From: ICLR.cc/2023/Conference/Program_Chairs
title: Paper Decision
decision: Reject
metareview:_summary,_strengths_and_weaknesses: The paper introduces a method based on Gaussian fusion with an adaptive induction network to address the problem of noisy samples in few-shot learning. This is an important problem. The paper has a clear motivation and the proposed method achieves good empirical results in standard datasets. The main weaknesses pointed out by the reviewers were limited novelty and incomplete evaluation. The author response provided new experiments on TieredImageNet but did not eliminate the main concern regarding insufficient technical contributions to pass the acceptance bar of ICLR.
justification_for_why_not_higher_score: While the paper addresses an important problem and achieves good experimental results on standard datasets, all four reviewers have independently raised the concern that the novelty of the paper is limited (see all references provided in the reviews). The AC appreciates the author response and the new results on TieredImageNet, but considers the paper does not pass the acceptance bar of ICLR, given the incremental nature of the proposed approach coupled with the fact that the gains over SOTA methods are not large.
justification_for_why_not_lower_score: N/A
summary_of_AC-reviewer_meeting: N/A

From: ICLR.cc/2023/Conference/Paper4602/Authors
title: Thanks and Response to the Reviewer x78M Comments-(2/2)
comment: **Q3:** Algorithm 1 seems to add transductive concatenation and self-attention to the Induction Module, but it does not explain the details clearly. For example, how $x\_{ij}$ and $x\_q$ are concatenated, and whether $x\_q$ is one vector or all vectors in Q. 
In addition, although this paper quoted the Induction Module, there is no explanation for this part, which will confuse the reader, such as the meaning of squash.  
**Response:** Thank you for your kind comments. We apologize for the reading distress caused by unclear expressions. It may be that our symbol makes you confused. In order not to be confused with the sample $ x $, we fixed the $ x\_{ij}^s $ to $ z\_{ij}^s $, and $ x\_q $ to $ z^q $, $ z^q $ is a vector in Q. At the same time, we checked the symbolic representation of the paper. We added the description of Algorithm 1, as shown below: 
Using the multi-head self-attention mechanism, the support vector $ z\_{ij}^s $ and query vector $ z^q $ are concatenated to calculate the relationship score; each support vector has its weight attached to the current query vector. Then, we apply dynamic routing to obtain a class vector. The process adjusts the connection's strength dynamically and makes sure that the sum of the coupling coefficients $ d_i $ between class $ i $ and all of its support samples is 1. The difference is that when adjusting the logits of coupling coefficients in the last step of every iteration, we consider not only the consistency of class candidate vectors and sample prediction vectors but also the relationship between query and support vectors.
We added some definitions of Algorithm1, such as,  $ \hat{z\_{ij}^s} $ is the prediction vector, $ p_{i}^{\\prime} $ is the class candidate vector, $ squash(p\_{i}^{\\prime})=\frac{\||p\_{i}^{\\prime}\||^{2}}{1+\\||p_{i}^{\\prime}\||^{2}} \frac{p\_{i}^{\\prime}}{\||p\_{i}^{\\prime}\||} $,  and $ squash $ is a non-linear squashing function.  

**Q4:** This paper does not give the details of how to use the mixed prototype to calculate the classification results, although the paper uses a simple prototype network. In fact, this part is important for the completeness of the paper.   
**Response:** Thank you for your kind suggestions! In 3.2.2, we added the mean-based prototype calculation to Equation 1, and then represented the estimate methods of the two prototypes separately.  

**Q5:** What is the operation of replacing the classifier with the feature extractor in the pre-training stage mentioned in the paper? Why it is beneficial to the solution of downstream tasks.   
**Response:** Thank you for your comments! By removing the last Softmax layer, the backbone is used to compute embeddings for the subsequent stages, while the classification head is discarded. The idea behind such design is that learning task-agnostic knowledge about prototype rectification from base classes and then applying this knowledge to novel classes to obtain more reliable prototypes. We use the base class samples to train the feature extractor, which obtains a better embedded representation.  

