Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification
Authors: Marzi Heidari
Abdullah Alchihabi
Qing En
Yuhong Guo
Venue: Submitted to ICLR 2023 (2023)

Abstract: Cross-domain few-shot classification induces a much more challenging problem than its in-domain counterpart due to the existence of domain shifts between the training and test tasks. 
In this paper, we develop a novel Adaptive Parametric Prototype Learning (APPL) method under the meta-learning convention for cross-domain few-shot classification. 
Different from existing prototypical few-shot methods that use the averages of support instances to calculate the class prototypes, we propose to learn class prototypes from the concatenated features of the support set in a parametric fashion and meta-learn the model by enforcing prototype-based regularization on the query set. 
In addition, we fine-tune the model in the target domain in a transductive manner using a weighted-moving-average self-training approach on the query instances. 
We conduct experiments on multiple cross-domain few-shot benchmark datasets. 
The empirical results demonstrate that APPL yields superior performance than many state-of-the-art methods. 

Under review as a conference paper at ICLR 2023
ADAPTIVE PARAMETRIC PROTOTYPE LEARNING FOR CROSS-DOMAIN FEW-SHOT CLASSIFICATION
Anonymous authors Paper under double-blind review
ABSTRACT
Cross-domain few-shot classification induces much more challenging problem than its in-domain counterpart due to the existence of domain shifts between the training and test tasks. In this paper, we develop a novel Adaptive Paramet- ric Prototype Learning (APPL.) method under the meta-learning convention for cross-domain few-shot classification. Different from existing prototypical few- shot methods that use the averages of support instances to calculate the class pro- totypes, we propose to learn class prototypes from the concatenated features of the support set in a parametric fashion and meta-learn the model by enforcing prototype-basee regularization model in the target domain in a transductive manner using a neighted-moving. average self-training approach on the query instances. We conduct experiments on multiple cross-domain few-shot benchmark datasets. The empirical results demonstrate that APPL yields superior performance than many state-of-the-art cross-domain few-shot learning methods.
the query set. In addition, we fine-tune the
INTRODUCTION
Benefiting from the development of deep neural networks, significant advancement has been achieved on image classification with large amounts of annotated data. However, obtaining large amounts of annotated data is consuminn and labour-intensive, while it is difficult to generalize trained models new categories of data. As solution, few-shot learning (FSL) has been proposed to classify instances from unseen classes using only few labeled instances. FSI. methods usually use base dataset with labeled images to train a prediction model in the training phase. The model is then fine-tuned on the prediction task of novel categories with few labeled instances (i.e. support set), and finally evaluated on the test data (i.e. query set) from the same novel categories in the test- ing phase. FSL has been widely studied in the in-domain settings where the training and test tasks are from the same domain (Finn et al., 2017] [nell et al. 2017 Lee et al., 2019). However, when the training and test tasks are in different domains, it poses much more challenging cross-domain few-shot learning problem than its in-domain counterpart due to the domain shift problem.
Recently, several methods have made progress to address cross-domain few-shot learning, including the ones based on data augmentation, data generation (Wang & Deng 2021] Yeh et al. 2020) Islam al. 202I]) and self-supervised learning (Phoo & Hariharan] 2020) techniques. However, such data generation and augmentation methods increase the computational cost and cannot scale well to sce- narios with higher-shots (Wang & Deng 202T). TT. Some other works either require large amounts of labeled data from multiple source domains (Hu et al.) 2022) or the availability of substantial unla- beled data from the target domain during the source training phase (Phoo & Hariharan 2020 Islam et al. 2021 Yao 2021). Such requirements are hard to meet and hence hamper their applicability in many domains. Although some existing prototypical-bot
few-shot methods have also been ap- plied to address cross-domain few-shot learning due to their simplicity and computational efficiency (Snell et al. 2017]] Satorras & Estrach| 2018), these standard methods lack sufficient capacity in handing large cross cross-domain shifts and adapting target domains.
In this paper, we propose a novel Adaptive Parametric Prototype Learning (APPL) method under the meta-learning convention for cross-domain few-shot image classification. APPL introduces parametric prototype calculator network (PCN) to learn class prototypes from concatenated feature vectors of the support instances by ensuring the inter-class discriminability and intra-class cohe-

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sion with prototype regularization losses. The PCN is meta-learned the source domain using the labeled query instances. In the target domain, we deploy a -eighted-mving--eeee self-training approach to leverage the unlabeled query instances fine-tune the prototype- based prediction model in a transductive manner. With PCN and prototype regularizations, the proposed method is expected have better generalization capacity in learning class prototypes in the feature embedding space, and hence effectively mitigate the domain shift and adapt to the target domain with WMA self-training. Comprehensive experiments are conducted on eight cross-domain few- shot learning benchmark datasets. The empirical results demonstrate the efficacy of the proposed APPL for cross- doomin few-shot classification, by comparing with existing state-of-the--e methods.
(WMA)
The contributions of our proposed method are as follows:
We propose novel adaptive prototype calculator network called Prototype Calculator Net- work (PCN), Our key contribution is that we parameterization mechanism generate more representative prototypes and propose two loss functions to learn discriminative class prototypes by enforcing both inter-class discriminability and intra-class cohesion in the extracted feature space.
2.
We propose a WMA self-training strategy that tailored for the CDFSL problem. Com- pared to existing methods, it overcomes the barrier of requiring large amounts of additional data for the target domain and reduces domain shift by generating better pseudo-labels. It ensures that the produced pscudo-labels are stable and clean (not noisy) by jointly em- ploying three mechanisms: rectified annealing schedule for the WMA and selectively sampling only the confident pseudo-labels to adapt the model.
Beghhted-Moving-Avee
updating of prediction vectors, a
3. Our proposed method work outperforms existing methods on both low-shot (5-shot) and high-shot (20-shot and 50-shot) classification tasks.
