Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper tackles Few-shot Class-incremental Learning (FSCIL) problem which is designed to incrementally recognize novel classes with only few training samples after the (pre-)training on base classes. A dilemma exists that we can hardly achieve both good base-class performance and novel-class generalization simultaneously. 

This paper analyzes the effects of positive margin and negative margin loss to the novel and based classes and designs a new network in which negative margin loss is attached to the lower layer and positive margin loss is attached to the top layer. Further improvement method based on the adjacency matrix is also proposed. Extensive experiments on three datasets show the effectiveness of the proposed method. 
strengths_and_weaknesses: Strengths
- Enhancing the novel class generality while keeping the discriminative features for the base model with positive and negative margin losses, which are attached to lower and upper layers is an elegant solution. 
- Further improvement method based on the adjacency matrix of all classes is proposed. 
- Detailed analysis of the dilemma of FSCIL is shown. 
- Experimental results show that the proposed method outperforms SOTA methods on FSCIL. 

Weaknesses
-  The proposed method is based on existing findings in the paper on the negative margin matters[15]. (Thought the method and analysis of this paper are more sophisticated.)

questions: - For the ablation study, how about the case when the positive margin loss is attached to the lower layer, and negative margin loss is attached to the higher layer? Is the proposed design of the loss function positions really better?  

- For the experiment of Sec.4.5, which dataset is used? 

limitations: The authors did not address the potentially negative societal impact of this work.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper studies margin-based few-shot class-incremental learning (FSCIL), which is motivated by an observation that positive (negative) margins adopted in base training can negatively influence the transferability (discriminability) of the representations. The authors explained this dilemma as a class-level overfitting problem from the perspective of pattern learning and claimed the cause of such class-level overfitting is due to the fact that the constraint of learning shared or class-specific patterns can be easily satisfied. To address this problem, the authors proposed simultaneously adopt positive and negative margin and learn positive-margin-features from negative-margin features. Experiments on a set of FSCIL datasets show the proposal can achieve STOA performance. 
strengths_and_weaknesses: Paper Strength

+ The connection between margins and pattern learning is interesting. It is an interesting observation that positive margin pushes the sparsity and fitness of patterns but harms the transferability of the patterns. This observation provides insight in understanding the generalisation of deep neural networks. 
+ Thorough experiments are conducted to evaluate the effectiveness of the proposed method. The results reveal that the method can simultaneously improve the performance of base and novel classes and thus is empirically proven to be able to overcome the dilemma of the margin-based classification. 

Weakness
- There lacks evidence to support some important claims made in this paper. For example, the authors claimed the cause of the class-level overfitting problem lies in the easily-satisfied constraint of learning shared (e.g., corner or edge features) or class-specific patterns (complex class-specific features that cannot generalise). But how to verify this? Additional visualization seems necessary. 
- It makes sense to consider the class relations into class-specific margin. But the necessity to setting two key hyper-parameters $m_{upper}$ and $m_{ave}$ manually makes the method less elegantly. How these two parameters are determined? Is this method sensitive to such parameters?
- From Table 3 it can be the baseline method performs very well even better than most of the comparing methods. More detailed explanation to the baseline and its performance should be added.   

questions: 1. How to justify the claim about the dilemma is from the easily-satisfied constraint of learning shared (e.g., corner or edge features) or class-specific patterns (complex class-specific features that cannot generalise).
2. How $m_{upper}$ and $m_{ave}$ are set?
3. Why baseline method performs so well?
4. the authors find large margin can lead to sparse patterns. Is this observation to some extent relevant to supervision collapse observed in [1], which described lose any information that is not necessary for performing the training task, including information that may be necessary for transfer to new tasks or domains?
limitations: Yes
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper deals with the few shot incremental learning task. The authors identify the inferior trade-off between base- and novel-class as a class-level overfitting problem during the margin-based training, and propose a new margin-based learning method with extra constraint to overcome the problem. The experiments show the advantage of the proposed method on certain commonly used benchmarks. 
strengths_and_weaknesses: Originality
Pros: Two branches for pos and neg margin setting, respectively, are new and interesting to me.
Cons: The class relation in the inter-class logit is not new, but is studied by many methods in the face recognition community.

Quality
Pros: The overall quality of this paper is good. The method is well formulated, based on the solid observation and analysis. The experiments are clear to show the effectiveness.
Cons: It lacks the comparison with the similar methods in the face recognition community.

Clarity
Pros: The method and the experiments are well presented.
Cons: Missing definition of horizontal and vertical axes in Fig. 2.

Significance
Pros: The proposed method will be useful for practical application of incremental learning task.
Cons: -

questions: It is better if the paper includes the extra experiments on face recognition datasets.


limitations: No obvious negative societal impact observed.
review_title: null

Meta-review: metareview: This work studied the few-shot class-incremental learning in the margin-based classification. It presented a deeper analysis about the dilemma between the base-class and novel class performance, from the perspective of positive and negative patterns corresponding to positive and negative margins. Although this dilemma had been observed and analyzed in some previous works, the analysis in this work is deeper and novel. The provided method is inspired by the analysis. Although it is simple, but reasonable and effective verified in experiments. The authors also provide convincing responses to most concerns. 

In summary, I think this work is well motivated, well writing. It is a professional work, could be accepted to NeurIPS. 
confidence: Certain
Decision: Accept