**Q6:** Some state-of-the-art methods are not compared in the experimental results. More importantly, because the proposed method is a transductive method, more transductive-based methods should be compared. Also, the inductive and transductive methods should be marked to distinguish.   
**Response:** Thank you for your great suggestions! We added the Setting column in Table 1, Table 2, and Table 3 to distinguish between Inductive and Transductive. Due to the space limitation, we have deleted some Inductive methods and added more Transductive methods, such as, AIM [3] and Curvature Generation [4] on *mini*ImageNet, Curvature Generation [4], ECKPN [5], EPNet[6] and BD-CSPN on *tiered*ImageNet.  
 
**Q7:** In addition, why only specific backbones are used for specific datasets? For example, why ResNet-12 is not used on miniImageNet and why WRN-28-10 is not used on tieredImageNet?  
**Response:** Thank you for your valuable comments! In the Appendix C, we discussed the classification results of different backbones on *mini*ImageNet and selected the best backbone to compare with others. However, due to the large size of the WRN-28-10 and *tiered*ImageNet, this experiment is missing. We added the backbone of WRN-28-10 on *tiered*ImageNet, the accuracy of 5-way 1-shot is 79.04 ± 0.81% and 5-way 5-shot is 88.65 ± 0.49%.  
Reference  
[1] Prototype Rectification for Few-Shot Learning. ECCV 2020.  
[2] Prototype Completion with Primitive Knowledge for Few-Shot Learning. CVPR 2021.   
[3] Lee et al, Few-shot and continual learning with attentive independent mechanisms, ICCV 2021.  
[4] Gao et al, Curvature generation in curved spaces for few-shot learning, ICCV 2021.  
[5] Chen et al, ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning, CVPR 2021.    
[6] Rodr et al, Embedding propagation: Smoother manifold for few-shot classification, ECCV 2020.

From: ICLR.cc/2023/Conference/Paper4602/Authors
title: Thanks and Response to the Reviewer x78M Comments-(1/2)
comment: Thank you for your kind comments! The main comments and our specific responses are detailed below.  
**Q1:** The writing of this paper could be improved. In addition, there are many confusions on the notations in this paper. For example, as for $ Q=\\left\\{(x^q, y^q)\\right\\}\_{q = N \\times K+1}^{N^{\\prime} \\times K} $ in Section 3.1, $N^{\\prime}$ is not defined. In Section 3.2, the $f_{\\theta_f}(x)$ represents both the feature extractor and the feature, the variance is represented by both $\\theta$ and $\\sigma$, and so on.  
**Response:** Thank you for your kind suggestions and cautious examinations.
There is a typo in $Q=\\left\\{\\left(x^q, y^q\\right)\\right\\}\_{q = N \\times K+1}^{N^{\\prime} \\times K}$, it should be $Q=\\left\\{\\left(x^q, y^q\\right)\\right\\}\_{q = N \\times K+1}^{N \\times K^{\\prime}}$. The query set contains examples of the same $ N $ classes in $ S $. $ K^{\\prime} $ is the quantity of each class in $ Q $. There is a misspelling, we fixed $ f\_{\\theta_f}(x) $ to represent features, and the variance is fixed to $\\sigma$.