2 RELATED WORKS
2.1 FEW-SHOT LEARNING
Most FSL studies have focused on the in-domain settings. The FSL approaches can be grouped into
three main categories: metric-based and meta-learning approaches (Finn et al., 2017 Snell al.,
2017 Lee et al. 2019), transfer learning approaches (Guo et al.) | Jeong & Kim 2020 Ge &
Yu 2017 Yosinski al. 2014 Dhillon al. 2019) and augmentation and generative approaches (Zhang et al. 2018 Lim et al. 2019, Hariharan & Girshick 2017 Schwartze et al. 2018 Reed et al.)
2018). In particular, the representative meta-learning approach, MAML (Finn et al. 2017). learns good initialization parameters from various source tasks that make the model easy to adapt to new tasks. The non-pan con-paraettric metric-based approach, MatchingNet (Vinyals et al., 2016), employs at- tention and memory in order to train network that learns from few labeled samples. ProtoNet (Snell jet al. 2017) learns a metric space where each class is represented by the average of the available support instances and classifies query instances based their distances to the class prototypes. A few meta-learning works, such as RelationNet (Sung et al. and Transductive Propagation Network (TPN) (Liu et al. 2019), exploit the similarities between support and query instances to classify the query instances. MetaOpt uses meta-learning to train a feature encoder that obtains discriminative features for a linear classifier (Lee et al. 2019). Transfer learning methods initially train a model on base tasks and then use various fine-tuning methods to adapt the model novel tasks (Guo et al. Jeong & Kim 2020 Ge & Yu 2017 Yosinski al.)
2018), GNN (Satorras Estrach| 2018)
2014 Dhillon et al., 2019). Generative and augmentation approaches generate additional samples
to increase the size of available data during training (Zhang al. 2018 Lim et al. 2019 Hariharan
& Girshick 2017 Schwartz et al., 2018 Reed et al. 2018).
2.2
CROSS-DoMan FEW-SHOT LEARNING
Recently cross-domain few-shot learning (CDFSL) has started receiving more attentions (Guo et al.
2020 Phoo & Hariharan 2020). Tseng et al. (2020) propose feature-wist transformation (FWT) layer that is used jointly with standard few-shot learning methods for cross-domain few-shot learn- ing. The FWT layer uses affine transformations augment the learned features in order help
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the trained network generalize across domains. Du et al. (2022) address the domain shift problem by proposing a prototype-basee baatyy Hierarchical Variational neural Memory framework (HVM), where the hierarchical prototypes and the memory are both learned using variational inference. Adler et al. (020) propose a Cross-domain Hebbian Ensemble Fusion (CHEF) method, which applies an ensemble of Hebbian learners on different layers of the neural network to obtain representation fusion. Data augmentation and data generation methods have also been used to bridge the gap be- Iween the source and target domains (Wang & Deng, 2021; Yeh al. 2020 Islam et al.) 2021) Wang & Deng (202T) propose an Adversarial Task Augmentation approach (ATA) to generate dif- ficult training tasks in an adversarial fashion and improve the generalizability of few-shot methods across largely different domains. [slam et al. (2021) employ dynamic distillation and consistency regularization to train student and teacher network jointly on the source domain data and unlabeled data from the target domain. Hu et al. (2022) propose domain-switch learning framework with multiple source domains, and use re-weighted cross-cntropy loss and binary KL divergence loss to prevent overfitting and catastrophic forgetting. (Sun et al. (202I) adapt the explanation method of Layer-wise Relevance Propagation (LRP) to the FSL setup, which guides the FSL training by dynamically highlighting the discriminative features of the input samples.
Some other works (Trinnaafiloou et al. 2019 Liu et al. 2021 Bateni et al. 2020 Doersch et al, 2020) have tested alternative cross-domain few-shot learning settings such Meta-Dataset (Tri- antafillou et al.] (211)) CDFSL setting where models are trained on several source-domain datasets and tested on multiple target-domain datasets. In this work, we focus on the CDFSL setting in Guo iet al. (2020) as it is the most widely studied CDFSL setting.
3 APPROACH
3.1 PRELIMINARY
The cross-domain few-shot learning problem aims to train a model on the source domain with its large set of labelled instances and then adapt the model to address the prediction task in the target domain with few labeled instances. We assume the two domains have different distributions in the input space (Ps * P.) and have disjoint classes O', n >a+ fl). In the target domain, the model is N,
provided with a support set 5 = {I ) and tested on a query set Q = {( yi where N. and N₂ are the sizes of the support and query sets respectively. The support set is made up of N classes with K instances in each class, which is commonly described as N-way K-shot.
In the classic prototypical few-shot learning (Snell al. 2017), each image first goes through feature encoder fa and obtains its embedding vector in the feature space. Then, for each class in the support set, a prototype Pr. IR_ is computed as the average embedding vector of the support instances:
[(.,))s. fo(x), where S, denotes the set of K support instances from class n. To classify the query instances, the distances between each query sample and the prototypes of all classes the support set are computed. Then the softmax function is used to normalize the calculated distances to obtain the class prediction probabilities as follows:
Pn.
P(u = j/x) exp(-d(fo(z),p)
Σm= exp(-d(fe(z),p.)
where -(..-) is distance function and P(y = j|a:) is the predicted probability that query sample belonging to class j. During the meta-training phase, the model is trained to minimize the cross- entropy loss the query instances:
Lce(Q) = = NEQ (CE Yz).
(2)
where for is the cross-entropy function, P., and Y, the predicted class probability vector and ground-truth label indicator vector respectively for a query sample x.
