory updates. The training procedure of the fine-tune stage is explained in Alg. 1.
Training MahiNet for Meta-learning. In meta learning, the memory is constant and stores features extracted from the support set for KNN classifier. The detailed training procedure can be found in Alg. 2. In summary, we sample each training task by randomly sampling a subset of fine classes, and then randomly sample a support set S and a query set Q. We store the CNN feature vectors of S in the memory, and train MahiNet to produce correct predictions for the samples in Q. When sampling the training/test tasks, we allow new fine classes that were not covered in any training task to appear as test tasks, but the ground set of the coarse classes is fixed for both training and test. Hence, every coarse class appearing in any test task has been seen in previous training, but the corresponding fine classes belonging to this coarse class in training and test tasks can vary.

heading: 4 EXPERIMENTS
text: We propose two benchmark datasets specifically for MCFS Problem: mcfsImageNet & mcfsOmniglot, and compare them with several existing datasets in Table 2. Please see more details in Appendix A. Our following experimental study focuses on these two datasets.

heading: 4.1 SUPERVISED LEARNING EXPERIMENTS
text: Experiments on mcfsImageNet. We use ResNet18 for the backbone CNN. The transformations g and h in the attention module are two fully connected layers followed by group normalization (Wu & He, 2018) with a residual connection. See more detailed parameter choices in Appendix B.
Table 3 compares MahiNet with the supervised learning model (i.e., ResNet18) and meta learning model (i.e., prototypical networks). The results show that MahiNet outperforms the specialized models, such as ResNet18 in MCFS scenario. Prototypical Net is a meta-learning model designed to solve few-shot classification problems. We train it in a supervised learning manner (i.e., on a single task with many classes and relatively much more samples per class),
and include it in the comparison to test its performance on MCFS problem. Prototypical network fails to solve MCFS problem in the supervised learning scenario. To separately measure the contribution of the class hierarchy and the attention-based KNN classifier, we conduct an ablation study that removes the KNN classifier from MahiNet. The results show that MahiNet outperforms ResNet18 even when only using the extra coarse-label information during training, and that using a KNN classifier further improves the performance. For each epoch, the average clustering time is 30s and is only 7.6% of the total epoch time (393s). Within an epoch, the memory update time (0.02s) is only 9% of the total iteration time (0.22s).

heading: 4.2 META-LEARNING EXPERIMENTS
text: Experiments on mcfsImageNet. We use the same backbone CNN, g, and h as in supervised learning. In each task, we sample the same number of classes for training and test, and follow the training procedure in Snell et al. (2017). More detailed parameters can be found in Appendix B.
Table 4 shows that MahiNet outperforms the supervised learning baseline (ResNet18) and the metalearning baseline (Prototypical Net). For ResNet18, we follow the fine-tune baseline in Finn et al. (2017). To evaluate the contributions of each component in MahiNet, we show results of several variants in Table 4. “Attention” indicates parametric functions for g and h, otherwise using identity mapping. “Hierarchy” indicates the assist of class hierarchy. For a specific task, “Mem-1” stores the average feature of all training samples for each class; “Mem-2” stores all features of the training samples; “Mem-3” is the union of “Mem-1” and “Mem-2”. Table 4 implies: (1) Class hierarchy information can incur steady performance across all tasks; (2) Combining “Mem-1” and “Mem-2” outperforms using either of them independently; (3) Attention should be learned with class hierarchy in MCFS problem. Because the data is usually insufficient to train a reliable similarity metric to distinguish all fine classes, but distinguishing the fine classes in each coarse class is much easier.
Experiments on mcfsOmniglot. We conduct experiments on the secondary benchmark mcfsImageNet. We use the same training setting as for mcfsImageNet. Following Santoro et al. (2016), mcfsOmniglot is augmented with rotations by multiples of 90 degrees. We do not use ResNet18 on mcfsOmniglot, since mcfsOmniglot is a 28×28 small dataset, which would be easy for ResNet18 to overfit. Therefore, we use four consecutive convolutional layers as the backbone CNN and compare MahiNet with prototypical networks as in Table 5. We do ablation study on MahiNet with/without hierarchy and MahiNet with different kinds of memory. “Mem-3”, i.e., the union of “Mem-1” and “Mem-2”, outperforms “Mem-1”, and “Attention” mechanism can improve the performance. Additionally, MahiNet outperforms other compared methods, which indicates the class hierarchy assists to make more accurate predictions. In summary, experiments on the small-scale and large-scale datasets show that class hierarchy brings a stable improvement.