**Q2:** The technical contribution of this paper is incremental for using the existing techniques from multiple works, such as Induction module (Geng et al., 2019) and DC (Yang et al., 2021). In fact, some works have tried the similar idea proposed in this paper, such as [1] and [2], which are recommended to be analyzed.   
**Response:** Thank you for your suggestion!  Liu et al. [1] used the pseudo-label to reduce intra-class bias, but it is easy to introduce noise. Our method reduces the intra-class bias by adaptively allocating sample weights without using pseudo labels, which alleviates the problem of noise. Yang et al. [2] calibrated the data distribution of novel classes by migrating similar base class distribution statistics, which may result in error due to sample noise or class differences. Our method does not use the similar features in the base class to enhance the novel class features. Zhang et al. [3] introduced primitive knowledge, regarded the two prototypes as samples from Gaussian distributions, and weighted the average to obtain class prototypes. To solve the problem that the calculation of a single prototype is not comprehensive, we obtained two prototypes from different angles and directly used the mean of the fusion distribution to obtain the class prototype, reducing the distribution estimation parameters and not introducing additional knowledge. To sum up, the above studies introduce sample noise or class differences. Our method aims to address the issue of sample noise in few-shot learning. Induction module [4] dynamically induces the class vector, but because the model has not seen query samples before extracting support features, some inappropriate features may be extracted. In order to learn a more representative class vector and reduce sample noise, we propose an adaptive induction network that pays more attention to effective instances for every query sample. We have made changes to the introduction.  
[1] Liu et al, Prototype rectification for few-shot learning, ECCV 2020.   
[2] Yang et al, Free Lunch for Few-shot Learning: Distribution Calibration, ICLR 2021.  
[3] Zhang et al, Prototype Completion with Primitive Knowledge for Few-Shot Learning, CVPR 2021.  
[4] Geng et al, Induction networks for few-shot text classification, EMNLP 2019.

From: ICLR.cc/2023/Conference/Paper4602/Authors
title: Thanks and Response to the Reviewer kAKG Comments
comment: Thank you for your suggestions! The main comments and our responses are detailed below.  
**Q1:** The novelty of the proposed methods is limited. The proposed method makes incremental modifications and combinations on existing methods (Geng et al. (2019) and Yang et al. (2021)).  
**Response:** Yang et al. [1] calibrated the data distribution of novel classes by migrating similar base class distribution statistics, which may result in error due to sample noise or class differences. Our method does not use the similar features in the base class to enhance the novel class features. Induction module [2] dynamically induces the class vector, but because the model has not seen query samples before extracting support features, some inappropriate features may be extracted. In order to learn a more representative class vector and reduce sample noise, we propose an adaptive induction network that pays more attention to effective instances for every query sample. We have made changes to the introduction.  
References  
[1] Yang et al, Free Lunch for Few-shot Learning: Distribution Calibration, ICLR 2021.  
[2] Geng et al, Induction networks for few-shot text classification, EMNLP 2019.   
 
**Q2:** Some important details are missing. For instance, the authors should discuss more on $p_m$ and $c_i$. Why and in which way they could learn mutual affiliations with each other?  
**Response:** To better expression, we changed the $ p_m $ to $ \hat{p_i} $, and $ c_i $ to $ p_{i}^{\prime} $. We added the calculation of $ \hat{p_i} $ in Equation 1, $ \\hat{p\_i}=\\frac{1}{\\left\|\\mathcal{S}\_{i}\\right\|\} \sum\_{x \\in \\mathcal{S}\_{i}} f\_{\\theta\_{f}}(x) $ , where $ S\_i $ represents the support set extracted for the class $ i $, and $ f\_{\theta\_f}(x) $ is the support feature. Then, we describe the process of Algorithm 1 in 3.2.1. Finally, we added some definitions of Algorithm1, such as,  $ \hat{z_{ij}^s} $ is the prediction vector, $ p_{i}^{\prime} $ is the class candidate vector, $ squash(p_{i}^{\prime})=\frac{\||p_{i}^{\prime}\||^{2}}{1+\||p_{i}^{\prime}\||^{2}} \frac{p_{i}^{\prime}}{\||p_{i}^{\prime}\||} $, where $ squash $ is a non-linear squashing function.  