3.2 ADAPTIVE PARAMETRIC PROTOTYPE LEARNING
In this section, we present our proposed Adaptive Parametric Prototype Learning (APPL) method for cross-domain few-shot image classification. The overall framework of APPI. is illustrated in
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gensrating paeaudo labels
query imae
untebeled query image
(e)
Aiee leserned protolypes
learnad protetypes
Care
Ca
(ex(S)
camputaltan llw
consatenated
support
concatenated
loomaale weights
Teatures
features
(a)
(b)
The proposed APPL method. (a) Training on the source domain. The concatenated feature vectors of each class are fed into the PCN (16s) to produce class prototypes, which are used to compute the meta-training loss terms on query instances. (b) Fine-tuning on the target domain. Both the labelled support set and the unlabeled query set with soft pseudo-label, computed using WMA are used to fine-tune the feature encoder in the target domain with prototype based losses.
Figure 1:
Figure [ APPL first performs meta-training in the label-rich source domain by meta learning an adaptive prototype calculator network (PCN) after the feature encoder. PCN generates the prototype of each class by segregating information from the concatenated feature vectors of the K-shot support instances. Then the trained model can be fine-tuned for the few-shot classification task in the target domain using a geighted-moiin-aaaee
(WMA) self-training approach, which aims to adapt the model to the target domain and further improve the quality of the learned class prototypes. We describe the details below.
3.2.1 ADAPTIVE PROTOTYPE CALCULATOR NETWORK
Simply averaging the support instances to calculate the class prototypes has the evident drawback of ignoring the inter-class and intra-class instance relations. To overcome this drawback, propose learn class prototypes from the support instances through a parametric prototype calculator network by enforcing both inter-class discriminability and intra-class cohesion in the extracted feature space. Such a parametric prototype generation mechanism is expected to produce more representative class prototypes from various support instance layouts, and guide the feature encoder to better adapt to the target domain through fine-tuning in the testing phase.
We define the adaptive prototype calculator network (PCN) as tha RK + R.,, where D the size of the learned embeddings by the feature encoder, fo. K is the number of support instances per class, and é denotes the parameters of the PCN. Specifically, PCN takes the concatenated feature vectors of the support instances of given class as input, and outputs the prototype of the corre- sponding class:
(3) where ai denotes the j-th support instance from class n and Pn is the learned prototype of class By feeding the support instances of each class to 18s can obtain the prototypes for all the N classes: IP P2....
Pn vhs (foncat fo(=)))),
fo(==))))))
We train the PCN during the meta-training phase using the few-shot training tasks in the source do- main. Specifically, given the feature encoder trained on the support instances, we update the param- eters of the PCN by minimizing the cross-entropy loss the query instances, (Q)))). Moreover, we introduce two auxiliary regularization loss terms, a prototype discriminative loss and a prototype cohesive loss, ensure the learned prototypes are both discriminative and representative of the un- derlying classes. To elaborate, the prototype discriminative loss Lais aims to push the prototypes of different classes away from each other and is defined as follows:
1
Lass Elpop d(pe,P))
(4)
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We in particular use squared Euclidean distance as d(:.-). ). By contrast, the prototype cohesive loss Lcon is designed to pull the prototypes and the query instances of their corresponding classes to be closer to each other:
Lenh =
EEQ. dopr)o(x)),
(5)
where Qn denotes the set of query instances from class n. Overall, PCN is meta-trained in the source domain by minimizing the following joint loss:
min Ltrain = LcE(Q) + Adis + Xcoh Leoh:
(6)
where Adis and Acoh are the trade-off hyper-parameters that control the contribution of the two regularization loss terms, Ldis and Cook, respectively.
3.2.2 WEIGHTED MOVING AVERAGE SELF-TRAINING
For cross-domain few-shot image classification, significant distribution discrepancies the input image space typically exist between the source and target domains. Hence after meta-training the feature encoder fe and the PCN dh in the source domain, it is critical to fine-tune the feature encoder fe on the few-shot test task in the target domain to overcome the cross-domain gap well adapt fo to the target test task. Due to the scarcity of the labeled support instances in the target task, we propose to employ the unlabeled query instances with predicted soft pseudo-labels size and diversity of the target data for fine-tuning and mitigate the domain shift between the source and target domains. To this end, develop a rerihted-moving-aveeee proach to compute the soft pseudo-labels and deploy the query instances for fine-tuning.
increase the
(WMA) self-training ap-
Specifically, each iteration i of the fine-tuning process, first calculate the distances between each query instance x and the class prototypes p2,.p2.... ... .P'v] produced by the PCN di from the support set S for all the N classes, and form the following distance vector for
n (x) (x( pi), d(Se: (x), (z)..)) d(Ss Pix]]
(7)
Then we use this distance vector (x)) to perform weighted moving average update and maintain a weighted- sootieedd-ooiin---ae avverngg- distance vector (z) for the current iteration as follows:
h' h'(x) ag h (x) + (1-x,) h hz=1
where a; is trade-off parameter that controls the combination weights between distances computed from the current iteration and previous iterations. The weighted weighted-moving--o
distance vectors can then be used to compute the class prediction probabilities over each query instance by using the softmax function:
P'(y -]z)) exp(-1i,(x))
exp( (pp(-hh
where (" j|æ) is the probability of the query instance being assigned to class j at iteration i. By using the predicted class probabilities as soft pseudo-labels, the query instances can subsequently be used to support the fine-tuning of fo in a self-training manner. The weighted-move moving-average average (WMA) update mechanism can stabilize the self-training process and dampen possible oscillating predictions for challenging query instances. Moreover, to increase the stability and convergence property of the WMA self-training, we adopt the following rectified annealing schedule for the WMA hyper-parameter a;:
max(amin 704-1),
(10)
where Olmin specifies the smallest value can take, and (0, 1) is reduction ratio parameter for updating the a value with increasing iterations. This annealing schedule can enable larger updates to the ht vectors in the beginning iterations fine-tuning by starting with a large value ag. while gradually reducing the degree of update with the decreasing of a; in later iterations.
