Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The goal of proposed method is to overcome the catastrophic forgetting in continual setting, while promoting the forward knowledge transfer, and it proposes a simple soft-masking approach applied to the gradients during back-propagation.
strength_and_weaknesses: (+) it simply uses gradient masks that can be easily computed by using gradient values.

(+) the importance values can also be used in EWC framework, and this is computationally cheaper than using the Fisher information.

(+) masking is only applied during the learning phase, and it is not required during the inference time.

(-) the proposed method shows competitive performance compered to other methods, but it is not well-studied why it works better than others.

(-) it is unclear why the loss $\text{Sum}(\mathcal{M}_\tau(X_t))$ for the previous task $\tau < t$ is useful to compute the importance value. 


clarity,_quality,_novelty_and_reproducibility: - The overall performance seems meaningful, but it is not well explained why the proposed method works better than others.
- Are there some results comparing 'soft'-masking with 'hard'-masking? 
- Recalculating the results by measuring the 'improvements from NCL performance', it will give better understanding of overcoming CF.
summary_of_the_review: - The method is simpler than other methods and the overall performance seems meaningful, but it is not well explained why the proposed method works better than others.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: The paper presents a new method (SPG) for task-incremental learning. SPG uses soft-masks to condition the full model for each task. Experiment results on benchmark datasets demonstrate the effectiveness of the proposed method.
strength_and_weaknesses: The main strength of the paper is that it is relatively easy to follow and understand the high-level idea.

Weaknesses:
- The topic of task-incremental learning is not particular interesting, given its proven simplicity in prior work.
- The core idea of the work is incremental: using importance mask to condition the model is common in architecture-based methods.
- The empirical evaluation is questionable. For example, in Table 2, all methods in the table has very small standard deviation. I do not think it corresponds with reported results in prior work.
clarity,_quality,_novelty_and_reproducibility: The clarity of the work is acceptable.
The overall quality of the work does not meet the standard of ICLR.
The novelty is incremental.
The reproducibility is questionable.
summary_of_the_review: The overall quality of the work does not meet the standard of ICLR, given the limited novelty and simplicity of the topic. Substantial amount of work is required to improve the paper. Therefore, I would recommend rejection.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The paper introduces a novel task-incremental approach that diminishes the gradients based on their importance (SPG). 
Specifically, the importance is calculated as the normalized gradients on the current and past tasks.
For the current task, the gradients are taken w.r.t. the log-likelihood of the data.
For past tasks, the logits' gradients are used instead.

An extensive empirical study shows that SPG is on average superior than task-incremental baselines.
strength_and_weaknesses: **Strengths**

The method is simple and sensible.
The empirical study is extensive and appropriate.
My intuition is that the reported results would generalize to most task-incremental scenarios.

**Weaknesses**

My main concern is about the studied scenario.
My opinion is that task-incremental learning isn't aligned with practical use-cases and over-studied.
I understand that it's a useful setting to study catastrophic forgetting.
However, we are seeing increasing evidence that catastrophic forgetting isn't as catastrophic as previously thought [1], especially in task-incremental scenarios where one does not have to deal with the problem of calibrating a single-head on all tasks.

Furthermore, most literature in this space often get away with omitting the replay baseline, which is challenging to outperform, at least when computational resources are correctly fixed throughout the methods.

Now, I do think that in the realm of task-incremental papers, SPG is a good one.
I'm willing to increase my score if the SPG survives the introduction of the following baselines:
- replay where all past data is allowed (memory is cheap, so there's no need for artificially constraining to small buffers).
    - try different current vs replay task balances
- this one is really important: copy the network at the end of each task, and only finetune the copy
    - this method is simple, doesn't incur forgetting, should allow for some forward transfer, and has the same computational requirements as other baselines.


[1] Pretrained Language Model in Continual Learning: A Comparative Study

clarity,_quality,_novelty_and_reproducibility: good on all accounts.
The English can be improved however.
summary_of_the_review: Simple and sensible approach.

Because of the weaknesses of the studied scenarios, the authors need some particular new baselines (see **weaknesses**)

---- POST REBUTTAL ------

I updated my score from a 5 to 6 in light of the added baselines.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: Authors have proposed a new SPG method for task-incremental learning. Specifically, SPG uses soft-masks to condition the full model for each task. It conducts experiments to demonstrate the effectiveness of the proposed method on benchmark datasets .

The novelty of this paper is limited as using importance mask to condition the model is common in architecture-based methods.
justification_for_why_not_higher_score: Not so novel and some experiment results are questionable.
Decision: Reject