heading: B EXPERIMENTAL SETUP
text: Setup for the supervised learning. We use ResNet18 (He et al., 2016) for the backbone CNN. The transformation functions g and h in the attention module are two fully connected layers followed by group normalization (Wu & He, 2018) with a residual connection. We set the memory size to m = 12 and the number of clusters to r = 3, which can achieve a better trade-off between memory cost and performance. Batch normalization (Ioffe & Szegedy, 2015) is applied after each convolution and before activation. During pre-training, we apply the cross entropy loss on the probability predictions in Eq. (7). During fine-tuning, we fix the θCNN , θMLPc , and θ MLP f to ensure the fine-tuning process is stable. We use SGD with a mini-batch size of 128 and a cosine learning rate scheduler with an initial learning rate 0.1. µ = 1.05, η = 0.95, a weight decay of 0.0001, and a momentum of 0.9 are used. We train the model for 100 epochs during pre-training and 90 epochs for the fine-tuning.
Setup for the meta learning. We use the same backbone CNN, g, and h as in supervised learning. In each task, we sample the same number of classes for training and test, and follow the training procedure in Snell et al. (2017). We set an initial learning rate to 10−3 and reduce it by a factor 2× every 10k iterations. Our model is trained by Adam (Kingma & Ba, 2015) with a mini-batch size of 128, a weight decay of 0.0001, and a momentum of 0.9. We train the model for 25k iterations in total. For class hierarchy, the objective function is the sum of the softmax with cross entropy losses on the coarse class and on the fine class, respectively.

heading: C RELATED WORKS
text: Few-shot learning has a long history. Before deep learning, generative models (Fei-Fei et al., 2006) are trained to provide a global prior knowledge for solving the one-shot learning problem. However, with the advent of deep learning techniques, some recent approaches (Wong & Yuille, 2015; Lake et al., 2013) use generative models to encode specific prior knowledge, such as strokes and patches. More recently, Douze et al. (2018) and Wang et al. (2018) have applied hallucinations to training images and to generate more training samples, which converts a few-shot problem to a many-shot problem.
Meta-learning has been used in attempts to solve the few-shot learning problems. Meta learning was first proposed in the last century (Naik & Mammone, 1992; Schmidhuber, 1987), and ) but has recently seen some significant improvements. For example, Lake et al. (2015) proposed a dataset of characters for meta-learningn while Koch et al. (2015) extended this idea into a Siamese network. A more challenging dataset (Ravi & Larochelle, 2017; Vinyals et al., 2016) was introduced later. Researchers have also studied RNN and attention based method to overcome the few-shot problem. More recently, Snell et al. (2017) is proposed based on a metric learning equipped KNN. In contrast, Finn et al. (2017) based their approach on the second order optimization. Mishra et al. (2018) uses temporal convolution to address the few-shot image recognition. However, unlike above methods, our model leverages the class hierarchy information, and can be easily applied to both the supervised learning and meta-learning scenarios.

heading: D COMPARISON TO VARIANTS OF RELATION NET
text: Relation network with class hierarchy. We train relation network with class hierarchy in the similar manner as in MahiNet. The results are shown in Table 6. It demonstrates that the class hierarchy also improves the accuracy of relation network by more than 1%, which verifies the advantage of using class hierarchy in other models besides MahiNet.
Relation network in high way setting. For relation network in high way settings, we found that the network is easy to be stuck into a suboptimal solution. After first few iterations, the training loss stays in a high level and the training accuracy stays in a low level. We demonstrate the training loss and training accuracy for the first 100 iterations under different learning rate as Figure 3. The training loss and accuracy keep the same value after 100 iterations.

heading: E ANALYSIS OF AUGMENTED MEMORY
text: Visualization. In order to show how representative and diverse the feature vectors selected into memory slots are, we visualize feature vectors in the memory and the rest image feature vectors in t-SNE in Figure 4. In particular, we randomly sample 50 fine classes marked by different colors. Within every class, we show both the selected feature vectors in memory and feature vectors of other images from the same class. It shows that the small number of highly selected feature vectors in memory are diverse and sufficiently representative of the whole class.
Memory Cost. In experiments of supervised learning, the memory size required by MahiNet is only 754 × 12/125321 = 7.2% (12 samples per class for all the 754 fine classes, while the training set includes 125, 321 images in total) of the memory needed to store the whole training set. We also tried to increase the memory size to about 10%, but the resultant improvement on performance is negligible. In each task of meta learning, since every class only has few-shot samples, the memory
required to store all the data is very small. For example, in the 20-way 1-shot setting, the memory only needs to store 20 feature vectors.