With the soft pseudo-labed predicted by the current prototype- based model (f', (). the query in- stances can be deployed through cross-entropy loss to guide the further update, i.e., fine-tuning, of the feature encoder fo- However, using all pseudo-labeled query instances may lead to noisy updates and negatively impact the model due to the low-confidence predictions of the pseudo-lahels.
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Algorithm 1 Fine-tuning Procedure on Target Domain
Input: Target N-way-K-shor test task (S, Q); source trained model (fo, va);
hyper-parameter Adiss Acohs Ommin oo; 7, C, initialize: 0 -0 {h (z) = 0, va EQ)
Output: Fine-tuned feature encoder parameter
for = maxiters do
Compute Pn. vh (n for S., {xT "....,,kkk Vn
VoL 0
Compute a; using Eq. 10
for x E Q do
Compute the prediction probabilities Pi(Y (z) using 7
7
VoLfi + VoL + P(()))))) end for
joh < VoLn VoLCE(S) Adis VoLiis + Aeoh V CoL
gi+l t- 8 Vo S 0 = gi+1
end for
Therefore, we choose to only employ query instances with high prediction confidence scores that larger than predefined threshold and compute the query-based cross-entropy loss follows:
{660 LCr(E, Parr(,,,i(ylarr (Y(()));; xEQ
if max (P 6, otherwise
(C)
(11)
where (a)))x:) denotes the soft pseudo-label vector computed via Eq. 9 for query instance x; while (eax(P((Y(a)
the maximum predicted probability, max (P((Yzz)) ) used as the prediction confidence score for
query instance z. Here Lcr(E, P'(Y|=); 0, Φ) denotes the cross-entropy loss computed over the soft pseudo-labeled pair (x, (Y])) with fe and tha
In addition to the cross-entropy loss on both the support and query instances, we also use the pro- totype regularization losses, Litis and frohh, introduced in the meta-training phase to guide the fine- tuning process. Since the true labels of the query instances unknown in the meta-testing phase, we modify the prototype cohesive loss Looh and compute it on the support instances instead:
onh Σ n=1 Σ BES. d(pm. fo(x)),
(12)
where S, is the set of support instances from class n. Overall, the feature encoder is fine-tuned by minimizing the following joint loss function with gradient descent:
min Cyt Cc (S) C.g (Q) Adis Aech Cttt
(13)
This fine-tuning procedure also summarized in Algorithm
EXPERIMENTS
4.1
EXPERIMENTAL SETUP
Datasets We conducted comprehensive experiments on eight cross-domain few-shot learning (CDFSL) benchmark datasets. We use MinilmageNet (Vinyals et al., (016) the single source domain dataset, and use the following eight datasets as the target domain datasets: CropDiseases (Mohanty et al. 2016), EuroSAT Helber et al. al. (2019), ISIC (Tschandl et al. (2088), ChestX (Wang al. 2017). CUB (Wah et al. 2011), Cars (Krause al. 2013), Places (Zhou et al. 2017) and Planate (Van Hor et al. 2018). We use the same train/val/test split as Guo et al. (2020). We select the hyperparameters based on the test accuracy the MinilmageNet validation set.
Implementation Details We ResNet10 (He et al., 2016) backbone network and use simple network made up of a single linear layer followed by ReLU activation to represent PCN. We train prototype-based prediction model (feature encoder and PCN) the source domain for 400

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Table 1: Mean classification accuracy (95% confidence interval in brackets) for cross-domain 5-way 5-shot classification. and i denote the results reported in Guo et al. (2020) and Wang & Deng (202T) respectively. Transductive methods are indicated using (T). Methods sharing query data via Batch Normalization are indicated using (BN)
ChesrX
Cropiisen.
ISIC
EaroAA
Places
Planate
Cars
CUB
MoschingNa Vingals ct.al
2016
22 12
00. a0.39t
64. (50.00
MAML(HN) (Fiir mi 201
28-48
78.05
40.13ko.28)
71. 7..7
ProteNet" Sucll dly 2017
24.05 013
7..72
39.57.0.m
73. 73.29
58.54 o8)
16. 16.50/uue)
41.7
55.51,0.45)
MetsOpt LS3 2019
22 22.53
68. ma
36,26 (o)
64.44
Relation BNT Sung et 2018
21.07
72. 40
38 (5.00
65.50 (..50
64 25,
12. 7 (2.77
40. 20.06Gs.um
56.77,0.00
13.87 ac)
83. 12:2.2
42.54
a..40
70:91 (0110.00)
18.51
43. 13.70m
62 (7
TPNT77 Lar ar 201V
22 17
81.91
45.66
(7.22
71 11139/uaau as)
50.9Ki
44.54 (m
63.52
MaschinNNar1 tene es 20.00
62.74 21)
50.40g 48g
56.00 06.04
ProtoNei-FFTT Tselig emali 2050
23.77 12)
72.72 no:
38.87
RebsiionNet+FW [IBNII
Eseng ot
2020
23.95
78. 0.40:
38. 38.68
69.15 19.13gg.u
65.55
10.29
20.15g..um
59.77 77..40
GNN-FWT TTeng
40.87
02 my
70.20go. 50)
19.60 oo;
46.19 m)
(4.97
TPN-FWT ( Tyeng cl + 2020
21. 22
70.06gamm
36, 36.96
65 (5.00
86. 86.75
13. 20,20
31.08gr. mn)
58. 18,0.50
WWing
24 20)
90. (a.00
45.83
75.48
49.14kc.am
66 23.300
LRP-CAN J Sun 31 2021
76. (5.0 (..
51. (3
42.57 mn
68.57.044)
LRF-GNN Sun el 2021
7:145
5d. M6ga.00
40.20gg.am
64.44o.445
CHER aram
24.72 10)
41,26 m..
