Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper proposes to tackle the problem of domain incremental learning (in which its setting is distinct from the problem of class-incremental learning. In domain incremental learning, different domain data arrive in a sequence, one domain per incremental phase, while these domains share the same group of object/image classes), in which the domain indexes are not given during the inference (therefore dissimilar to the setting of task-incremental learning). As other incremental learning scenarios, the catastrophic forgetting is one of the biggest challenges in domain incremental learning, where the domain gap is main reason behind to cause the forgetting (i.e. while learning to classify new domain data, the model's ability on classifying old domain data could deteriorate). In order to tackle such catastrophic getting issue, instead of additionally maintaining portion of old domain data as what replay-based approaches do (under the consideration for better data security and privacy), the proposed method learns a set of prompts over (pretrained and fixed) transformers where the domain-specific knowledge is stored by a prompt pool, in which during the inference the classification is done by firstly identifying the domain ID (via K-NN to search for the nearest domain centroids obtained by K-means on training data to the test image feature) then feeding the corresponding domain-specific prompt and the image tokens to the transformer to perform the classification via the corresponding domain-specific classifier. With the fact that the proposed method is able to not only achieve image prompt learning (via ViT) but also a brand-new language-image prompt learning (via CLIP), the extensive experiments are conducted to show the superior performance of the proposed method against various baselines (including exemplar-free and replay-based incremental-learning methods, self-supervised learning method, and the recently published prompting methods) on several datasets. 
strengths_and_weaknesses: Pros:
+ The proposed method is built upon pretrained (image or even language-image) transformers (without any fine-tuning needed) with advancing the existing prompting methods of continual learning on a novel idea: although existing methods also aim to learn the domain-specific prompts, their learning is dependent across domains thus potentially leading to less separation for the classes from different domains, while the proposed method particularly drives the prompt learning independently across domains to achieve the best for each domain. Experimental results as well as ablation study will verify the contribution of such novel idea. 
+ The proposed prompt learning (with only little overhead needed to store the domain-specific prompt) surpasses not only other prompting methods but even the replay-based incremental learning approaches (where the exemplars of old domain data are explicitly stored in the replay buffer and used during the learning for new domain data to alleviate the catastrophic forgetting) and the expansion-based incremental learning approaches (e.g. DER++, where different feature extractors are adopted per domain) , which again demonstrates the contribution of the novel idea behind the proposed method.

Cons:
- Though the authors do provide additional experiments to demonstrate that the random assigning domain ID or having errors on domain identification (i.e. to firstly recognize which domain that the test data belongs to) would not have huge impact on the final classification performance, such argument seems to be contradictive to the idea of having domain-specific prompts and learning them in a domain-independent way. In other words, if using the wrong domain-specific prompt can still provide the slightly worse but comparable performance, then perhaps it could be unnecessary to perform domain identification. Instead, directly having the ensemble over the results produced by using each of the domain-specific would potentially contribute the best performance? The authors are highly encouraged to provide more discussion on this potential contradiction and experiment the variant of ensemble in the rebuttal.
- Though the t-SNE visualizations show that the proposed method is able to produce more separation between the classes from different domains, but actually the degree of separation is not that significant (if we consider the high-level idea illustration provided in Figure.1, where the domain identification ideally would produce different subspaces across domains). The authors are highly encouraged to provide further analysis (e.g. are we able to define a metric to measure the degree of separation thus having more objective comparison between different methods) and discussion on such aspect. 
questions: Overall I pretty enjoy reading the whole paper and like the novel idea of learning domain-specific prompts in a domain-independent way (which strike a much better balance between alleviating the forgetting and learning to recognize new domain data) as well as its applicability on pretrained and fixed image/language-image transformers. The questions that I would encourage the authors to address in the rebuttal, for further strengthening the paper, are the ones listed in cons, including the potential contradiction between the domain-specific prompts and the robustness to erroneous domain identification, and the insignificant separation between different domains. 
limitations: there is no potential negative societal impact
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: The submission focus on the catastrophic forgetting problem in domain-incremental learning, in which a model needs to learn from different domains sequentially. To achieve the goal in an exemplar-free way, the authors design an S-Prompting mechanism. A new trainable prompt is introduced every time a new domain appears. The trained prompt is then added to a prompt repository. At inference time, when new data comes, the prompt of the corresponding domain is retrieved and prepend to the input of ViT or CLIP. Extensive experiments are conducted and the best S-Prompts surpasses not only exemplar-free methods but also some exemplar-based method.
strengths_and_weaknesses: Strengths:

1. The proposed approach is clear and elegant. It uses prompt to store the domain-related feature while keeping the backbone model unchanged though it is not the first time that prompt method is used for continual learning. Besides, no additional space is required to store the exemplar.

2. The experiment is extensive and persuasive. Previous methods are properly considered. The performance is impressive, especially when compared with exemplar-based methods.

3. The writing is clear and easy to follow.

Weaknesses:

No serious weaknesses found.
questions: 1. I could that see there is an analysis in Section 4 and supplementary materials about the overheads of the proposed methods compared to its backbone, but there is no information about the memory overheads of the baselines. I would suggest listing the parameter scale of the baseline methods as well.

2. What do you mean by ``dynamic classifers'' in line 246 and line 272? The term never appears in the approach section.
limitations: The limitations are adequately discussed in the submission.
review_title: null

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: This paper proposes to address DIL by (1) learning a pool of prompts and classifier heads (one for each domain) for ViT and CLIP (2) inferring domain label based on K-means and KNN. 
strengths_and_weaknesses: Strengths
1. This paper is written clearly and generally easy to follow.
2. The proposed idea is simple.

Weaknesses
1. I’m not sure whether the performance improvement over baselines (especially L2P and DyTox) are due to the proposed method or the usage of CLIP. The comparisons based on the same backbone, ViT (corresponding to S-iPrompts), don’t show a clear improvement over the two prompt-based baselines. And the significant improvement comes from the CLIP-based models. 
2. Related to the previous point, the good performance of random domain selection (Table 4) seems to imply that much of the performance is due to a simple prompt + backbone transformer.
3. Minors: there’re typos and grammatical errors and they should be corrected. e.g., cheep prompts -> cheap prompts?
questions: Can the authors clarify or disentangle the contributions from (1) CLIP as a backbone and (2) learning independent prompts for each domain?
limitations: See weaknesses and questions above.
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 proposed S-Prompting and two concrete approaches to highly reduce the forgetting degree in continual learning scenarios, i.e., domain incremental learning (DIL).

The main idea is to learn independent prompts across domains with pre-trained transformers. 

The independent prompt can achieve the best for each domain.

The learning method derives an image prompt learning approach and a brand-new language-image prompt learning approach. 

The methods outperformed all three standard DIL tasks.

strengths_and_weaknesses: (+) Compared with prior works: L2P and DyTox, the proposed S-Prompts learn the tasks independently.

(+) The S-prompts are technically sound and seem to be well supported by experimental results on the three standard Domain-IL benchmark datasets, and outperformed others. 

(-) In Tables 1 and 2, for fair comparisons with others, the S-prompts compared with others under a little bit different architecture.

(-) S-prompts (S-liPrompts) seems strongly dependent on CLIP (a pre-trained Image-text mapping function), which decreases the main novelty.


questions: In Tables 1 and 2, for fair comparisons with others, the architecture should be the same as the base-ViT model. 
In the appendix, as the authors state, the number of parameters -ConViT(78M) and base-ViT(86M) are almost similar. However, I think if the structures are one another, the representations also differ.
I would recommend the authors compare the performances under the same architecture such as the base-ViT(86M).

limitations: The authors discuss the limitation of the S-prompts properly in the script.
review_title: null

Meta-review: metareview: This paper adopts the prompt learning idea into continual learning to tackle the problem of domain incremental learning. The proposed approach is clear, the writing is easy to follow. The experiment is convincing. 
confidence: Certain
Decision: Accept