F mcfsIMAGENET CLASS SPLITS
The hierarchy of coarse and fine classes is shown below. Every key (marked in bold) in the dictionary is a coarse class, the value is a list of the fine classes of this coarse classes.
n02470325: [n02482650, n02481500, n02483362, n02481103, n02482060, n02481235, n02482474, n02481366, n02482286, n02483708] n01769347: [n01782516, n01775370, n01772664, n01773797, n01773549, n01774384, n01777304, n01773157, n01778217, n01770081] n12855042: [n12856479, n12855710, n12855886, n12855494, n12855365, n12856287, n12856680, n12856091] n13109733: [n11705171, n12717224, n12734215, n12491017, n11710136, n11707827, n12634734, n12711984, n12709103, n12753245] n07806221: [n07807834, n07808904, n07806774, n07808268, n07808587, n07806879, n07807472, n07808022, n07807594, n07808479] n01428580: [n02583890, n01447331, n01449374, n02520147, n01439808, n02538562, n01442972, n02543565, n02527057, n01447658] n01789386: [n01793249, n01790812, n01790304, n01795735, n01815036, n01806297, n01793085, n01796729, n01794344, n01797020] n12582231: [n12590499, n12597466, n12587487, n12598027, n12595699, n12596148, n12583855, n12587803, n12597134, n12596345] n01816887: [n01819734, n01819313, n01818299, n01817263, n01819465, n01817346, n01822300, n01821869, n01818832, n01818515] n12724942: [n12728322, n12731029, n12727518, n12729521, n12726159, n12729729, n12728864, n12730544, n12725940, n12728508] n07802417: [n07804543, n07802863, n07889193, n07804900, n07804771, n07804657, n07802963, n07803310] n07882497: [n07628181, n07861247, n07861158, n07594066, n07628068, n07678586, n07594155, n07679034, n07839478, n07678953] n01940736: [n01945143, n01953594, n01944118, n01955084, n01947396, n01959029, n01965529, n01960177, n01963317, n01958346] n07911677: [n07914586, n07915800, n07916437, n07931452, n07918309, n07914995, n07917392, n07918193, n07917133, n07916183] n12864545: [n12865037, n12866002, n12866162, n12866459, n12865824, n12865708, n12865562, n12866635] n07886572: [n07887192, n07890226, n07889990, n07887304, n07887967, n07888816, n07890540, n07890068, n07887461, n07888229] n01524359: [n01561732, n01556182, n01537895, n01561452, n01593553, n01583828, n01539925, n01543632, n01535690, n01538630] n12268246: [n12278865, n12271933, n12278650, n12277150, n12276872, n12270278, n12276477, n12273344, n12271187, n12270741] n01674216: [n01682435, n01682714, n01680983, n01689811, n01687978, n01690466, n01678043, n01680655, n01693175, n01689081] n02084071: [n02088466, n02101006, n02113624, n02091467, n02111277, n02087394, n02102177, n02105412, n02094258, n02106662] n02274024: [n02305085, n02297819, n02304432, n02277742, n02302244, n02303284, n02281015, n02279972, n02276078, n02282257] n02441326: [n02443484, n02445171, n02442572, n02446206, n02445004, n02443346, n02450295, n02448060, n02447762, n02449183] n02484322: [n02488415, n02493793, n02494079, n02493509, n02492660, n02492948, n02487547, n02485536, n02485688, n02490811] n07901587: [n07906111, n07902799, n07905296, n07904865, n07907037, n07904293, n07905386, n07902443, n07903101, n07902336] n02801938: [n02925009, n04024862, n03050864, n03390673, n03883385, n03129848, n02893608, n04204238, n03378342] n12387633: [n12389501, n12388858, n12390099, n12389727, n12390314, n12388989, n12389130, n12388143] n04576211: [n04474035, n03235979, n03435991, n02871314, n03193260, n02740300, n03256166, n03173929, n04026813, n04285965] n01661592: [n01666585, n01664990, n01668665, n01671125, n01669372, n01670802, n01666228, n01669654, n01668892, n01665541] n01627424: [n01636352, n01646555, n01633406, n01632458, n01654637, n01646802, n01641739, n01641930, n01629819, n01631354] n02131653: [n02133704, n02133400, n02132788, n02132466, n02134084, n01322983, n02132580, n02134418] n06271778: [n06272612, n06276501, n06278475, n06273555, n06274760, n06273294, n06273986, n06273414, n06275471, n06277135] n13100677: [n12162425, n12828220, n11731659, n11788727, n12513172, n12828379, n11789589, n12383737, n11769803, n12485981] n01726692: [n01744401, n01738065, n01749939, n01732614, n01737728, n01753180, n01743936, n01733466, n01750167, n01757343] n02394477: [n02419634, n02414290, n02396014, n02412080, n02405440, n02396427, n02415829, n02395931, n02418465, n02427470] n01604330: [n01608814, n01611800, n01624115, n01606809, n01623615, n01606177, n01610100, n01609391, n01609956, n01604968] n02373336: [n02377291, n02391373, n02389026, n02379329, n02378969, n02379081, n02386853, n02379908, n02382204, n02389943] n02898711: [n04311004, n02775897, n03865557, n04479939, n03415486, n03981760, n04108822, n03233744, n03122073, n02986160] n01976146: [n01986214, n01983674, n01988203, n01981276, n01984695, n01980166, n01978455, n01978287, n01982068, n01979874] n02062430: [n02065726, n02071028, n02072798, n02070624, n02065263, n02069412, n02068541, n02071636, n02063662, n02069974] n02075927: [n02080713, n02081798, n02078738, n02077384, n02077787, n02080146, n02080415, n02079851, n02076402, n02078574] n13112664: [n12632335, n11948864,
n12249542, n12861541, n12905135, n12675876, n12921868, n11865874, n12806015, n12482668] n07829412: [n07837002, n07837755, n07837545, n07840520, n07838073, n07831267, n07832416, n07840219, n07835921, n07826091] n07892813: [n07897438, n07898247, n07897975, n07897600, n07899533, n07899769, n07894799, n07894703, n07894451, n07896765] n09366017: [n09398076, n09454744, n09233446, n09435739, n09452291, n09410224, n09245515, n09415671, n09295946, n09344324] n01480516: [n01495493, n01484562, n01492569, n01484285, n01500476, n01485479, n01481498, n01489709, n01496331, n01491006] n02974697: [n02962843, n03031012, n03498441, n03468821, n04570815, n03210372, n04035836, n03986949, n03438863, n04064747] n07927197: [n07928696, n07928998, n07929172, n07927836, n07928163, n07928578, n07928488, n07927716, n07928790, n07928887] n11545524: [n13230843, n13181244, n13193143, n13214485, n13231078, n13229543, n12961879, n13190747, n13183056, n12953712] n03206908: [n03062336, n04130257, n04050933, n04242704, n04263257, n03775546, n02927764, n04499062, n03920288, n02997910] n07596684: [n07609632, n07601686, n07600177, n07643891, n07604956, n07643200, n07608098, n07609215, n07597263, n07608533] n07560652: [n07561590, n07565083, n07562017, n07938007, n07562379, n07565161, n07560903, n07563366, n07564796, n07563207] n07583197: [n07588817, n07588574, n07584423, n07585557, n07588193, n07588111, n07585107, n07586894, n07586318, n07587618] n13108841: [n11646344, n11626585, n11656123, n11626826, n11615607, n11644462, n11615387, n11655974, n11622368, n11662371] n02164464: [n02177196, n02174001, n02173373, n02178411, n02167944, n02175916, n02183096, n02169974, n02180875, n02181724] n13085113: [n11874081, n11898775, n12003167, n12394118, n11949015, n11965218, n11954161, n11920133, n11919761, n12033139] n02139199: [n02146879, n02146371, n02146700, n02140049, n02147173, n02147947, n02147328, n02147591] n07907943: [n07910048, n07910152, n07908812, n07908411, n07910379, n07910970, n07911249, n07908567, n07908647, n07910538] n02188699: [n02197185, n02195526, n02203152, n02201626, n02198859, n02191979, n02196119, n02202124, n02204907, n02192513] n07843775: [n07848936, n07847917, n07848093, n07847827, n07848196, n07849733, n07616046, n07847453, n07849912, n07847585] n02121808: [n02122430, n02123478, n02123045, n02122510, n02123159, n02122298, n02123242, n02122878, n02123394, n02123917] n11868814: [n11876803, n11876204, n11870747, n11875523, n11879722, n11882074, n11882426, n11877646, n11877193, n11876432] n02552171: [n02656301, n02580679, n02555863, n02565324, n01451426, n02663211, n02578928, n02654112, n02572484, n02607470] n12997654: [n13013965, n13075684, n13053608, n13014265, n13013764, n13018088, n13020964, n13011595, n13032618, n13003522] n03764276: [n04006067, n04308273, n03466600, n04457474, n04308397, n03549199, n03811295, n04487894, n04363082, n03466493] n09366317: [n09362945, n09214916, n09230202, n09472597, n09411295, n09421951, n09193705, n09396465, n09269341, n09218641] n07679356: [n07682808, n07685218, n07685399, n07682477, n07683039, n07680761, n07690431, n07684938, n07681691, n07681450] n07929519: [n07919572, n07920872, n07920349, n07919441, n07920222, n07731284, n07919894, n07920663, n07920540, n07921239] n02323449: [n02324514, n02324431, n02327842, n02324587, n02324850, n02327656, n02328150, n02325722, n02326862] n13100156: [n12766869, n11724109, n11734698, n12455950, n11723227, n11773987, n11767877, n12935609, n12767648, n12941220] n12334293: [n12336727, n12338454, n12336224, n12337617, n12316572, n12340581, n12338655, n12340755, n12338146, n12336973] n01909422: [n01914830, n01917882, n01917611, n01917289, n01915700, n01913166, n01916388, n01909906, n01916481] n01838038: [n01839949, n01842235, n01841679, n01840775, n01841441, n01839086, n01840412, n01843065, n01839330, n01840120] n02206270: [n02213663, n02209111, n02213788, n02211627, n02218371, n02221715, n02210427, n02216211, n02221414, n02214499] n07712382: [n07696625, n07696728, n07696839, n07697313, n07698672, n07865105, n07696977, n07697699, n07698401, n07697408] n07811416: [n07935043, n07820960, n07933154, n07816398, n07821758, n07812046, n07818825, n07826340, n07816164, n07820297] n12685431: [n12686077, n12686274, n12686496, n12686676, n12687957, n12686877, n12687044, n12687462, n12687698] n12101870: [n12139921, n12140903, n12140511, n12134486, n12123741, n12126084, n12121610, n12117326, n12130549, n12133462]
G mcfsOMNIGLOT CLASS SPLITS
The hierarchy of coarse and fine classes is shown below. Every key (marked in bold) in the dictionary is a coarse class, the value is a list of the fine classes of this coarse classes.
Training Hierarchy Classes:
Mkhedruli (Georgian): [character10, character38, character25, character29, character21, character04, character13, character37, character34, character31, character17, character16, character03, character30, character02, character06, character24, character11, character39, character19, character07, character14, character32, character36] Alphabet of the Magi: [character18, character05, character17, character03, character08, character01, character15, character07, character09, character10, character19, character11, character20] Angelic: [character12, character16, character20, character06, character13, character19, character04, character03, character07, character09, character05] Early Aramaic: [character09, character02, character07, character11, character04, character19, character16, character05, character21, character18, character06, character12, character03, character13, character10] Old Church Slavonic (Cyrillic): [character21, character36, character38, character07, character15, character11, character29, character14, character43, character17, character34, character25, character41, character35, character23, character22, character20, character44, character19, character09, character10, character33, character45, character26, character32] Gujarati: [character05, character44, character06, character35, character40, character45, character07, character29, character12, character09, character24, character38, character46, character15, character32, character21, character18, character25, character04, character02, character23, character03, character31, character14, character34, character20, character37, character43, character30, character36, character26, character08, character28] Japanese (katakana): [character31, character44, character10, character21, character33, character14, character36, character06, character11, character28, character27, character04, character26, character43, character03, character08, character12, character24, character16, character15, character35, character05, character01, character41, character25, character29, character38, character39, character22, character02] Syriac (Serto): [character14, character23, character03, character04, character18, character06, character02, character22, character15, character09] Tengwar: [character21, character19, character15, character13, character03, character11, character16, character10, character18, character05, character14] Korean: [character03, character14, character35, character32, character04, character19, character15, character33, character01, character28, character09, character40, character16, character07, character39, character34, character37, character11, character36, character24, character10, character05] Malayalam: [character37, character34, character42, character01, character10, character40, character28, character06, character17, character35, character14, character19, character38, character13, character08, character46, character47, character21, character23, character18, character36, character24, character43, character33, character03] Japanese (hiragana): [character44, character05, character13, character19, character31, character40, character24, character50, character07, character11, character06, character01, character32, character10, character46, character04, character47, character45, character27, character42, character36, character09, character34, character49, character12, character28, character37, character26, character23, character52, character38, character17, character25, character30] Atemayar Qelisayer: [character06, character08, character09, character16, character05, character14, character11, character01, character10, character13, character02, character20, character26] Tibetan: [character31, character30, character21, character18, character20, character34, character29, character10, character23, character16, character42, character17, character15, character03, character33, character01, character25, character13, character09, character05, character40, character26, character38, character06, character28] Sylheti: [character03, character28, character17, character13, character23, character27, character08, character22, character05, character14, character12, character16, character04, character18, character20, character24] Malay (Jawi - Arabic): [character27, character11, character12, character13, character23, character36, character26, character33, character31, character39, character32, character40, character21, character09, character14, character17, character28, character38, character06] Latin: [character07, character08, character02, character24, character22, character06, character14, character09, character21, character10, character25, character04, character13, character19, character03, character26, character05, character18, character16, character15] Syriac (Estrangelo): [character20, character19, character04, character10, character09, character07, character21, character08, character22, character16, character15, character14, character03, character05, character01, character11, character17] ULOG: [character04, character02, character15, character05, character08, character12, character03, character16, character24, character13, character26, character23, character25, character19] Blackfoot (Canadian Aboriginal Syllabics): [character13, character03, character12, character11, character05, character07, character01, character10] Futurama: [character22, character20, character08, character11, character05, character06, character16, character02, character13, character03, character14, character15, character17, character12] Gurmukhi: [character34, character21, character08, character38, character04, character37, character09, character03, character26, character35, character20, character19, character41, character13, character16, character28, character10, character39, character32, character29, character33, character24, character12, character45, character22, character05] Ojibwe (Canadian Aboriginal Syllabics): [character08, character04, character01, character12, character13, character11, character03, character10, character05, character02] Greek: [character13, character14, character06, character22, character01, character19, character08, character02, character09, character07, character04, character18, character12, character03] Armenian: [character11, character13, character35, character36, character21, character26, character01, character30, character27,
character32, character40, character05, character25, character16, character22, character19, character39, character15, character14, character33, character09] Kannada: [character01, character32, character20, character28, character40, character11, character10, character17, character25, character33, character02, character19, character27, character39, character37, character38, character36, character41, character29, character23, character16, character14, character04, character18, character21, character12, character24, character09] Manipuri: [character04, character20, character24, character32, character02, character09, character33, character30, character14, character29, character16, character05, character34, character03, character13, character12, character31, character10, character18, character15, character22, character23] Oriya: [character09, character31, character45, character27, character05, character07, character11, character23, character15, character20, character29, character44, character32, character06, character19, character46, character36, character03, character25, character01, character26, character04, character38, character22, character35] Asomtavruli (Georgian): [character10, character12, character17, character11, character03, character24, character25, character35, character32, character22, character21, character29, character14, character23, character34, character20, character33, character26, character16, character28, character04, character05, character08, character06] Keble: [character17, character03, character15, character18, character13, character19, character10, character07, character21, character26, character08, character12, character09, character11, character14, character16, character05, character22] Arcadian: [character09, character10, character03, character16, character26, character04, character08, character18, character23, character02, character14, character22, character05, character01] Cyrillic: [character33, character26, character01, character11, character08, character24, character22, character10, character04, character27, character07, character09, character06, character18, character20, character19, character21, character12, character25, character29, character32] Hebrew: [character16, character02, character10, character19, character13, character01, character11, character14, character22, character12, character07, character15, character21, character05] Avesta: [character23, character21, character17, character18, character03, character25, character19, character24, character22, character06, character09, character16, character05, character26, character15] Glagolitic: [character14, character01, character23, character42, character39, character06, character33, character03, character19, character10, character11, character37, character21, character04, character31, character41, character30, character07, character17, character35, character26, character28, character25, character29] Ge ez: [character18, character11, character07, character22, character21, character04, character06, character13, character15, character25, character20, character12, character08, character05] Bengali: [character44, character06, character17, character45, character12, character09, character04, character32, character22, character46, character37, character14, character07, character26, character20, character41, character11, character33, character02, character40, character39, character10, character35, character28, character01, character19, character05, character27, character13] Atlantean: [character17, character26, character11, character15, character08, character19, character09, character12, character04, character18, character21, character03, character02, character06, character23] Sanskrit: [character20, character40, character24, character01, character37, character39, character28, character42, character13, character09, character02, character25, character23, character33, character19, character07, character35, character36, character17, character11, character38, character15, character16, character31, character03, character10, character26, character21, character29] Braille: [character12, character14, character18, character15, character23, character05, character01, character10, character24, character02, character22, character11, character20, character21] Burmese (Myanmar): [character15, character23, character33, character26, character10, character08, character30, character25, character21, character07, character31, character19, character34, character05, character29, character27, character06, character20, character03, character09, character18, character24, character22, character12] Tagalog: [character05, character14, character04, character03, character12, character15, character10, character09, character01, character17, character11] Mongolian: [character25, character16, character11, character30, character14, character20, character18, character07, character17, character13, character19, character12, character29, character27, character08, character01, character28, character22, character26] Inuktitut (Canadian Aboriginal Syllabics): [character13, character05, character06, character15, character11, character16, character10, character14] N Ko: [character23, character12, character16, character11, character32, character20, character19, character02, character10, character31, character13, character28, character18, character14, character24, character26, character21, character30, character15, character01] Balinese: [character19, character06, character17, character13, character11, character01, character24, character02, character04, character20, character23, character12, character14, character18, character09] Aurek-Besh: [character23, character20, character05, character17, character14, character06, character15, character09, character22, character13, character07, character03, character19, character16, character24, character08, character10, character02, character12, character21, character18, character11, character25] Anglo-Saxon Futhorc: [character11, character14, character09, character01, character10, character16, character05, character27, character18, character02, character08, character21, character13, character29, character17, character20] Tifinagh: [character42, character38, character35, character36, character31, character08, character52, character26, character41, character50, character09, character45, character24, character11, character39,
character19, character34, character05, character21, character16, character23, character37, character04, character28, character03, character32, character10, character43, character40, character48, character17, character44, character49, character06, character20] Grantha: [character39, character13, character12, character33, character35, character15, character16, character27, character20, character19, character30, character26, character42, character22, character23, character03, character02, character07, character38, character40, character25, character18, character08, character37, character32, character24]
Validation Hierarchy Classes:
Alphabet of the Magi: [character16] Angelic: [character01, character10, character11] Early Aramaic: [character17, character14] Oriya: [character14, character40, character10, character21, character33, character39, character12, character41, character16] Gujarati: [character13, character47, character33, character10, character22] Japanese (katakana): [character17, character46, character45, character30, character40, character07, character18] Syriac (Serto): [character13, character12, character17, character21, character08, character20, character19] Tengwar: [character09, character06, character22] Korean: [character25, character29, character26, character38, character06] Malayalam: [character12, character09, character30, character11, character31, character44, character39, character32, character04] Arcadian: [character13, character19, character07, character06] Atemayar Qelisayer: [character18, character24, character25, character22] Tibetan: [character32, character11, character12, character41, character35] Sylheti: [character25, character01, character26, character06] Old Church Slavonic (Cyrillic): [character16, character28, character01, character12, character40, character42, character39] ULOG: [character07, character22, character06, character17, character14, character20, character18, character10] Syriac (Estrangelo): [character12, character06] Tagalog: [character08] Blackfoot (Canadian Aboriginal Syllabics): [character09, character08] Futurama: [character25, character10, character04, character26, character24, character19, character01] Gurmukhi: [character18, character17, character43, character25, character07, character42, character06, character02] Ojibwe (Canadian Aboriginal Syllabics): [character06, character09] Mkhedruli (Georgian): [character40, character33, character22, character26, character05, character27, character15, character12, character28] Armenian: [character37, character18, character08, character17] Kannada: [character15, character13, character08, character26, character05, character35, character07, character34, character30] Manipuri: [character06, character36, character11, character27, character38, character26] Anglo-Saxon Futhorc: [character25, character24, character06] Asomtavruli (Georgian): [character02, character09, character15, character07, character39, character18, character30] Keble: [character24, character20, character04, character23, character01] Japanese (hiragana): [character22, character02, character33, character41, character14, character16, character51] Braille: [character13, character26, character04, character25, character17] Hebrew: [character20, character03, character18, character06, character09] Avesta: [character10, character01, character08, character11, character13, character20, character07] Glagolitic: [character44, character38, character08, character18, character36, character12, character13, character09] Latin: [character23] Ge ez: [character01, character23, character09, character19] Bengali: [character29, character23, character21, character38] Atlantean: [character13, character20] Greek: [character05, character21, character24, character10] Cyrillic: [character02, character16] Burmese (Myanmar): [character14, character13, character32] Grantha: [character17, character14, character31, character21, character01, character11] Mongolian: [character23, character24, character15, character03] Inuktitut (Canadian Aboriginal Syllabics): [character02, character08, character12, character09] N Ko: [character33, character03, character09, character06, character25] Balinese: [character15, character07, character03, character05] Aurek-Besh: [character26] Tifinagh: [character30, character15, character54, character33, character02, character18, character07, character13] Sanskrit: [character14, character18, character22, character27, character08] Malay (Jawi - Arabic): [character08, character05, character02, character16, character19, character18, character29]
Testing Hierarchy Classes:
Mkhedruli (Georgian): [character01, character18, character20, character41, character08, character09, character23, character35] Alphabet of the Magi: [character14, character04, character13, character06, character02, character12] Angelic: [character02, character08, character14, character17, character15, character18] Early Aramaic: [character01, character08, character15, character22, character20] Oriya: [character30, character18, character43, character28, character17, character13, character24, character42, character02, character37, character08, character34] Gujarati: [character17, character39, character19, character11, character01, character16, character41, character27, character48, character42] Japanese (katakana): [character20, character32, character42, character09, character13, character37, character19, character34, character23, character47] Syriac (Serto): [character05, character07, character10, character11, character01, character16] Ge ez: [character10, character16, character17, character14, character03, character24, character26, character02] Korean: [character31, character12, character22, character21, character18, character02, character23, character27, char-
acter30, character17, character20, character08, character13] Malayalam: [character07, character22, character15, character05, character25, character16, character02, character45, character41, character29, character26, character27, character20] Atlantean: [character16, character14, character25, character05, character01, character07, character10, character22, character24] Atemayar Qelisayer: [character19, character21, character12, character04, character07, character17, character15, character23, character03] Arcadian: [character24, character20, character25, character12, character21, character17, character15, character11] Tibetan: [character04, character36, character19, character37, character07, character39, character22, character02, character24, character08, character27, character14] Old Church Slavonic (Cyrillic): [character02, character13, character08, character06, character30, character27, character03, character05, character24, character31, character37, character18, character04] Malay (Jawi - Arabic): [character04, character03, character15, character37, character20, character34, character24, character35, character25, character30, character07, character10, character01, character22] Cyrillic: [character13, character15, character05, character14, character23, character31, character28, character17, character03, character30] Syriac (Estrangelo): [character23, character18, character13, character02] Tagalog: [character07, character06, character13, character02, character16] Bengali: [character25, character18, character36, character16, character30, character15, character03, character42, character43, character31, character34, character08, character24] ULOG: [character09, character01, character11, character21] Gurmukhi: [character30, character14, character11, character44, character31, character27, character23, character36, character15, character40, character01] Ojibwe (Canadian Aboriginal Syllabics): [character07, character14] Sanskrit: [character34, character06, character30, character41, character12, character04, character05, character32] Armenian: [character24, character34, character41, character07, character31, character12, character28, character29, character20, character38, character23, character02, character03, character10, character06, character04] Kannada: [character06, character22, character03, character31] Manipuri: [character35, character37, character17, character39, character25, character28, character19, character01, character07, character40, character21, character08] Anglo-Saxon Futhorc: [character22, character19, character28, character03, character23, character07, character15, character26, character12, character04] Asomtavruli (Georgian): [character36, character27, character37, character31, character01, character40, character19, character38, character13] Keble: [character25, character06, character02] Japanese (hiragana): [character03, character35, character15, character08, character43, character20, character18, character21, character29, character39, character48] Latin: [character12, character01, character11, character20, character17] Hebrew: [character08, character17, character04] Avesta: [character14, character12, character02, character04] Glagolitic: [character40, character24, character45, character20, character32, character43, character05, character02, character15, character27, character22, character34, character16] Tengwar: [character25, character01, character08, character20, character24, character23, character04, character17, character02, character07, character12] Greek: [character20, character15, character17, character16, character23, character11] Sylheti: [character02, character10, character07, character19, character15, character21, character11, character09] Braille: [character09, character16, character07, character08, character19, character06, character03] Burmese (Myanmar): [character01, character16, character11, character28, character17, character04, character02] Futurama: [character21, character23, character09, character18, character07] Mongolian: [character02, character09, character06, character05, character21, character04, character10] Inuktitut (Canadian Aboriginal Syllabics): [character01, character03, character04, character07] N Ko: [character27, character07, character04, character22, character17, character08, character05, character29] Blackfoot (Canadian Aboriginal Syllabics): [character06, character02, character04, character14] Balinese: [character08, character10, character22, character16, character21] Aurek-Besh: [character04, character01] Tifinagh: [character12, character53, character51, character46, character55, character25, character22, character14, character47, character01, character27, character29] Grantha: [character10, character34, character29, character04, character05, character06, character43, character36, character28, character09, character41]

Review summaries:
This paper presents methods for (1) adding inductive bias to a classifier through coarse-to-fine prediction along a class hierarchy and (2) learning a memory-based KNN classifier through an intuitive procedure that keeps track of mislabeled instances during learning. Further, the paper motivates focused work on the many class / few shot classification scenario and creates new benchmark datasets from subsets of imagenet and omniglot that match this scenario. Experimental results show gains over popular competing methods on these benchmarks.

This study explores the class hierarchy to solve many-class few-short learning problem in both traditional supervised learning and meta-learning. The model integrates both the coarse-class and fine-class label as the supervision information to train the DNN, which aims to leverage coarse-class label to assist fine-class prediction. The core part in the DNN is memory-augmented attention model that includes at KNN classifier and Memory Update mechanism. The re-writable memory slots in KNN classifier aim to maintain multiple prototypes used to describe the data sub-distribution within a class, which is insured by designing the memory utility rate, cache and clustering component in Memory Update mechanism. This study presents a relatively complex system that combines the idea of matching networks and prototypical networks.