74.15
HVM or
27. 27.15%
87.63
42,05
(PPL(T)
24.87 4:3
92.5110050
46.28
79.78
68.84yp
(5.2000.a5)
52.67
67.446g.7o
epochs with 100 meta-training tasks and 15 query instances per class. Adam optimizer with weight decay of le-2 and learning rate of le-6 is used to train the APPL. The trade-off parameters Adia and Acoh are set to 0.1 and 1e-3 respectively. The proposed APPL is evaluated on 600 randomly selected few-shot learning tasks in each target domain. We fine-tune the feature encoder for 100 iterations for each task with learning rate of le-2. For the fine-tuning hyperparameters, ammnn Og. 7 and take the values of 0.1, 0.5, 0.99 and 0.4 respectively. For all experiments we report the average accuracy (%) well as 95% confidence interval.
4.2 COMPARISON RESULTS
4.2.1
LEARNING WITH FEW SHOTS
We first evaluate the performance of the proposed APPL method on the common cross-domain 5-
way 5-shot classification tasks. We compare APPL with both set of representative FSL methods
(MatchingNet (Vinyals et al. 2016), MAML (Finn et al. (017). ProtoNet (Snell et al. 2017). Re-
lationNet (Sung et al. 2018). MetaOpt (Lee al., (019), GNN (Satorras & Estrach 2018 and
TPN (Liu et al. 2019 ) and five statc-of-the-arr CDFSL methods (FWT (Tseng et al.) 2020). ATA
Waang& & Deng ) LRP (Sun al.) 2021), CHEF (Adler al.) 2020) and HVM (Du et al.,
2. FWT has been applied jointly with five standard FSL methods: MatchingNet, ProtoNet, RelationNet, GNN and TPN. LRP has been applied jointly with two standard FSL methods: GNN and Cross-attention network (CAN). The comparison results are presented in Table where the top part of the table reports the results of the standard FSL methods and the bottom part reports the results of the CDFSL methods.
We can see that the CDFSL methods (ATA, CHEF, HVM, LRP and APPL.) designed specifically to handle vast differences between source and target domains perform largely better than the standard FSL works for in-domain settings. FWT however only produces improvements in most cases over its base RelationNet. Notably, the proposed APPL outperforms all standard FSL methods includ- ing ProtoNet and ProtoNet+FWT on all the eight datasets. In particular, its performance gain over ProtoNet is remarkable, exceeding 10% on four out of the eight datasets, which highlights the im- portance of the prototype learning network. In addition, APPL outperforms all the CDFSL methods on five datasets, and produces the second best results the two datasets. These results demonstrate the effectiveness of the proposed APPL method for cross- moos--dmminn few-shot learning.
4.2.2 LEARNING WITH HIGHER SHOTS
We further investigated CDFSL with higher-shot tasks in the target domain. In particular, we evaluate the proposed method with cross-domain 5-way 20-shot and 5-way 50-shot learning tasks on four target-domain datasets (ChestX, CropDiseases, ISIC and EuroSAT), works also reported results. To handle higher-shot problems and increase the scalability of APPL, extend APPL. by adding clustering function g (details are given in appendix) prior to the PCN
which previous
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Table 2: Mean classification accuracy (95% confidence interval within brackets) for cross-domain 5- way 20-shot and 50-shot classification. denotes results reported in Guo et al. (2020) Transductive methods are indicated using (T). Methods sharing query data via Batch Normal ization are indicated using (BN)
ChestX
Croppiseesess
ISIC
FuroSAT
20-shot
50-shot
20-chot
MaschingNer Vinvals et
2116
23. 11
22. 12 ss)
p6.38
45. (5.7
54.5%
77.10)
54.44%
MAML(BN) Finn 2i 2017
27. 15.55..4
59. 59.75
52. 52.566..377
81.95
ProtoNat Smcll
1..52/1.133
15.15)
59.5
june;
62:27(a.57
80.18.a.57
MetaOpt Las at 2019
25.58 09)
29.35
91.76 82.89(a.54)
49. 22. 90)
54. 50
79.19
(3.02
ReletionNerreINT [Siin S ml. 2018
26 26.63a.9
28, 28.45g1
90.45pu.4
41. a5.0
85.08
49: 49.32
14.43
74.91
MakchinNee4PPW11 (osnng
23.23..33
23.00 (4:44)
74.500mmm
p go.401...44
63.38
(2.77.
ProteNet-FWT
Tschig qral 1050
26.87..433
30.12;0 453
a5.82
43.78jo.17 87.17
(7)
19. 19.84
75.74k.mm
78.0 28.044..57
FWl [1B1) (esng ena 2020
27. 27.56 29
26. 26.15m 41)
78. 78.43
81.14..00
46. 46.38 43.31
60. 60.40gu
73.9 73.94
CHEP(Allee 2020
31 11.25,0 no
54.300a.31) 96.77.0.00
60. 60.86 1x:
88.31
86.50,0.13
HVM
31.54...49
32 02.76mu.e as3
95, 13
97.80ma.00
ml.71
(4.8)
87.16)
APPLTT
30.76
33. 33.14:g.5
95 35.77
98. 14
57. 57.97
62.17r0.a3
88 a..))
89. 19.75
component. The g function clusters the support instances in each class into K' clusters based on their learned embeddings. The obtained cluster centroid vectors are then concatenated input for PCN. We compared APPL with both standard FSL methods and several CDFSL methods, and the results are presented in Table [ where the top part of the table presents the results of the FSL methods and the bottom part presents the results of the CDFSL methods.
