Concept Learners for Few-Shot Learning
Authors: Kaidi Cao
Maria Brbic
Jure Leskovec
Venue: ICLR 2021 Poster (2021)
Keywords: few-shot learning
meta learning

Abstract: Developing algorithms that are able to generalize to a novel task given only a few labeled examples represents a fundamental challenge in closing the gap between machine- and human-level performance. The core of human cognition lies in the structured, reusable concepts that help us to rapidly adapt to new tasks and provide reasoning behind our decisions. However, existing meta-learning methods learn complex representations across prior labeled tasks without imposing any structure on the learned representations. Here we propose COMET, a meta-learning method that improves generalization ability by learning to learn along human-interpretable concept dimensions. Instead of learning a joint unstructured metric space, COMET learns mappings of high-level concepts into semi-structured metric spaces, and effectively combines the outputs of independent concept learners. We evaluate our model on few-shot tasks from diverse domains, including fine-grained image classification, document categorization  and cell type annotation on a novel dataset from a biological domain developed in our work. COMET significantly outperforms strong meta-learning baselines, achieving 6-15% relative improvement on the most challenging 1-shot learning tasks, while unlike existing methods providing interpretations behind the model's predictions.

Published as a conference paper at ICLR 2021
CONCEPT LEARNERS FOR FEW-SHOT LEARNING
Kaidi Cao", Maria Brbić", Jure Leskovec Department of Computer Science
Stanford University (kaidicao, mbrbic, jure}êcs. stanford.eds
ABSTRACT
Developing algorithms that are able to generalize to a novel task given only a few labeled examples represents a fundamental challenge in closing the gap between machine- and human-level performance. The core of human cognition lies the structured, reusable concepts that help to rapidly adapt to new tasks and provide reasoning behind our decisions. However, existing meta-learning methods learn complex representations across prior labeled tasks without imposing any structure on the learned representations. Here we propose COMET, a meta-learming method that improves generalization ability by learning to learn along human- interpretable concept dimensions. Instead of learning a joint unstructured metric space, COMET learns mappings of high-level concepts into semi-structured metric spaces, and effectively combines the outputs of independent concept learners. We evaluate our model on few-shot tasks from diverse domains, including fine- grained image classification, document categorization and cell type annotation on a novel dataset from a biological domain developed in our work. COMET significantly outperforms strong meta-learinng baselines, achieving 6-15% relative improvement on the most challenging 1-shot learning tasks, while unlike existing methods providing interpretations behind the model's predictions.
INTRODUCTION
Deep learning has reached human-level performance on domains with the abundance of large-scale labeled training data. However, learning on tasks with small number of annotated examples is still an open challenge. Due the lack of training data, models often overfit or are too simplistic to provide good generalization. On the contrary, humans can learn new tasks very quickly by drawing upon prior knowledge and experience. This ability to rapidly learn and adapt to new environments is a hallmark of human intelligence.
Few-shot learning Miileer et al., 2000, Fei-Fei et al. 2006 Koch et al. 2015) aims at addressing this
fundamental challenge by designing algorithms that are able to generalize to new tasks given only few labeled training examples. Meta-learning (Schmidhuber 1987 Bengio et al. 1992) has recently made major advances in the field by explicitly optimizing the model's s ability generalize, or learning how to learn, from many related tasks (Snell et al., 2017] Vinyals et al. 2016 Ravi & Larochelle 2017 Finn et al. 2017). Motivated by the way humans effectively use prior knowledge, meta-learning algorithms acquire prior knowledge over previous tasks so that new tasks can be efficiently learned from small amount of data. However, recent works (Chen et al. 2019b] Raghu et al., (0220) show that simple baseline methods perform comparably to existing meta-lcarning methods, opening the question about which components are crucial for rapid adaptation and generalization.
Here, we argue that there is an important missing piece in this puzzle. Human knowledge is structured in the form of reusable concepts. For instance, when we learn recognize new bird species we are already equipped with the critical concepts, such as wing, beak, and feather. We then focus on these specific concepts and combine them identify a new species. While learning to recognize new species is challenging in the complex bird space, it becomes remarkably simpler once the reasoning is structured into familiar concepts. Moreover, such structured way of cognition gives us the ability to provide reasoning behind our decisions, such as "ravens have thicker beaks than crows, with more
"The two first authors made equal contributions.

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fB
(I)
Concept importance scores
d
Concept leamers
Concept embeddings
AGG
(i)
Concept
Aggregate
prototypes
Figure 1: Along each concept dimension, COMET learns concept embeddings using independent concept learners and compares them to concept prototypes. COMET then effectively aggregates information across concept dimensions, assigning concept importance scores each dimension.
of curve to the end". We argue that this lack of structure is limiting the generalization ability of the current meta-learners The importance of compositionality for few-shot learning was emphasized in (Lake et al. 2011 2015) where hand-designed features of strokes were combined using Bayesian program learning.
Motivated by the structured form of human cognition, we propose COMET, meta-learnin method that discovers generalizable representations along cuuman-iteerpretable concept dimensions. COMET learns unique metric space for each concept dimension using concept-specific embedding functions, named concept learners, that are parameterized by deep neural networks. Along each high-level dimension, COMET defines concept prototypes that reflect class-level differences in the metric space of the underlying concept. To obtain final predictions, COMET effectively aggregates information from diverse concept learners and concept prototypes. Three key aspects lead strong generaliza- tion ability of our approach: (i) cemi-structured representation learning, (ii) concept-specific metric spaces described with concept prototypes, and (ii) ensembling of many models. The latter assures that the combination of diverse and accurate concept learners improves the generalization ability of the base learner (Hansen Salamon 1990 Dvornik et al., 2019). Remarkably, the high-level universe of concepts that are used to guide algorithm can be discovered in fully unsupervised way, or we can use external knowledge bases to define concepts. In particular, we can get a large universe of noisy, incomplete and redundant concepts and COMET learns which subsets of those are important by assigning local and global concept importance scores. Unlike existing methods (Snell et al. 2017: Vinyals et al.. 2016;; Sung al. 2018 Gidaris & Komodakis 2018), COMET's predictions are interpretable-at an advantage especially important in the few-shot learning setting, where predictions are based only handful of labeled examples making hard to trust the model. interpretable meta-learning approach.
