Paper: Class Incremental Learning via Likelihood Ratio Based Task Prediction (ICLR-2024)

Reviewer comment (Writing):
The writing requires improvement. The author frequently used abbreviations and jargon, especially in the introduction, which occasionally left me puzzled. It would be beneficial if these terms were interpreted more straightforwardly.

Author response:
Thanks for your suggestion. We have significantly reduced the use of abbreviations and jargon to make the introduction section easier to read and to interpret. Since we edited in many places, we did not highlight the changes. You may check out the new introduction section in the revised paper.

Reviewer comment (Experiments):
Although the author clarifies their focus on Class incremental learning, which doesn't provide the task-id during inference, it remains ambiguous whether they are using a memory buffer (rehearsal-based) or are memory-free (online CIL). I suggest the author address this in the introduction and related works.

Author response:
Yes, we are using a memory buffer to save some previous data as in replay-based methods but the saved data in our method are used to estimate the distribution of U_{CIL} in training each task and also in testing rather than to replay them in training a new task like replay-based methods. We are not doing online CIL. We have added footnote 3 to make these clearer.

Reviewer comment (Experiments):
Some recent benchmarks are missing: The author left memory-free (non-replay-based approaches) CIL in related works. The author also left balanced CIL works, e.g., SS-IL, TKIL.

Author response:
Many systems in the related work section are memory-free methods, e.g., those regularization-based methods, orthogonal-projection based methods, and some of those task-id prediction based methods. 

As a large number of papers have been published in continual learning, there are inevitable casualties in literature review. Thanks for pointing out the balanced CIL work. We have added it and cited both SS-IL and TKIL papers in the related work section. Please see the last sentence (in blue) on page 2 of the revised paper.

Reviewer comment (Experiments):
Table 1 is impressive, but the comparisons seem biased. The author claims they compared with 17 baselines, including 11 replay-based and 6 non-replay-based. From my understanding, the author requires a memory buffer, as indicated in the "Overview of the Proposed TPLR", equation 2.

Author response:
We also think that our results are very strong. Thanks. We have some difficulty understanding your comment “the comparisons seem biased”. Could you explain more? Yes, we use a memory buffer to save some past data like replay based methods but we have compared with 11 strong replay-based baselines. We also include 6 non-replay based methods as some non-replay based methods are also strong, e.g., PASS and SLDA.

Reviewer comment (Experiments):
It would be more equitable if the author juxtaposed their method with replay-based CIL. Specifically, the author should draw a clear comparison with methods using task-id prediction, highlighting the advantages of their technique.

Author response:
Yes, we agree with you to juxtapose our method with replay-based CIL, and we have included 11 strong replay-based baselines in Table 1. Besides, the best baselines MORE and ROW also use task-id prediction. The results show that our method TPLR outperforms them by a large margin. We did not include some earlier task-id prediction based CIL methods (but they have been reviewed in the related work section) as they are much weaker than MORE and ROW. Below, we report the results of two more task-id prediction based CIL methods, iTAML [1] and PR-Ent [2], based on the same pre-trained model. Notice that the vanilla iTAML assumes each test batch of many instances comes from a single task and uses the whole batch of data to predict the task-id. We set its test batch size as 1 to disable the assumption for a fair comparison as all other methods predict one test instance at a time, which is more realistic.

|        | C10-5T    | C100-10T  | C100-20T  | T-5T      | T-10T     | Average   |
| ------ | --------- | --------- | --------- | --------- | --------- | --------- |
| iTAML  | 78.65     | 59.56     | 52.31     | 49.53     | 46.34     | 57.28     |
| PR-Ent | 84.37     | 64.51     | 60.52     | 58.63     | 55.44     | 64.69     |
| TPLR   | **92.33** | **76.53** | **76.34** | **68.64** | **67.20** | **76.21** |

Reviewer comment (Experiments):
One important baseline is missing: AFC[3].