We can see that again the CDFSL methods (CHEF, HVM, and APPL) outperform the standard FSL methods. The performance gains of APPL over Protonet and Protonet+FWT are remarkable exceeding 6% and 9% on three datasets (CropDiseases, ISIC and EuroSAT) in the cases of 20-shot and 50-shot respectively. Moreover, APPL consistently outperforms all the other methods on all the four datasets for both the 20-shot and 50-shot cases. These results again validate the effectiveness of APPL for cross-domain few-shot learning and demonstrate its capacity in handling cross-domain higher-shot learning problems. Two factors account for our proposed method's s good performance in the case of higher shots: First, benefiting from the proposed WMA self-training approach in the target domain, are able to generate more accurate pscudo-labels with higher shots, which enables our model to obtain better results. Second, we conduct clustering over the embeddings of support instances to generate class centroids with higher shots, which can eliminate some noisy information and allow the PCN to learn the most representative features.
4.3 ABLATION STUDY
To investigate the importance of each component of the proposed APPL approach, we conducted
an ablation study to compare APPL with its six variants: (1) "-w/o es,"" which drops PCN and replaces it with a simple average of the support instances of each class. (2) "-w/o Like" and (3)
"-w/o Leok", ", which drop the Lais loss and Looh loss respectively. (4) "-w/o LCE(S)", which drops the cross-entropy loss over the support instances in fine-tuning. (5) "-w/o Ce 'Q)", which drops the cross-entropy loss over the query instances and hence the WMA self-training component in fine-tuning. (6) "ProtoNet", which can be considered as a variant of APPL that drops both PCN and WMA self-training, well as Lais and Leoh.
We compared APPL with its six variants on the cross-domain 5-way 5-shot setting on all the eight datasets, and the results are reported in Table 3 We can see that APPL outperforms all the other variants on almost all datasets. The "-woo (S)" variant produced the largest performance drop among all variants, which highlights the importance of the few labeled support instances for fine-tuning in the target domain. The performance degradation for ProtoNet and "-w/o vo." " high- lights the importance of the proposed PCN component. In addition, wwwoo v.." outperforms Pro- toNet, which underlines the performance gain obtained by using pseudo-labeled query instances with WMA self-training and the prototype regularization losses in the absence of PCN. The other three variants, "ww/o Lais", " "-w/o Lcoh" and "-w/o f (Q)", also perform worse than APPL, which verifies the contributions of the two prototype regularization loss terms and the WMA self- training component respectively.
8

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Under review as a conference paper at ICLR 2023
Table 3: Ablation study results for cross-domain 5-way 5-shot classification tasks.
ChestX
Cisp
ISIC
EuroSAT
Places
Planate
Cars
CUB
ProtoNet
24.05
79. (9.72
(0
73. (3.29)
58. (4.0.)
46.80g0.05)
41.74
55.51
APPL
(4.87
92.3 32.51
46.28.o.64
68.84 79.78().78
(0.20)))
(1.67
(7.46
-w/o tig
22.33)o.56)
89.11 89.11 (0.00)
(..99)
(0.57 (o.9)
53.6910.60)
80.30
(4.03(
w/o Cdis
24.84(0.09
91.31 (0.72)
q3.23
66.18(p.1)) (8.25
54.56
81.56;0.5)
60.45
w/o Look
23.77
90.19
43.
79.53
65. (6.89
51. 61.62().77)
a0.32(oan)
60.96
w/o (S)
21.08
59. 15
26. 78.90().7))
49.95
34. (9.
25.72(0.77
27.19
30.12
w/o C. Q
22.36
88. 88.24(0.a0) 22.36
42. (o.
77 63
67.1 67.14 ((0.9))
55. 55.69
51.59/o.22)
(0.222(.88)
Accurcy
02
Accuncy
01-
Accurany
Acourazy
15-
05
07
Ada
(a) exo
(b)
(c) Aook
(d) Adia
Figure 2: Sensitivity analysis for the proposed method on hyyer--arameteen cip, e, Aonh and Aais on
CropDisease dataset under cross-domain 5-way 5-shot task: (a) co. (b) (c) Aeohs (d) Adie.
HYPER-PARAMETER SENSITIVITY ANALYSIS
To demonstrate the effect of the hyper-parameters of our proposed method, we summarize the re- sults of various studies in Figure The figure shows the performance of our proposed method on CropDisease dataset under the cross-domain 5-way 5-shot task as we modify each separately. As seen from the results, it is clear that our proposed APPL is not sensitive to the choice of value for og, as the performance of APPL is stable across all values of og- As for Acoh. too large too small value will pull the query/support instances to the prototypes weakly, thus having negative impact on the results. A reasonable value between le-l and 5e-3 required obtain reasonable performance. In the case of Adis. the experimental results improve as the value of Adis increases. And the results become stable when the parameter reaches le-1. We believe that this is because Adie controls the scale of pushing the prototypes of different classes away from each other so that when the distance between the prototypes reach a certain threshold, embedding no longer susceptible to mutual influence.
strongly more
Finally, it is worth noting that is an important hyperparameter, represents the degree of certainty of the pseudo-labels predictions utilized fine-tuning. When c too small, more unlabeled sam- ples are used in fine-tuning with their noisy pseudo-labels, and when is too large, less unlabeled samples are used fine-tuning with less noisy pseudo-labels. Experimental results show that when the intermediate value of 0.4 is selected, the number and accuracy of pseudo-labels can be balanced and the best results for this model were obtained.