For few-shot learning, when the data is limited (1-shot or 2-shot), more similar and representative features need to be selected to learn. However, with the increase of samples (3/4/5-shot), AINet will lose more local information while the mean-based prototype gets more samples, and the difference between the mean-based prototype and the theoretical expectation decreases gradually. Most methods only calculate prototypes on a single level. Therefore, we get the fusion of prototypes from different levels, mainly by viewing the prototypes as samples from Gaussian distributions and then using the mean of the fusion distributions to calculate the prototype representation.

From: ICLR.cc/2023/Conference/Paper4602/Authors
title: Thanks and Response to the Reviewer WNef Comments
comment: Thank the reviewer for the time spent and the review. We would like to discuss some of the concerns addressed by the reviewer.  
**Q1:** The main idea is to propose a new method to rectify the classical prototype network, similar to the previous work 'Prototype Rectification for Few-Shot Learning' i.e. BD-CSPN (Liu et al. (2020)). However, the authors do not provide sufficient analysis of the differences. It is confusing for the readers to understand the advantages compared with BD-CSPN.  
**Response:** For the problem of prototype bias, Liu et al. [1] used the pseudo-label to reduce intra-class bias, but it is easy to introduce noise. Our method reduces the intra-class bias by adaptively allocating sample weights without using pseudo labels, which alleviates the problem of noise.  
[1] Liu et al, Prototype rectification for few-shot learning, ECCV 2020.   

**Q2:** The experimental results are not complete. For example, BD-CSPN provided the results on *tiered*ImageNet. However, the authors do not make the comparison in the experimental section. This leads to a question: does the proposed method truly outperform the BD-CSPN based on the rectification prototype.  
**Response:** Thank you for your suggestion! We previously lacked experimental results on *tiered*ImageNet using WRN-28-10 as the backbone due to computational power, so we did not compare the results with BD-CSPN. At present, we have supplemented the comparison results of our method and BD-CSPN in Table 2. In 5-way 1-shot settings, the accuracy of our method is improved by 0.49%, and in 5-way 5-shot settings, it is improved by 1.73%. It verifies the effectiveness of our model.  
| WRN-28-10 | 5-way 1-shot | 5-way 5-shot |
| :---------: | :------------: | :------------: |
| BD-CSPN   | 78.74 ± 0.95 | 86.92 ± 0.63 |
| FP_AINet  | 79.23 ± 0.70 | 88.65 ± 0.49 |  

**Q3:** In Fig. 2, the authors had better add the results of FP_AINet to enhance the benefits of the proposed method.  
**Response:** Since Figure 2 is a structure diagram, we think the reviewer mentioned Figure 3. It has been supplemented in Figure 3. The prototypes generated by FP\_AINet are visualized using t-SNE. A 5-way 1-shot task of *mini*Imagenet is shown in Figure 3, where circles represent query samples and different colors denote different classes, star points represent PN features, pentagons are AINet features, and squares represent FP\_AINet. It is demonstrated that the prototypes generated by FP\_AINet are much closer to the class center, which can effectively learn the representation of a prototype and improve the capacity of the support set. 
 
**Q4:** In Alg. 1 the adaptive induction network output $c_i$, but there is no $c_i$ in Fig. 2.  
**Response:** Thank you for your comments, which helped us to improve the quality of this paper.  
We unified the class vector of $ c_i $ in Algorithm 1 and Figure 2 by $ p_{i}^{\prime} $. To better illustrate, the prototypes are all represented by p, subscripts represent the prototypes of the $ i $ th class, and superscripts represent different ways of obtaining prototypes.  

**Q5:** In Eq. 3, it is confusing to use $p_m$ in the numerator but use $p_c$ in the denominator. What is the reason?  
**Response:** Thank you for your review! To demonstrate, we changed the $ p_m $ to $ \hat{p_i} $. The numerator of $e^{d\left(f_{\theta_f}(x), \hat{p_i}\right)}$ represents the exponential function of cosine similarity of $ f_{\theta_f}(x)$ and $ \hat{p_i}$, the denominator of ${\sum_c e^{d\left(f_{\theta_f}(x), p_c\right)}}$ represents exponential function sum of cosine similarity of $ f_{\theta_f}(x) $ and all prototypes of c classes.  

**Q6:** In Alg. 2, only the mean $μ_f$ is used for the fusion prototype. Have the authors considered adding the variance for further improvement? By the way, it is better to use to $μ_f$ replace $μ_g$, which is consistent with Eq. 2.  
**Response:** Thank you for your review! We sincerely appreciate your suggestions on weighted average fusion, our experiments show that the accuracy of 5-way 1-shot is 70.67 ± 0.73% on *mini*ImageNet, lower than our results. We fixed the $ \mu_g $ and $ \mu_f $ to $ \mu_i $ in the Equation and Algorithm.

From: ICLR.cc/2023/Conference/Paper4602/Authors
title: Thanks and Response to the Reviewer 8vWo Comments
comment: Thank you for your kind comments! The main comments and our specific responses are detailed below.  

**Q1**：The similar idea has already been adopted in recent works, such as  

**Response**：Thank you for your suggestion! Through search and analysis, the papers that are similar to ours are as follows: For the problem of prototype bias, Liu et al. [1] used the pseudo-label to reduce intra-class bias, but it is easy to introduce noise. Our method reduces the intra-class bias by adaptively allocating sample weights without using pseudo labels, which alleviates the problem of noise. Yang et al. [2] calibrated the data distribution of novel classes by migrating similar base class distribution statistics, which may result in error due to sample noise or class differences. Our method does not use the similar features in the base class to enhance the novel class features. Zhang et al. [3] introduced primitive knowledge, regarded the two prototypes as samples from Gaussian distributions, and weighted the average to obtain class prototypes. To solve the problem that the calculation of a single prototype is not comprehensive, we obtained two prototypes from different angles and directly used the mean of the fusion distribution to obtain the class prototype, reducing the distribution estimation parameters and not introducing additional knowledge. To sum up, the above studies introduce sample noise or class differences. Our method aims to address the issue of sample noise in few-shot learning. Induction module [4] dynamically induces the class vector, but because the model has not seen query samples before extracting support features, some inappropriate features may be extracted. In order to learn a more representative class vector and reduce sample noise, we propose an adaptive induction network that pays more attention to effective instances for every query sample. We have made changes to the introduction.  

References  
[1] Liu et al, Prototype rectification for few-shot learning, ECCV 2020.   
[2] Yang et al, Free Lunch for Few-shot Learning: Distribution Calibration, ICLR 2021.  
[3] Zhang et al, Prototype Completion with Primitive Knowledge for Few-Shot Learning, CVPR 2021.  
[4] Geng et al, Induction networks for few-shot text classification, EMNLP 2019.    

**Q2**: The computation cost should be reported in the experimental results.  

**Response**: According to your suggestions, we have supplemented the experimental results of computation cost. In the table below, it displays a detailed analysis of different modules in our method and the results of a 5-way 1-shot task on *mini*ImageNet. Compared with the baseline of PN, FLOPs increased by 16M due to the introduction of self-attention structure parameters. The operation of fusion almost without additional calculations. The parameters of our method only increased by 410.9 K, but the accuracy improved by 10.66%. The discussion has been added to Appendix B.

|     | FLOPs    | Params    | Accuracy     |
| :------------------: | :------: | :-------: | :----------: |
| Backbone (WRN-28-10) | 36.193 G | 36.47 M   | -            |
| PN                   | + 0      | + 0       | 61.47 ± 0.66 |
| AINet                | + 16.0 M | + 410.9 K | 63.88 ± 0.66 |
| Ours                 | + 16.0 M | + 410.9 K | 72.13 ± 0.73 |

From: ICLR.cc/2023/Conference/Paper4602/Reviewer_8vWo
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: The paper propse a method to assign scores to support samples and use Gaussian-based fusion algorithm to aggregate prototypes, in order to imigate the effect of the noise samples. in few-shot learning.
strength_and_weaknesses: Strength:
(1) The issue of noise labled data in the few-shot learning is very important and necessary to research further.
(2) The method of FR_AINet which can generate a more representative prototype makes sense and achieve a higher performance.

Weakness:
(1) The similar idea has already been adopted in recent works, such as 
(2) The computation cost should be reported in the experimental results.
clarity,_quality,_novelty_and_reproducibility: The quality of the work is mediate, and the clarity of the paper is satisfied, and the originality of the work is incremental.
summary_of_the_review: Based on the limited originality of the work, I recommend to weakly reject the paper.
correctness: 2: Several of the paper’s claims are incorrect or not well-supported.
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.
flag_for_ethics_review: NO.
details_of_ethics_concerns: None.
recommendation: 5: marginally below the acceptance threshold

From: ICLR.cc/2023/Conference/Paper4602/Reviewer_WNef
confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
summary_of_the_paper: This paper proposes to use an adaptive induction network to rectify the prototype network for few-shot learning. The adaptive induction network is developed to address the noisy samples, while this network has degraded performance with the increase of support samples since it ignores the relative local features. Thus, the authors propose a Gaussian-based fusion to combine prototype and induction networks. The extensive experimental results demonstrate the proposed method achieves better performance than the prototype network.
strength_and_weaknesses: Strength

1. Most of the paper is well-written and easy to understand.

2. Fig.2 , Alg. 1 and Alg. 2 are clear and helpful in reproducing the proposed method.

3. The ablation studies clearly demonstrate the effectiveness of the main components of the proposed method.

Weaknesses

1. The main idea is to propose a new method to rectify the classical prototype network, similar to the previous work 'Prototype Rectification for Few-Shot Learning' i.e. BD-CSPN (Liu et al. (2020)). However, the authors do not provide sufficient analysis of the differences. It is confusing for the readers to understand the advantages compared with BD-CSPN.

2. The experimental results are not complete. For example, BD-CSPN provided the results on tieredImageNet. However, the authors do not make the comparison in the experimental section. This leads to a question: does the proposed method truly outperform the BD-CSPN based on the rectification prototype? 

3. In Fig. 2, the authors had better add the results of FP_AINet to enhance the benefits of the proposed method.

4. In Alg. 1 the adaptive induction network output $c_i$, but there is no $c_i$ in Fig. 2.

5. In Eq. 3, it is confusing to use $p_m$ in the numerator but use $p_c$ in the denominator. What is the reason? 

6. In Alg. 2, only the mean $\mu_f$ is used for the fusion prototype. Have the authors considered adding the variance for further improvement? By the way, it is better to use $\mu_g$ to replace $\mu_f$, which is consistent with Eq. 2.


clarity,_quality,_novelty_and_reproducibility: The whole paper is clear and easy to reproduce. However, the novelty is limited.
summary_of_the_review: The novelty is incremental since the main idea is similar to the previous work based prototype rectification. The experimental results are insufficient to demonstrate its effectiveness, especially for the comparison with BD-CSPN.
correctness: 2: Several of the paper’s claims are incorrect or not well-supported.
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.
flag_for_ethics_review: NO.
recommendation: 5: marginally below the acceptance threshold

From: ICLR.cc/2023/Conference/Paper4602/Reviewer_kAKG
confidence: 2: You are willing to defend your assessment, but it is quite likely that you did not understand the central parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
summary_of_the_paper: A new method based on Gaussian distribution with AINet is proposed to address the problem of noisy samples in few-shot learning. More Specifically, a novel AINet is developed to focus on query-related support samples to decrease the weight of less informative samples. In addition, Yeo-Johnson transformation is further utilized to make the feature distribution more Gaussian-like. Finally, a Gaussian-based fusion algorithm is employed to obtain more accurate prototypes.
strength_and_weaknesses: Strength:
1. Strong empirical results.
2. A modified Gaussian-based fusion algorithm is employed to aggregates prototypes from PN and AINet by exploring the unlabeled samples.

Weakness:
1. The novelty of the proposed methods is limited.  The proposed method makes incremental modifications  and combinations on existing methods (Geng et al. (2019) and Yang et al. (2021)).
2. Some important details are missing. For instance, the authors should discuss more on p_m and c_i.  Why and in which way they could learn mutual affiliations with each other?
clarity,_quality,_novelty_and_reproducibility: Generally speaking, the paper is well-organized and easy to follow up. The paper should be easy to reproduce if the authors could release their implementations. 
summary_of_the_review:  The paper improves existing few-shot methods by using a modified Gaussian-based fusion method. It shows strong empirical results on multiple datasets when compared with challenging baselines.  However, the proposed method makes incremental modifications  and combinations on existing methods and thus the novelty is limited.
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: 2: The contributions are only marginally significant or novel.
flag_for_ethics_review: NO.
recommendation: 6: marginally above the acceptance threshold

From: ICLR.cc/2023/Conference/Paper4602/Reviewer_x78M
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: This paper proposes a method FP_AINet by using an adaptive induction network to obtain fusion prototype for addressing the issue of noisy samples in few-shot learning. Extensive experiments are conducted on multiple datasets, showing the effectiveness of the proposed method.
strength_and_weaknesses: * Strength

1. The motivation of using fusion prototype is sound. 

2. The experimental results of the proposed method are good.


* Weaknesses

1. The writing of this paper could be improved. In addition, there are many confusions on the notations in this paper. For example, as for {Q=\{(x_q,y_q)\}_{q=N\times K+1}^{N'\times K}} in Section 3.1, N' is not defined. In Section 3.2, the {f_{\theta_f}()} represents both the feature extractor and the feature, the variance is represented by both \theta and \sigma, and so on.

2. The technical contribution of this paper is incremental for using the existing techniques from multiple works, such as Induction module (Geng et al., 2019) and DC (Yang et al., 2021). In fact, some works have tried the similar idea proposed in this paper, such as [1] and [2], which are recommended to be analyzed. 

3. Algorithm 1 seems to add transductive concatenation and self-attention to the Induction Module, but it does not explain the details clearly. For example, how x_ij and x_q are concatenated, and whether x_q is one vector or all vectors in Q. In addition, although this paper quoted the Induction Module, there is no explanation for this part, which will confuse the reader, such as the meaning of squash.

4. This paper does not give the details of how to use the mixed prototype to calculate the classification results, although the paper uses a simple prototype network. In fact, this part is important for the completeness of the paper.

5. What is the operation of replacing the classifier with the feature extractor in the pre-training stage mentioned in the paper? Why it is beneficial to the solution of downstream tasks.

6. Some state-of-the-art methods are not compared in the experimental results. More importantly, because the proposed method is a transductive method, more transductive-based methods should be compared. Also, the inductive and transductive methods should be marked to distinguish.

7. In addition, why only specific backbones are used for specific datasets? For example, why ResNet-12 is not used on miniImageNet and why WRN-28-10 is not used on tieredImageNet?


[1] Prototype Rectification for Few-Shot Learning. ECCV 2020.

[2] Prototype Completion with Primitive Knowledge for Few-Shot Learning. CVPR 2021.


clarity,_quality,_novelty_and_reproducibility: * Clarity: The writing could be improved to make this paper more clear.

* Quality: The overall of this paper is good.

* Novelty: The novelty is incremental and limited.

* Reproducibility: It seems to be reproducible but some details are missing.

summary_of_the_review: In general, the contribution of this paper is limited because of the limited novelty and unclear details.
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: 1: The contributions are neither significant nor novel.
flag_for_ethics_review: NO.
recommendation: 3: reject, not good enough