As such, COMET is the first
We demonstrate the effectiveness of our approach on tasks from extremely diverse domains, including fine-grained image classification in computer vision, document classification in natural language pro- cessing, and cell type annotation in biology. In the biological domain, we conduct the first systematic comparison of meta-learning algorithms. We develop a new meta-learning dataset and define a novel benchmark task to characterize single-cell transcriptome of all mouse organs (Consortium) 2018 2020). Additionally, we consider the scenario in which concepts are not given in advance, and test COMET's performance with automatically extracted visual concepts. Our experimental results show that all domains COMET significantly improves generalization ability, achieving 6-15% relative improvement over state-of-the-arr methods in the most challenging 1-shot task. Furthermore, we demonstrate the ability of COMET to provide interpretations behind the model's predictions, and support our claim with quantitative and qualitative evaluations of the generated explanations.
PROPOSED METHOD
Problem formulation. In few-shot classification, we assume that we are given a labeled training set Dtr. unlabeled query set Dyr. and a support set S consisting of few labeled data points that share the label space with the query set. Label space between training and query set is disjoint, i.e., (Y.)) (Yrr) 0. where (Y.)) denotes label space of training set and (Y₂r) denotes label space of
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query set. Each labeled data point (x. 3) consists of a D-dimensional feature vector x E R and class label y/ E [1,, K.. Given a training set of previously labeled tasks Diy and the support set S of few labeled data points novel task, the goal is to train model that generalize to the novel task and label the query set Dgr.
2.1 PRELIMINARIES
Episodic training. To achieve successful generalization to a new task, training of meta- metanneerning methods is usually performed using sampled Each episode is formed by first sampling classes from the training set, and then sampling data points labeled with these classes. The sampled data points are divided into disjoint sets of: (i) a support set consisting of a few labeled data points, and (ii) query set consisting data points whose labels are used to calculate prediction error. Given the sampled support set, the model minimizes the loss the sampled query set in each episode, The key idea behind this meta-learning training scheme is to improve generalization of the model by trying mimic the low- dawadata regime encountered during testing. Episodes with balanced training sets are usually referred to as "N-way, k-shot" episodes where N indicates number of classes per episode ("way"), and l indicates number of support points (labeled training examples) per class ("shot").
called episodes (Vinyals et al. (016).
Prototypical networks. Our work is inspired by prototypical networks (Snell et al. 2017). simple but highly effective metric-based meta-learning method. Prototypical networks learn a non non-linear embedding function fe- R + R parameterized by a convolutional neural network. The main idea is to learn function fe such that in the M -i-imnnsional embedding space data points cluster around a single prototype representation P& E RM for each class Class prototype P& is computed as the mean vector of the support set labeled with the class l:
P
fo(x:),
(I)
Sk
where Sy denotes the subset of the support set S belonging to the class k. Given a query data point Xg. prototypical networks output distribution over classes using the softmax function:
exp(-d((o(xx)). Px))
po = k: xx,x,)) = Σx
(2)
PH:)))
where d : RM + IR denotes the distance function. Query data point x,, is assigned to the class with the minimal distance between the class prototype and embedded query point.
2.2 META-LEARNING VIA CONCEPT LEARNERS
Our main assumption is that input dimensions can be separated into subsets of related dimensions corresponding to high-level,
concepts that guide the training. Such sets of potentially overlapping, noisy and incomplete human-interpretable dimensions exists in many real- world scenarios. For instance, in computer vision concepts can be assigned to image segments; in natural language processing to semantically related words; whereas in biology we use external knowledge bases and ontologies. In many problems, concepts are already available as a prior domain knowledge (Ashburner al., 22000, Murzin et al. 1995: Wah al. 2011 Mo et al.) et al. 2000), or can be automatically generated using existing techniques (Blei et al.
2019 Miller
2003 Zhang
al. 2018 Jakab al., 2018). Intuitively, concepts can be seen as part-based representations of the input and reflect the way humans reason about the world. Importantly we do not assume these concepts are clean complete. On the contrary, we show that even if there thousands of concepts, which are noisy, incomplete, overlapping, or redundant, they still provide useful guidance to the meta-learning algorithm.
Formally, let C ((()) denote set of N concepts given/extracted as prior knowledge, where
each concept c(d) {0, P is binary vector such that cfS) if i-th dimension should be used to describe the j-th concept and denotes the dimensionality of the input. We do not impose any
constraints on C, meaning that the concepts can be disjoint overlap. Instead of learning single
mapping function fe RDD + & across all dimensions, COMET separates original space into
subspaces of predefined concepts and learns individual embedding functions 1(5 RD - RM
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for each concept j (Figure 1]_ Concept embedding functions 10 named concept learners, non-linear functions parametrized by deep neural network. Each concept leamer produces its own (5) for class & computed as the average of concept embeddings of data points in concept prototypes Pk the support set:
f0 (x, co
Pk
Sk
(E
where o denotes Hadamard product. As result, each class h is represented with set of N concept prototypes {Px 1p 1-=1-
Given query data point Xq. we compute its concept embeddings and estimate their distances to the concept prototypes of each class. We then aggregate the information across all concepts by taking sum over distances between concept embeddings and concept prototypes. Specifically, for each concept embedding f (xg c(u)) compute its distance concept prototype p of given class k. and sum distances across all concepts to obtain a distribution over support classes. The probability of assigning query point x to k-th class is then given by:
exp(-E, 1o(--,a(fo' (x, o oc Pk (()))
pa k(xx)) Σx exp(- d( (xg c(6)), 1.p) p)
(4)
The loss is computed as the negative log- likelihood Lg - log ( k|x,) ) of the true class, and COMET is trained by minimizing the loss on the query samples of training set in the episodic fashion (Snell et al., 2017, VVnyals et al. 2016). In equation (4). we use euclidean distance as the distance function. Experimentally, we find that it outperforms cosine distance (Appendix B which agrees with the theory and experimental findings in (Snell et al. 2017). We note that order for distances to be comparable, it is crucial to normalize neural network layers using batch normalization (loffe & Szegedy 2015).
2.3 INTERPRETABILITY
Local and global concept importance scores. In COMET, each class represented with N concept prototypes. Given query data point x,, COMET assigns local concept importance scores by comparing concept embbeddings of the query to concept prototypes. Specifically, for a concept j in class k: the local importance score is obtained by inverted distance d(_F(²) (x, clu)), ))) Higher importance score indicates higher contribution in classifying query point to the class k. Therefore, explanations for the query point x., given by local concept importance scores, and directly provide reasoning behind each prediction. To provide global explanations that can reveal important concepts for set of query points of interest concept prototype and concept embeddings of all query points interest. Inverted average distance and can be used to rank concepts, providing insights
entire class, COMET computes average distance between
reflects global concept importance important concepts across set of examples.
Discovering locally similar examples. Given fixed concept COMET can be used rank data
points based on the distance of their concept embeddings to the concept prototype Pk of class By ranking data points according to their similarity the concept of interest, COMET can find examples that locally share similar patterns within the same class, or even across different classes. For instance, COMET can reveal examples that well reflect concept prototype, examples that very distant from the concept prototype.
3 EXPERIMENTS
3.1 EXPERIMENTAL SETUP
Datasets. We apply COMET to four datasets from three diverse domains: computer vision, natural language processing (NLP) and biology. In the computer vision domain, we consider fine- grained image classification tasks. We use bird classification CUB-200-2011 (Wah et al., 2011)) and flower classification Nloers--122 (Nilsback & Zisserman 2008) datasets, referred to as CUB and Flowers
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hereafter. To define concepts, CUB provides part-based annotations, such beak, wing, and tail of a bird. Parts were annotated by pixel location and visibility in each image. The total number of 15 parts/concepts is available; however concepts are incomplete and only subset of them is present in an image. In case concept is not present, we rely on the prototypical concept to substitute for a missing concept. Based the part coordinates, we create surrounding bounding box with fixed length to serve the concept mask c(5), On both CUB and Flowers datasets, we test automatic concept extraction. In NLP domain, we apply COMET to benchmark document classification dataset Reuters (Lewis et al. 2004) consisting of news articles. To define concepts, we use all hypernyms of a given word based on the WordNet hiearchy (Lewis et al. 2004). On all datasets, we include a concept that captures the whole input, corresponding
binary mask of all ones.
In the biology domain, we introduce a new cross-organ cell type classification task (Brbić et al. 2020) together with new dataset. We develop a novel single-cell transcriptomic dataset based on the Tabula Muris dataset (Consortium 2018: 2020) that comprises 105, 960 cells of 124 cell types collected across 23 organs of the mouse model organism. The features correspond the gene expression profiles of cells. Out of the 23, 341 genes, we select 2, 866 genes with high standardized log dispersion given their mean. We define concepts using Gene Ontology (Ashburner et al. 2000 Consortium, 2019), resource which characterizes gene functional roles in a hierarchically structured vocabulary. We select Gene Ontology terms at level 3 that have at least 64 assigned genes, resulting in the total number of 190 terms that define our concepts. We propose the evaluation protocol in which different organs are used for training, validation, and test splits. Therefore, a leaanele needs to learn to generalize to unseen cell types across organs. This novel dataset along with the cross-organ evaluation splits is publicly available at phttps:: / snap. st anford. edu/ comet) To our knowledge, this is the first meta-learning dataset from the biology domain.
Baselines. We compare COMET's performance to seven baselines, including Fine Tune/Baslline++ (Chen al. 2009b)). Matching Networks (MatchingNet) (Vinyals al., 2016). Model Agnostic Meta-Learning (MAML) (Finn et al., 2017). Relation Networks (Sung et al. 2018), MctaOptNet
(Lee et al. 2019), DeepEMD (Zhang al. 22020] and Prototypical Networks (ProtoNet) (Snell et al.
2017). DeepEMD is only applicable to image datasets.
We provide more details on evaluation and implementation in Appendix A
Code is publicly available
at httttt::///tttttt ctm/ snap- wtw.onnnnnodooooooooooooooo comet.
3.2 RESULTS
Performance comparison. We report results on CUB, Tabula Muris and Reuters datasets with concepts given as prior domain knowledge in Table [ COMET outperforms all baselines by remarkably large margin on all datasets. Specifically, COMET achieves 9.5% and 9.3% average improvements over the best performing baseline in the 1-shot and 5-shot tasks across datasets. Notably, COMET improves the result of the ProtoNet baseline by 19-23% in the 1-shot tasks across datasets. COMET's substiantial improvement are retained with the deeper Conv-6 backbone (Appendix C). To confirm that the improvements indeed come from concept learners and not from additional weights, we compare COMET to ensemble of prototypical networks, and further evaluate performance of COMET with shared weights across all concepts. Results shown in Table Table demonstrate that COMET achieves significantly better performance than the ensemble of ProtoNets even when the weights across concepts are shared. Of note, COMET's performance is only slightly affected with shared weights across concepts. More experimental details are provided in Appendix D
Effect of number of concepts. We systematically evaluate the effect of the number of concepts on COMET's performance on CUB and Tabula Muris datasets (Figure 2p. In particular, we start from ProtoNet's result that can be seen as using single concept in COMET that covers all dimensions of the input. We then gradually increase number of concepts and train and evaluate COMET with the selected number of concepts. For the CUB dataset, we add concepts based on their visibilityy frequency, whereas on the Tabula Muris we are not limited in the coverage of concepts so we randomly select them. The results demonstrate that on both domains COMET consistently improves performance when increasing the number of concepts. Strikingly, by adding just one most frequent concept corresponding to a bird's beak on top of the whole image concept, we improve ProtoNet's performance on CUB by 10% and 5% in 1-shot and 5-shot tasks, respectively. On the Tabula Muris, with just 8 concepts COMET significantly outperforms all baselines and achieves 7% and 17%
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Table Results on 1-shot and 5-shot classification on the CUB and Tabula Muris datasets, We report average accuracy and standard deviation over 600 randomly sampled episodes.
CUB
Tabula Muris
Reuters
Method
1-shot
5-shot
1-shot
5-shot
1-shot
5-shot
Finetune
61.4 1.0
80.2 0.6
65.3 + 1.0
82.1 + 0.7
48.2 + 0.7
64.3 + 0.4
MatchingNet
61.0 + 0.9
75.9
0.6
7L0 0.9
#2.4 0.7
55.9
0.6
70.9 0,4
MAMI
52.8
1.0
74.4
0.8
5..4
57.4
1.1
45.0
60.5 0.4
RelationNet
62.1 1.0
78.6
1.0
80.1
+ 0.8
38.8 0.7
68.3 = 0.3
MetaOptNet
62.2 1.0
79.6
7..
85.4
0.9 NA
62.1 0.8
77.8 + 0.4
DeepEMI
64.0 1.0
0.7
NA
NA
NA
ProtoNet
57.1 1.0
7.71
6..5
82.5 1 0.7
58.3 0.7
75.1 0.4
COMET
67.9 0.9
85.3 0.5
79.4 0.9
91.7 + 0.5
71.5 = 0.7
89.8 = 0.3
Table 2: Comparison to the ensemble of prototypical networks and COMET with shared weights across concepts. On the CUB dataset weights are always shared.
CUB
Tabula Muris
Reuters
Method
I-shot
5-shot
1-shot
5-shot
1-shot
5-shot
ProtoNetEns
64.0 = 0.8
82.3 0.5
67.2 0.8
83,6 + 0.5
62.4 + 0.7
79.3 = 0.4 88.6 + 0.3
COMET shared w
67.9 + 0.9
85.3 0.5
78.2 1.0
91.0 0.5
69.8 + 0.8
COMET
67.9 = 0.9
85.3 0.5
79.4 0.9
91.7 0.5
71.5 - 0.7
89.8 = 0.3
improvement over ProtoNet 1-shot and 5-shot tasks, respectively. To demonstrate the robustness of our method to huge set of overlapping concepts, extend the number of concepts to 1500 by capturing all levels of the Gene Ontology hierarchy, therefore allowing many redundant relationships Even in this scenario, COMET slightly improves the results compared to 190 concepts obtained from single level. These results demonstrate that COMET outperforms other methods even when the number concepts is small and annotations are incomplete, as well as with many overlapping and redundant concepts.
CUB dataset
Tabula Muris dataset
90
Test Accuracy
80
1-shot
80
70
70
60
# Concepts
Concepts
Figure 2: The effect of number of concepts COMET's performance. COMET consistently improves performance when we gradually increase number of concept terms.
Unsupervised concept annotation. While COMET achieves remarkable results with human- validated concepts given as external knowledge, we next investigate COMET's performance on automatically inferred concepts. In addition to CUB dataset, consider Flowers dataset for fine- grained image classification. To automatically extract visual concepts, we train the autoencoding framework for landmarks discovery proposed (Zhang al., 2018). The encoding module outputs landmark coordinates that we use as part coordinates. We generate concept mask by creating bounding box with fixed length around landmark coordinates. Although extracted coordinates are often noisy and capture background (Appendix FF., we find that COMET outperforms all baselines on both CUB and Flowers Line-graine classification datasets (Table 3). This analysis shows that the benefits of method expected even with noisy concepts extracted in fully automated and unsupervised way.
To test unsupervised concept annotation Tabula Muris and Reuters datasets, randomly select subsets of features for concept definition. Since COMET is interpretable and can be used to find important concepts, validation select concepts with the highest importance scores. Even

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in this case, COMET significantly outperforms all baselines, achieving only 2% lower accuracy the Tabula Muris dataset and 1% on the Reuters dataset on both 1-shot and 5-shot tasks compared to human- -emmnnd concepts. This additionally confirms COMET's effectiveness with automatically extracted concepts. We provide more results Appendix F
Table 3: Results 1-shot and 5-shot classification with automatically extracted concepts. We report average accuracy and standard deviation over 600 randomly sampled episodes. We show the average relative improvement of COMET over the best and ProtoNet baselines.
Accuracy
CUB: 1-shot
CUB: 5-shot
Flowers: 1-shot
Flowers: 5-shot
COMET
64.8 + 1.0
82.0 0.5
70.4
86.7
Improvement of COMET...
over best baseline 1.3%
1.1%
4.8%
4.6%
over ProtoNet 13.5%
7.8%
6.0%
8.1%
3.3 INTERPRETABILITY
We analyze the reasoning part of COMET'S by designing case studies aiming answer the following questions: (i) Which concepts are the most important for given query point (ie., local explanation)? Which concepts the most important for given class (i.e., global explanation)?; (iii) Which examples share locally similar patterns?; (iv) Which examples reflect well concept prototype? We perform all analyses exclusively on classes from the novel task that are seen during training.
Concept importance. Given a query point, COMET ranks concepts based on their importance scores, therefore identifying concepts highly relevant for the prediction of a single query point. We demonstrate examples of local explanations in Appendix G To quantitatively evaluate global explanations that assign concept importance scores to the entire class, we derive ground truth explanations on the Tabula Muris dataset. Specifically, using the ground truth labels on the test set, we obtain a set of genes that are differentially expressed for each class (i.e., cell type). We then find Gene Ontology terms that significantly enriched (false discovery rate corrected p-value< 0.1) in the set of differentially expressed genes a given class, and use those terms as ground-truth concepts. We consider only cell types that have at least two assigned terms. To obtain COMET's explanations, rank global concept importance scores for each class and report the number of relevant terms that are successfully retrieved in top 20 concepts with the highest scores in the 5-shot setting (Figure 3 left). We find that COMET's importance scores agree extremely well with the ground truth annotations, achieving 0.71 average recall @:20 across all cell types. We further investigate global explanations on the CUB dataset by computing the frequency of the most relevant concepts across the species (Figure right). Beak, belly and forehead turn out be the most relevant features, supporting
pancreelic enterooyle epiinellu
14
12
pancrealic
pancrealic
10
granulacyle
precursor cell
endothelial
enteroendocrine
leukocyte
pro-8
grauuoocytspoielcc
beak
belly
forehwed
tail
muscle satellte
ctypt
naive
COMET
koratinocyte
opidermis
Ground fruth
2
4
8
10
14
16
# GO Terms
Figure (Left)) Quantitatively, on the Tabula Muris dataset COMET's global importance scores agree well with the ground truth important Gene Ontology terms estimated using differentially expressed genes. (Right) Qualitatively, on the CUB dataset importance scores correctly reflect the most relevant bird features.
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common-senss intuition. For instance, 'beak' is selected as the most relevant concept for 'parakeet auklet' known for its nearly circular beak; "belly" for 'cape may warbler' known for its tiger stripes on the belly; while 'belted kingfisher' indeed has characteristic 'forehead" with its shaggy crest the top of the head. This confirms that COMET correctly identifies important class-level concepts.
Locally similar patterns. Given fixed concept of interest, we apply COMET to sort images with respect to the distance their concept embedding to the concept prototype (Figure(4) 4) COMET finds images that locally resemble the prototypical image and well express concept prototype, correctly reflecting the underlying concept of interest. On the contrary, images sorted using the whole image as concept often reflect background similarity and can not provide intuitive explanations. Furthermore, by finding most distant examples COMET can aid in identifying misannotated or non-visible concepts (Appendix HD which can be particularly useful when the concepts are automatically extracted. These analyses suggest that COMET can be used to discover, sort and visualize locally similar patterns, revealing insights on concept-based similarity across examples.
Images ranked according to their distance to the prototypical concept
beak concept
global concept
support
Figure Top row shows images with beak concept embeddings most similar the prototypical beak. Bottom row shows images ranked according the global concept that captures whole image. COMET correctly reflects local similarity in the underlying concept of interest, while global concept often reflects environmental similarity.
4 RELATED WORK
Our work draws motivation from rich line of research on meta-learning, compositional representa- tions, and concept- hansept-based interpretability.
Meta-learning. Recent meta-learning methods fall broadly into two categories. Optimization based methods (Finn et al.) 2017; Rusu et al. [2019] Nichol & Schulman 2018 Grant et al. 2018 Antoniou al. 2019) aim to learn good initialization such that network can fine-tuned to target task
within few gradient steps. On the other hand, metric-based methods (Snell et al. 2017: Vinyals jet al., 2016: Sung et al., 2018 Gidaris Komodakis) 2018) learn a metric space shared across tasks such that in the new space target task can be solved using nearest neighbour classifier. DccpEMD (Zhang et al. (2020) learns optimal distance between local image representations. Prototypical networks (Snell et al. (217)) learn metric space such that data points cluster around prototypical representation computed for each category as the mean of embedded labeled examples. has remained one of the most competitive few-shot learning methods (Triantafillou al. 2019).
simple linear
resulting in many follow-up works (Sung et al. [018 Oreshkin et al.) 2018 Ren et al. 2018 Liu
al. 2019; Xing al., 2019). Two recent works (Hou al.] 2019 Zhu et al.) proposed learn local discriminative features with attention mechanisms in image classification tasks. Our work builds upon prototypical networks and extends the approach by introducing concept- prototypes. Prototypical networks were extended by learning mixture prototypes in (Allen et al., 2019); however prototypes this work share the same metric space. In contrast, COMET defines buman-interpretable concept-specific metric spaces where each prototype reflects class-level differences in the metric space of the corresponding concept.
Compositionality. The idea behind learning from few examples using compositional represen- tations originates from work on Bayesian probabilistic programs in which individual strokes were combined for the handwritten character recognition task (Lake et al., 2011 2015). This approach was extended in (Wong & Yuille 2013)) by replacing hand designed features with symmetry axis as object descriptors. Although these early works effectively demonstrated that compositionality is
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key ingredient for adaptation in low-data regime, it is unclear how extend them to generalize beyond simple visual concepts. Recent work (Tokmakov et al. 2019) revived the idea and showed that deep compositional representations generalize better in few-shot image classification. However, this approach requires category-level attribute annotations that are impossible to get in domains not intuitive humans, such biology. Moreover, even in domains in which annotations can be collected, they require tedious manual effort. On the contrary, our approach is domain-agnostic and generates human- uumaa-unnderstndabbl interpretations in any domain.
Interpretability.
There has been much progress on designing interpretable methods that estimate the
importance of individual features (Selvaraju al. 2016 | Sundararaaan et al. 2017; Smilkov et al. 2017|Ribeirg et al.,
& Lee 2017 Melis & Jaakkola (018)). However, individual
features are often not intuitive, or can even be misleading when interpreted by humans (Kim et al.
2018). To overcome this limitation, recent advances have been focused on designing methods that
explain predictions using high-level human understandable concepts (Kim et al., | Ghorbani al. 2019). TCAV (Kim et al. 2018) defines concepts based on user-annotated set of examples in which the concept of interest appears. On the contrary. high-level concepts in our work are defined with set of related dimensions. As such, they already available in many domains, obtained in an unsupervised manner. Once defined, they are transferable across problems that share feature space. As opposed the methods that base their predictions the posthoc analysis (Ribeiro et al. 2016 Lundberg & Lee 2017]: Melis & Jaakkola 2018:: Kim et al. 20188) COMET is designed as an inherently interpretable model and explains predictions by gaining insights from the reasoning process of the network. The closest to our work are prototypes-basee explanation models (Li ct al.
can be
2018 Chen al. 2019a). However, they require specialized convolutional architecture for feature extraction and are not applicable beyond image classification, or to a few-shot setting.
5 CONCLUSION
We introduced COMET, a novel metric-based meta-learning algorithm that learns to generalize along human-intenn interpretaeee concept dimensions. We showed that COMET learns generalizable representations with incomplete, noisy, redundant, very few or huge set of concept dimensions, selecting only important concepts for classification and providing reasoning behind the decisions. Our experimental results showed that COMET does not make tradc-off between interpretability and accuracy and significantly outperforms existing methods on tasks from diverse domains, including a novel benchmark dataset from the biology domain developed in work.
ACKNOWLEDGEMENTS
The authors thank Yusuf Roohani, Michihiro Yasunaga and Marinka Zitnik for their helpful comments. We gratefully acknowledge the support of DARPA under Nos. N66001 1 1924033 (MCS); ARO under Nos. W911NF- 11FF-16-1-0344 1-0342 (MURI. 1991NFF16-1 11-1--1--1 (DURIP); NSF under Nos. OAC- 18355985 (CINES), 0AC-1934578 (HDR), 19F-1918940 (Expeditions), 1IS-2030477 (RAPID): Stanford Data Science Initiative, Wu Tsai Neurosciences Institute, Chan Zuckerberg Biohub, Amazon, JPMorgan Chase. Docomo, Hitachi, JD.com, KDDI, NVIDIA, Dell, Toshiba, and UnitedHealth Group. J. L. is Chan Zuckerberg Biohub investigator.

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A EXPERIMENTAL SETUP
Evaluation. We test all methods on the most broadly used 5-way classification setting. In each episode, we randomly sample 5 classes where each class contains k examples as the support set in the k-shot classification task. We construct the query set to have 16 examples, where each unlabeled sample in the query set belongs to one of the classes in the support set. We choose the best model according to the validation accuracy, and then evaluate it on the test set with novel classes. We report the mean accuracy by randomly sampling 600 episodes in the fine-tuning or meta-testing stage.
On the CUB dataset, we followed the evaluation protocol in (Chen et al., 2019b) and split the dataset into 100 base, 50 validation, and 50 test classes in the exactly same split. On the Tabula Muris, we use 15 organs for training, organs for validation, and organs for test, resulting into 59 base, 47 validation, and 37 test classes corresponding to cell types. The 102 classes of Flowers dataset are split into 52, 25, 25 as the training, validation and testing set, respectively. As for Reuters dataset, we leave out 5 classes for validation and 5 for test.
Implementation details. On the CUB dataset, we the widely adopted four-layer convolutional backbones Conv-4 with an input size of 84 x 84 (Snell et al. 2017). We perform standard data augmentation, including random crop, rotation, horizontal flipping and color jittering. We use the Adam optimizer (Kingma & Ba 2014) with an initial learning rate of 10 and weight decay 0. We train the 5-shot tasks for 40, 000 episodes and 1-shot tasks for 60, 000 episodes (Chen al,, 2019b). To speed up training of COMET, we share the network parameters between concept learners. In particular, we first forward the entire image x; into the convolutional network and get a spatial feature embedding fo (x,), and then get the j-th concept embedding as fx(,)) cl.), Since convolutional filters only operate pixels locally, in practice we get similar performance if we apply the mask at the beginning or at the end while significantly speeding up training time. In case the part is not annotatated (i.e., visible), we use the prototypical concept corresponding to whole image to replace the missing concept. For the Tabula Muris dataset, we use a simple backbone network structure containing two fully-connected layers with batch normalization, ReLu activation and dropout. We use Adam optimizer (Kingma& & Ba 2014) with an initial learning rate of 10-3 and weight decay O. We train the network for 1, 000 episodes. For MAML, RelationNet, MatchingNet, Fine'Tune and ProtoNet, we use implementations from (Chen et al. 2019b). For MetaOptNer and DeepEMD we use implementations from the respective papers.
B ABLATION STUDY ON DISTANCE FUNCTION
We compare the effect of distance metric on the COMET's performance. We find that Euclidean distance consistently outperforms cosine distance in fine- ine-grained image classification and cell type annotation tasks.
Table 4: The effect of distance metric on COMET's performance.
CUB
Tabula Muris
Distance
1-shot
5-shot
1-shot
5-shot
Cosine 65.7 + 1.0
82.2 0.6
77.1 + 0.9
90.1 + 0.6
Euclidean
67.9 + 0.9
85.3 0.5
79.4 + 0.9
91.7 + 0.5
C ABLATION STUDY ON BACKBONE NETWORK
We compare performance of COMET to baselines methods using deeper Conv-6 backbone instead of Conv-4 backbone on the CUB dataset. We use part based annotations to define concepts. The results reported in Table COMET outperforms all baselines by adding just one most frequent concept corresponding to a bird's beak on top of the whole image concept, COMET improves ProtoNet's performance by 3.8% 1-shot task and 2. 2.2% on 5-shot task.
with deeper backbone. Additionally,
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Table 5: Peformance using Conv-6 backbone on CUB and Flowers dataset. We report average accuracy and standard deviation over 600 randomly sampled episodes.
CUB
Method
1-shot
5-shot
Finetune
66.0 0.9
82.0 + 0.6
MatchingNet
66.5 1.99 0.9
77.9 + 0.7
MAML
6.1
78.8
0.7
RelationNet
80.2
+ 0.6
MetaOptNet
65.5
83.0 1 0.8
DeepEMD
66.8 1 0.9
83.8
+ 0.7
ProtoNet
66.4 1.0
82.0
+ 0.6
COMET- concept
68.9 68.9 0.9
83.8 + 0.6
COMET
72.2 - 0.9
87.6
+ 0.5
D ABLATION STUDY ON ENSEMBLE METHODS
We compare COMET to the ensemble of prototypical networks. We train ProtoNets in parallel and combine their outputs by majority voting typically done ensemble models. particular, given query point x and prototypes {px }e the prototypical ensemble outputs probability distribution for each ProtoNet model j:
Po (y kb/xg) = exp(- (xg): ()))
(5)
Σx exp(-dl d(f( (1) (x,): P ())
On the CUB dataset, we use 5 ProtoNets. We use smaller number than the number of concepts because training an ensemble of larger number of ProtoNets on CUB results in memory issues due to the unshared weights. On the Tabula Muris and Reuters datasets we use the same number of ProtoNets as the number concepts, that is 190 on Tabula Muris and 126 on Reuters.
UNSUPERVISED CONCEPT ANNOTATION: ADDITIONAL RESULTS
We evaluate COMET and baseline methods the Flowers dataset for fine fine-grained image classifica- tion, We automatically extract concepts using unsupervised landmarks discovery approach (Zhang al. 2018). Results in Table show that COMET outperforms all baselines by large margin.
Table 6: Results on 1-shot and 5-shot classification the Flowers dataset. We report average accuracy and standard deviation over 600 randomly sampled episodes.
Flowers
Method
1-shot
5-shot
Finetune
65.4 4: 0.9
81.9 + 0.7
MatchingNet
66.0 0.9
82.0 + 0.8
MAML
63.2
76.6 0.8
RelationNet
6.94
80.8 0.6
MetaOptNet
8.7 1.0
DeeDEMD
0.9
82.9 0.7
ProtoNet
64.4 1.0
80.2 - 0.8
COMET
70.4
0.9
86.7 + 0.6
On the Tabula Muris and Reuters datasets, we test COMET without any prior knowledge by defining concepts using selected random masks. In particular, we randomly select subsets of features as concepts and then use validation set to select the concepts with the highest importance scores as defined by COMET. We use same number of concepts used in Tabula Muris and Reuters datasets. Results are reported in Table 7
F UNSUPERVISED CONCEPT ANNOTATION: LANDMARKS EXAMPLES
To assess the performance of COMET using automatically extracted visual concepts on the CUB dataset, applied autoencoding framework for landmarks discovery proposed in (Zhang al.
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Table 7: Results on 1-shot and 5-shot classification on Tabula Muris and Retuters dataset with selected concepts. We report average accuracy and standard
random masks concepts and deviation over 600 randomly sampled episodes.
Tabula Muris
Reuters
Method
1-shot
5-shot
1-shot
5-shot
with selected random masks with prior knowledge
77.2 + 1.0
89.8 0.5
70.1 = 0.9
89.0 + 0.4
79.4 - 0.9
91.7 0.5
71.5
0.7
89.8 1 0.3
2018). We use default parameters and implementation provided by the authors, and set the number of landmarks to 30. The encoding module provides coordinates of the estimated landmarks. To create concept mask, we create bounding box around discovered landmarks. We train the autoencoder using same parameters as Zhang et al. (2018), and the number of concepts to 30. Examples of extracted landmarks for 20 images from the CUB dataset are visualized in Figure
NLD PARKING
Figure 5: Examples of automatically extracted landmarks using (Zhang et al. 2018 on the CUB dataset.
G INTERPRETABILITY: LOCAL EXPLANATIONS
Here, we demonstrate COMET's s local explanations on the CUB dataset. Given a single query data point, COMET assigns local concept importance scores to each concept based on the distance between concept embedding of the query data point to the prototypical concept. We then rank concepts according to their local concept importance scores. Figure (igurs shows examples of ranked concepts. Importance scores assigned by COMET visually reflect well the most relevant bird features.
H INTERPRETABILITE LOCAL SIMILARITY
Given fixed concept of interest, apply COMET to sort images with respect the distance of their concept embedding to the concept prototype. Figure /shows example of chipping sparrow images with the belly concept embedding most similar the prototypical belly, and images with the belly
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Support set: 1 1-shot task
Query data point
Ranked concepts
1. Beak
Forehead
Crown
Throat
Left leg
Support set: 1-shot task
Query data point
Ranked concepts
Breast
Belly
3. Crown
4. Nape
5. Forehead
Support set: 1-shot task
Query data point
Ranked concepts
1. Tail
Left leg
Right leg
Nape
Breast
Figure 6: Examples of COMET's local explanations on the CUB dataset. Concepts are ranked according to the highest local concept similarity scores. Qualitatively, local importance scores correctly reflect the most relevant bird features.
concept embedding most distant to the prototypical belly. Most similar images indeed have clearly visible belly part and reflect prototypical belly well. On the contrary, most distant images have only small part of belly visible, indicating that COMET can be used to detect misannotated or non-visible concepts.
Chipping sparrow images with belly embedding most similar the prototypical belly
Support set: 1-shot task
Chipping images with belly embedding most distant to the prototypical belly
Class: chipping sparrow Concept: belly
Figure 7: Images ranked according to the distance of their belly concept embedding to the belly concept prototype. Most similar images (top) and most distant images (bottom). Images closest to the prototype have clearly visible belly part that visually looks like prototypical belly of chipping sparrow, whereas most distant images do not have belly part clearly visible.
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Reviews:
[{"review_id": "w01y9Bl3tQs", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The paper presents a knowledge-driven prototypical learning strategy for few-shot classification tasks. The main idea of this work is to introduce a set of concepts defined in the subspaces of inputs and represent each class as a group of concept prototypes for few-shot learning. Following the prototypical networks, the method first computes the concept embeddings of an input, and then takes the summation of the distances between those embeddings and their corresponding concept prototypes in each class to estimate the class probability.  The experiments validates the proposed methods on 4 benchmarks in three different domains, including vision, language and biology. For the biology task, the authors also develop a new benchmark on cross-organ cell type classification. \n\nStrengths:\n-  The idea of introducing concept knowledge into few-shot learning in a domain-agnostic manner seems interesting. While prior work has explored the compositionality for few-shot learning in vision domain, which shares the similar idea, the specific way that this paper introduces concepts as class representation is different. \n\n-  The paper is well written and easy to follow. \n\n- The experiments show the proposed method outperforms baseline prototypical network and other prior work on all four benchmarks across three domains.\n\nConcerns:\n-  Additional supervision from concepts: Using concept-based prototypes essentially introduces a form of part-level supervision, and therefore, it is not surprising to see the improvement in few-shot learning.  In addition, while such representations make sense for transferring prior knowledge on classes and provide interpretability, it is non-trivial to obtain such concepts in some domains. For example, it is difficult to learn the keypoint concepts for general object classes in vision domain. \n\n- Eq. 4: The distance from an input to each class representation sums over individual distances from each concepts. It is unclear how such aggregation is able to handle noisy concepts and outliers. The summation would be dominated by concepts with large distances, which may not relevant to a specific input. More clarification would be helpful for this point. \n\n- The experimental evaluation seems a bit lacking. For the vision domain, the paper only evaluated this method on the fine-grained classification tasks, which is uncommon in few-shot classification literature. It would be more convincing to show the results on the miniImageNet, which is a general object classification task, and see how the concept would be generalized to such cases.  \n\n- The method use multiple prototypes for each class, which has a higher model complexity than the standard prototypical network. Instead of using ensemble strategy (as shown in the suppl), what if the ablative study evaluates the setting in which the baseline learns multiple prototypes for each class? \n\n- The effect of predefined concepts. Do the human-defined concepts play an critical role in the proposed method? What if the method uses randomly selected subsets of features for concept definition?   \n  \nUpdates: Thanks for the authors' response. The newly added experimental results address my concerns. I think this is an interesting work and recommend this paper to be accepted. ", "extra_scores": {}, "extra_text": {}}, {"review_id": "VAY0Qn2siwa", "reviewer": "AnonReviewer5", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "**Overview** \nInspired by DeepEMD's obersvation that compositional representations generalize better for few-shot image classification, this paper (COMET) introduces \"Concepts Embeddings\" components to the Prototypical Networks. \"Concepts Embeddings\" are part-based representations and are learnt by a set of independent networks $ \\\\{ f_{\\theta_i} \\\\} $ (can also share weights). \n\nFor each novel class, its $N$ concept prototypes are computed using the support set, and a query example is classified by measuring its distances to each novel class's $N$ concept prototypes. \n\nEvaluations and experiments are carried out on 3 datasets: CUB, Tabula Muris and Reuters. The proposed COMET approach obtains favorable performances on all 3 datasets.\n\n**Pros** \nThis work explore ways to learn compositional representations in a semi-supervised fashion.  It takes Prototypical Networks as baseline and adds a novel \"Concept Embedding\" component.  The authors show the proposed approach can significantly improve ProtoNet baseline on 3 datasets. It also outperforms the recent approach DeepEMD. The following observation on the CUB dataset is very interesting: \"Strikingly, by adding just one most frequent concept corresponding to a bird’s beak on top of the whole image concept, we improve ProtoNet’s performance on CUB by 10% and 5% in 1-shot and 5-shot tasks, respectively.\"\n\n**Cons** \nUsing multiple prototypes per class has been explored in \"Infinite Mixture Prototypes for Few-shot Learning\" (ICML'19), maybe the authors should mention it and highlight differences.\n\nIt's unclear where does $C$ comes from by just reading section 2.2.  How to compute $C$ is later elaborated in experiments section 3.1. Essentially $C$ are hand-crafted for each dataset. Maybe the authors should hint this in section 2.2.\n\nThe math symbols are a bit confusing in section 2.2. For instance, inconsistent notation $f_{\\theta}^{(j)}$ in section 2.2 and $f_{\\theta_j}$ in Figure 1, also $D$'s definition is not given (is it equal to the number of pixels $\\times$ number of input channel?).\n\nI appreciate the authors provinding many interesting ablation studies, but the main evaluation (Table 1) misses 2 important image few-shot classification dataset/benchmarks: miniImageNet, and MetaDataset.  How does the COMET with \"Unsupervised concept annotation\" performs on the above two datasets?\n\nWhen compared with ensemble, why specifically 5 ProtoNets? How many concepts are used in COMET and COMET (shared weight)? This ablation should be included in main paper, as network capacity can improve accuracy significantly, see: \"A Closer Look at Few-shot Classification\"(ICLR'19).\n\n\n**Reason for the decision** \nThis paper explores using compositional representations in Prototypical Networks. The authors introduce a novel concept embeddings idea, and show that it can significantly improve ProtoNet baseline on 3 datasets. However using multiple prototypes per class is not a new idea and the authors should highlight the differences with prior works (what's the strength of this particular formulation?). For the evaluation, I highly suggest the authors to include standard mini-ImageNet and Meta-Dataset FSL image classification benchmarks and report performances on them. \n\nI find the following observation on the CUB dataset  very interesting: \"Strikingly, by adding just one most frequent concept corresponding to a bird’s beak on top of the whole image concept, we improve ProtoNet’s performance on CUB by 10% and 5% in 1-shot and 5-shot tasks, respectively.\" That means human-validate concepts are complimentary to the representations learnt by a 4-layer network, but how about using a deep/stronger backbone, would it narrow the margin? I suggest the authors add this ablation study: How much does adding a concept help w.r.t to the depth of the backbone.  \n", "extra_scores": {}, "extra_text": {}}, {"review_id": "IRI5y-DVJr6", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Summary\n\nThis paper introduces potential use of intermediate structured representation of input space called “concepts” which are most likely human-interpretable. This intermediate space is then used for few-shot learning instead of using only the input space. This leads to better classification performance on the task, and it shows that injecting human-interpretable structured representation into task correlates with better performance (as one would hope). The paper uses datasets from different domains and shows improvement over approaches that don’t use the above defined “concepts”.\n\n\nStrengths\n- Paper is well-written\n- Approach is tested on different dataset domains\n- Experiments, ablation and qualitative results verify the claims of the paper\n\n\nWeaknesses\n\n- Figure 3 (Right)\n    - In CUB you mentioned that not all 15 part-based annotations are available for every image (ref. Section 3.1 - Datasets). Does that mean there is a non-uniform distribution of part annotations per class? If this is the case, should it be taken into account for Figure 3 (Right)? It could be that beak is important for most of the classes simply because it was present more often that the other parts.\n\n- Fine-grained classification only?\n    - If I understood correctly, you need concepts (or part-based annotations) to be consistently present in all input images and all classes, because if they are not and the distribution of concepts is heavily skewed towards a subset of classes, then your approach won’t be effective. Does that mean that your approach is only effective for fine-grained classification datasets? Can mini-ImageNet or Omniglot be candidates for testing?\n\n\nMinor concerns (suggestions, typos, etc.)\n- Table 1\n    - Are the methods reimplemented or you cited results from papers? Can you appropriately mention these facts and/or cite the papers where you picked numbers from in Table 1?\n    - It was a bit surprising to see MAML underperforming in comparison to all the baselines (including Finetune)\n\n\nPreliminary Rating and its justification\n\nI see no obvious issues with the paper. I recommend accept.\n\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "CioF1RY_yW1", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper proposes concept learners to effectively combines the outputs of independent concept learners. The model is evaluated on several datasets from different domains. \n\nFirst of all, why the authors define it as generalizable few-shot learning, the settings targeted in this paper seem to do no different from traditional few-shot learning. Why is it called generalizable few-shot learning?\n\nThe other two main concerns are:\n\nThe idea of learning to attend different segments of an image or learning to the segment has been proposed in previous literature [a,b,c,d]. Even if they are not specifically targeted on a few-shot image classification, the proposed concept learners are still pretty similar to previous works and are not specifically designed for few-shot image classification tasks. Thus, I believe the novelty is somewhat limited for this submission.\n\nIn experiments, even if the authors choose three datasets for comparisons. I am more interested in results on standard benchmarks, such as miniImageNet, tieredImageNet. The results on the current datasets are not a convincing performance in my point of view. It is expected to see experiments on more standard and large-scaled datasets.\n\nA minor point I am curious about is that by simple data augmentation method: crop, is it possible that multiple random cropping can generate different concepts and achieve similar effects by simply cropping multiple times on one image?\n\n\n[a] Linsley, D., Shiebler, D., Eberhardt, S. and Serre, T., 2018. Learning what and where to attend. arXiv preprint arXiv:1805.08819.\n\n[b] B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba. Learning Deep Features for Discriminative Localization. CVPR'16 (arXiv:1512.04150, 2015).\n\n[c] Zhu Y, Liu C, Jiang S. Multi-attention meta learning for few-shot fine-grained image recognition[C]//Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence. 2020: 1090-1096.\n\n[d] Hou R, Chang H, Bingpeng M A, et al. Cross attention network for few-shot classification[C]//Advances in Neural Information Processing Systems. 2019: 4003-4014.\n", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper introduces "Concept Embeddings"  to  Prototypical Network, which are part-based representations and are learnt by a set of independent networks (which can share weights).  The method first computes the concept embeddings of an input, and then takes the summation of the distances between those concept embeddings and their corresponding concept prototypes in each class to estimate the class probability. The experiments validates the proposed methods on 4 benchmarks in three different domains, including vision, language and biology. For the biology task, the authors also develop a new benchmark on cross-organ cell type classification.  The key novel idea of transferable concepts results in significantly improved generalization ability over the existing few-shot learning methods.

Although some reviewers raised concerns about not using other few-shot image classification datasets such as MiniImageNet these are not appropriate benchmarks, as the method requires the “part-based concepts” to reasonably span the space of all images which is a characteristic of fine-grained image classification problem.   Although this does limit the scope of the method, the fact that it is applicable for multiple tasks is a strong counteragument to the claim that it is too limited, so overall I disagree with the assessment of one reviewer that the choice of benchmarks is insufficient.


Decision: Accept (Poster)