CONCLUSION
In this paper, we proposed novel Adaptive Parametric Prototype Learning (APPL) method to ad- dress the cross-domain few-shot learning problem. APPL, meta-trains an adaptive prototype caleu- lator network in the source domain to learn more discriminative and representative class prototypes from various support instance layouts, which can then guide the feature encoder to adapt to the target domain through fine-tuning. Moreover, adopted to enhance fine-tuning by exploiting the unlabeled query instances in the target domain to mitigate domain shift and avoid overfitting support instances. Experimental results eight benchmark cross-domain few-shot classification datasets demonstrate that the proposed APPL out- performs existing state-of-the-art methods.
siightd-moovin--averd
self-training approach

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Under review as a conference paper at ICLR 2023
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Algorithm 2 Training Procedure on Source Domain
Input: Source domain dataset Da, K, .N; pre-trained feature extractor fo: learning-rate " and 72:
Output: Learned model parameters 6 Initialize: 0 4 Qu, 6-60
for iter 1 to maxiters do
V 6- randomly sample class indices from all classes in D.
for in (1.-., N do
Sn, 2n + randomly sample support & query sets for class n from D end for
S = S1 U.. UN. Q- Q1 U..QQN for initer=1
maxiniters do 0 - 0 - VoLcn
end for
Compute Luse and Looh with Eq. and (q. respectively.
Lirain = Adis + Acoh Lais
for (I,1) E E do
Ltrain + Ltrain + (CE(1,)) end for
f- 72 V Derrain
end for
A TRAININGG ON SOURCE DOMAIN
We present the meta-training procedure for our proposed APPL in Algorithm 2
B DETAILS ON LEARNING WITH HIGHER SHOTS
In order demonstrate the scalability the proposed APPL the case higher shots experimental setup, propose clustering- based solution to guarantee that the Adaptive Prototype Calculator Network scales efficiently with the number of support samples.
Specifically, first cluster the embeddings of support instances of each class into K' clusters as follows:
/ (() fo(kk)))
(14)
where g the clustering function that takes as imput the learned embeddings of the support instances of a given class. As a result, the class centroids μ (μu ", are obtained, where K' is the number of clusters. this process, the clustering function g learns the cluster centroids by minimizing the following loss function:
K K'
min Letust
ΣΣ ((cololl0())) j=16=1
- pell?,
(15)
P.c
where e the clustering assignment vector. Then, we use the concatenation of the obtained cluster centroid vectors as input to the adaptive prototype calculator network follows:
ps th (conet ( ::, pk:)),
(16)
Consequently, the number of learnable parameters our adaptive prototype calculator network fixed regardless of the number of support instances in each class, which ensures the proposed APPL to be scalable and easy to apply in the cases of higher shots.
C IMPACT OF THE INPUT ORDER OF ADAPTIVE PROTOTYPE CALCULATOR NETWORK
To demonstrate the effectiveness of the order of the input samples fed to the adaptive prototype calculator network, summarize the results in Table Since the concatenated embeddings of the
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Under review as a conference paper at ICLR 2023
support set instances are fed as input to the adaptive prototype calculator network to generate the prototypes of each class, is important to demonstrate that APPL is resilient to the concatenation order of the support samples. Therefore, domain 5-way 5-shot learning task under different datasets where the order of the input samples the adaptive prototype calculator network is randomly permuted during the evaluation step of the fine-tuning step. We generate 5 different random permutations of the input samples and report the corresponding results for each permutation in Table
evaluate the performance of APPL under the cross-
Table 4: Input order of Adaptive Prototype Calculator Network results in terms of mean classifi- cation accuracy (95% confidence interval within brackets) on 4 target domain datasets using the cross-domain 5-way 5-shot task.
ChestX
CropDiseases
ISIC
EuroSAT
Permutation 1
24.79(0.36)
(0.77
46. 46.12(0.555
79.69(
Permutation # 2
24.68(0.35)
92.73 (0.78)
46.29 46.29 (0.54)
79.57 79.57 (0.72)
Permutation 3
24.82(0.36)
92.52
(6.28
(9.68)
Permutation # 4
24.90
92.54(0.76)
46. 46.18(0.55) (0.55)
79. (o.74)
Permutation # 5
(0.47
92.51
79.78
14


Reviews:
[{"review_id": "jmg6XCw01Ep", "reviewer": "Reviewer_TtNB", "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": "This paper proposes to perform few-shot recognition, and introduces two innovations. The first is that instead of averaging support image features to get a prototype, the proposed approach meta-learns a model that produces a prototype from concatenated features. The second innovation is to perform self-training with weighted moving averaging on the target domain. ", "summary_of_the_review": "I think this is a reasonable paper and worth accepting, but I would like to see how it does on the traditional FSL benchmarks.", "strength_and_weaknesses": "Strengths:\n- The proposed approach is novel.\n- The performance of the approach is quite good.\n- The ablations are very informative.\n\nWeaknesses\n- The method seems general enough to apply to all few-shot learning systems. Why restrict to the cross-domain setting?\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "WnqqKzMXREW", "reviewer": "Reviewer_BfM8", "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": "This paper tackles the problem of cross-domain few-shot classification, in which novel classes and base classes belong to different data distribution. The authors propose a novel Adaptive Parametric Prototype Learning method based on prototypical network. For meta training, class prototypes are learned by enforcing both inter-class discriminability and intra-class cohesion. For finetuning approach, weight-moving-average self-training is applied. Experiments are conducted on a single source dataset and eight target datasets. Performance of the proposed method is competitive against other few-shot or cross-domain few-shot learning methods.", "summary_of_the_review": "This paper studies a challenge problem of cross-domain few-shot classification. However, the problem is studied by many previous works. Methods proposed by this work can achieve better performance than most of previous works, but the contribution and novelty towards CDFS problem is limited. In addition, this work has some problem with writing and clarity. So from my perspective, it is below the acceptance threshold. ", "strength_and_weaknesses": "Strength:\n+ The studied cross-domain few-shot problem is more challenge than simple few-shot classification problem. Methods for CDFS are more robust towards real scenarios.\n+ Experiments are conducted on eight target domain datasets. It’s more complete than some previous works. And performance of the proposed method is competitive.