Author response:
We’ve added AFC as a new baseline in Table 1 and cited the paper as well. A summary of the results (accuracy after learning the final task) is presented in the following table. It can be seen that our TPLR also outperforms AFC by a large margin.. 

|      | C10-5T    | C100-10T  | C100-20T  | T-5T      | T-10T     | Average   |
| ---- | --------- | --------- | --------- | --------- | --------- | --------- |
| AFC  | 84.59     | 69.85     | 69.44     | 56.40     | 55.35     | 67.13     |
| TPLR | **92.33** | **76.53** | **76.34** | **68.64** | **67.20** | **76.21** |

Reviewer comment (Experiments):
The inference setting remains unclear. Does the author predict both the task-id and class-id simultaneously? Is there any fine-tuning step involved? Typically, some fine-tuning follows the task-id prediction, e.g., iTAML. If the author's method circumvents this, it could be seen as a distinct advantage. Therefore, I recommend the author incorporate a discussion about the computational load when integrating likelihood ratio predictions, elucidating the benefits and drawbacks of this model.

Author response:
Yes, we predict both the task-id and class-id simultaneously, more specifically estimating their probabilities. There is no fine-tuning step involved. As shown on the right part of Figure 1, our inference pipeline allows the simultaneous computation of within-task class prediction (top part) and task-id prediction (bottom part) probabilities. We have discussed the computation costs of our method in Appendix G.3, which shows that the likelihood ratio prediction is very efficient. Overall, the time efficiency is comparable with the best accuracy performing baselines MORE and ROW.

Reviewer comment (Presentation):
Lacks visualizations: Could the author add a real visualization of data distribution, like the "Feature-based likelihood ratio score" in Figure 1? It will be strong evidence the TPLR works well.

Author response:
That’s a good suggestion! We have added the visualization of data distribution in Appendix F. The visualization demonstrates that TPLR’s likelihood ratio is crucial in estimating the task prediction probability. We put the detailed analysis in Appendix F.

Reviewer comment (Writing):
While the writing quality is satisfactory, a friendlier approach to outlining the proposed method is recommended. First, outline the key ingredients, then explain how they interact, and finally cross-reference these with the existing figure.

Author response:
Thank you for the suggestion. We tried to follow a top-down approach to present our method: 

Section 3 presents the motivation and overview of our TPLR method, which consists of training and testing. 

- For training, the key components are our proposed network architecture and HAT for protecting each task model, which corresponds to the left part of Figure 1.
- For testing, there are two key ingredients: within-task prediction and task-id prediction probabilities, which correspond to the top right part and bottom right part of Figure 1.

As estimating task-id prediction probability is the most critical part, we use the whole of Section 4 to introduce the theorem and the design. Also, the design consists of three key ingredients: 

- (1) estimating the likelihood ratio between $P_t$ and $P_{t^c}$ (corresponds to the “Feature-based likelihood ratio score” in Figure 1). 
- (2) combining with a logit-based score (corresponds to “Max logit score” in Figure 1). 
- (3) converting task-id prediction scores to probabilities (corresponds to “softmax” in Figure 1).

Following your suggestions, we have revised the paper (especially Section 3 and Section 4) to improve the clarity and also make it friendlier by adding more cross-references (in blue fonts).

Reviewer comment (Evaluation):
I wonder the proposed methods can achieve high task-prediction accuracy. Different from the ideal situation, the accuracy may be lower than we expected. If the semantics across different classes are similar, the task-prediction accuracy can be low, and the overall performance also can decrease.

Author response:
We are not sure about the first part of your question and “the accuracy we expected.” Our method does not explicitly predict the task, but only estimates the task prediction probability for each test instance. Just for curiosity, we use the estimated task prediction probability to do task prediction by choosing the task with the highest probability. The task prediction accuracy results (after learning the final task) are shown in the table below (we also compare with the best-performing baselines MORE and ROW). Our TPLR consistently outperforms the two strong baselines on task prediction accuracy, which demonstrates the superiority of TPLR over OOD detection-based methods for task prediction in MORE and ROW.

|      | C10-5T | C100-10T | C100-20T | T-5T   | T-10T  | Average |
| ---- | ------ | -------- | -------- | ------ | ------ | ------- |
| MORE | 0.8861 | 0.7076   | 0.6997   | 0.7368 | 0.6835 | 0.7427  |
| ROW  | 0.9131 | 0.7531   | 0.7411   | 0.7382 | 0.6855 | 0.7662  |
| TPLR | **0.9291** | **0.7819**   | **0.7681**   | **0.7891** | **0.7393** | **0.8015**  |

About the second part of your comment, if the semantics across different classes are similar, the accuracy should be lower, which is the case for any supervised learning. In our case, the accuracy will be lower for all CIL baselines.