\n\nWeaknesses:\n- The proposed method combines some losses and self-training method which is widely used in domain adaptation with prototypical networks. Novelty of this work is a bit limited. \n- Authors didn’t summarize their contributions and the superiority of this work over previous few-shot and cross-domain few-shot methods, since this paper is not the first to tackle cross-domain few-shot classification problem. \n- There are too many losses and hyperparameters in the proposed method, searching for the best combination of hyperparameters may be hard.\n- A few mistakes in writing. E.g. λ in Fig.1. or L ? Quotation marks in section 4.3 are incorrect. Chinese quotation marks are used by the authors.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "FV6ZAvyck1-", "reviewer": "Reviewer_9xQ2", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "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": "This paper proposes an adaptive parametric prototype learning approach for one specific setting of the cross-domain few-shot learning (CDFSL) classification. \n\nTechnical contributions: (1) the authors proposed the prototype calculator network (PCN) to learn new prototypes for the support set with meta-training; (2) a weighted-moving-average (WMA) self-training strategy was proposed and applied to the query images at meta-test time in a transductive manner. \n\nExperiments: the proposed algorithm is evaluated on both Guo et al. (2020) and FWT (Tseng et al., 2020) settings for CDFSL. Higher performances are achieved on the part of the eight investigated datasets with different improvements. An ablation study and parameter analysis are conducted. ", "summary_of_the_review": "The technical contributions are insufficient for an ICLR paper, the experimental verification is also less evident.   \n\nI think this paper is not ready for publishing in ICLR. ", "strength_and_weaknesses": "Strength:\n- This paper proposed the Adaptive Parametric Prototype Learning (APPL) method, which includes PCN and WMA submodules designed for the CDFSL problem. \n- Experiments are conducted on eight datasets, and slightly improved results are obtained on some datasets. \n\nWeaknesses: \n- Technically, the proposed modules (PCN and WMA) provide little new knowledge for the few-shot and cross-domain few-shot learning field. A series of improvements for Prototypical Networks is proposed, e.g., [R1-R3] to name a few. Learning from the concatenated support features is similar to set-to-set representation [R4]. WMA explored the idea of transduction with moving average updates, which was also discovered in previous work [R5, R6]. \n- Experimentally, the performance improvements are marginal or negative on most dataets, e.g., in Table 1, the proposed method underperforms baselines on three datasets, overlaps on three datasets, and only outperforms with a significant margin on two datasets. Similar results are in Table 2. \n- This paper builds on the cross-domain setting of Guo et al. (2020). However, as a cross-domain few-shot learning paper, it should also discuss other cross-domain settings, at least in the related work section, e.g., [R2, R7-R10]. \n- In Table 1 and Table 2, it is better to indicate the transductive/inductive setting of each method.   \n- Typos. In Sec 2.2., (1) \"... methods. for cross-domain few-shot learning . The FWT ...\", (2) \"... across largely different domains .\", (3) in Figure 1, \"(a)\" and \"b)\" are inconsistent.\n\n\n\n\n\n[R1] Yang, Boyu, et al. \"Prototype mixture models for few-shot semantic segmentation.\" European Conference on Computer Vision. Springer, Cham, 2020.   \n[R2] Bateni, Peyman, et al. \"Improved few-shot visual classification.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.   \n[R3] Jiang, Ruijin, and Zhaohui Cheng. \"Mixture Gaussian prototypes for few-shot learning.\" 2021 International Conference on Data Mining Workshops (ICDMW). IEEE, 2021.   \n[R4] Ye, Han-Jia, et al. \"Few-shot learning via embedding adaptation with set-to-set functions.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.   \n[R5] Chen, Wentao, et al. \"Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images.\" arXiv preprint arXiv:2105.11874 (2021).   \n[R6] Islam, Ashraful, et al. \"Dynamic distillation network for cross-domain few-shot recognition with unlabeled data.\" Advances in Neural Information Processing Systems 34 (2021): 3584-3595.   \n[R7] Triantafillou, Eleni, et al. \"Meta-dataset: A dataset of datasets for learning to learn from few examples.\" arXiv preprint arXiv:1903.03096 (2019).   \n[R8] Liu, Yanbin, et al. \"A multi-mode modulator for multi-domain few-shot classification.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021. \n[R9] Bateni, Peyman, et al. \"Improving few-shot visual classification with unlabelled examples.\" (2020).   \n[R10] Doersch, Carl, Ankush Gupta, and Andrew Zisserman. \"Crosstransformers: spatially-aware few-shot transfer.\" Advances in Neural Information Processing Systems 33 (2020): 21981-21993.   \n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "-97jgkPo4sH", "reviewer": "Reviewer_EF7b", "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": "In this paper, the authors propose a novel Adaptive Parametric Prototype Learning for cross-domain few-shot learning. The idea is to learn class prototypes from a parametric prototype calculator network. The class prototypes  are learnt from concatenated feature vectors of the support instances to ensure inter-class discriminability and intra-class cohesion. The proposed approach is shown to be effective for cross-domain few-shot learning. ", "summary_of_the_review": " The authors propose a novel Adaptive Parametric Prototype Learning for cross-domain few-shot learning which has some novel ideas. However, the motivations are not clearly stated and it is hard to understand where is the improvement comes from. ", "strength_and_weaknesses": "Strength:\n1. The problem of cross-domain few-shot learning is important and the authors propose to improve ProtoNet for cross-domain few-shot learning. \n2.  The authors conducted extensive experiments to show the benefits of the proposed approach. \n\n\nWeaknesses:\n1. Many design choices lack motivation. For example,\ni. What's the benefits of using the concatenated feature vectors for computing the prototypes? What's the issue of using the feature from the last layer?\nii. Why use the unlabeled target can reduce the domain shift?\niii. What's the motivation of using a weighted-moving-avarage?\n\n2. The proposed method has many hyperparameters which it hard to use the method for other applications. \n\n3. What is the time complexity of the proposed approach compared with the baselines? The introduction of the adaptive prototype calculator network is an additional overhead. \n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject