Paper: Classifiers are Forgetful! Balancing the Mutual Causal Effects in Class-Incremental Learning (ICLR-2024)

Reviewer comment (Experiments):
The selected datasets contain limited number of classes. For class-incremental learning, it's recommended to utilize datasets with a broader range of classes, such as ImageNet.

Author response:
The revised manuscript adds more experiments on large-scale datasets. We further evaluate BaCE on these large-scale datasets: OmniBenchmark, ImageNet-R, ObjectNet, and Tiny-ImageNet, which contain 300, 200, 200, and 200 classes. The dataset statistics are summarized in Table 5. The experimental results are provided in **"Evaluation on Challenging Datasets."** and **"Evaluation on DeiT-S/16 with Less Information Overlapping."** of Appendix D.1.

Please also refer to **"Improve 1: More experiments on the datasets with less information overlapping and the datasets with a larger number of classes"** in the global response due to the space limitation.

Reviewer comment (Novelty):
The data imbalance problem in continual learning is not new. It is weird that the related work on dealing with data imbalance is put in the appendix. Your method of using KD to prevent forgetting is not new.

Author response:
We want to clarify that the confrontational phenomenon (i.e., causal imbalance problem) is different from the class (data) imbalance problem. Both problems indeed come from the imbalance between new and old data. However, the old data is always unavailable or inadequate in the IL scenario. Otherwise, IL is converted into Multi-Task Learning (MTL). The key difference between the class imbalance problem and the causal imbalance problem is that the former only focuses on the **scale of logits** while the latter focuses on the **learning process** of both new and old features and embeddings. We provide more experimental results in the revised manuscript. Please refer to **"Improve 2: More analysis about the causal imbalance problem and the class imbalance problem"** in the global response due to the space limitation.

Furthermore, knowledge distillation (KD) is indeed a common technique in various machine learning problems. In the area of IL, various methods such as BiC, LUCIR, DER++, and CLSER are inspired by the thought of KD. However, how and what to distillate is also an important research question. In this research, we focus on the underexplored causal imbalance problem.

Reviewer comment (Experiments):
My complaints are about the experiments. First of all, the pre-training data ImageNet 21k already contains most of the classes in cifar100 and 5-datasets, which means that those classes have been learned during pre-training, which is information leak. In continual learning, they aren’t new anymore. That is why less forgetting occurs. The proper way of conducting the experiments is to pre-train using only the data in ImageNet21k with those intersecting classes removed, like this paper: Kim et al. Learnability and Algorithm for Continual Learning. Proceedings of ICML, 2023.

Author response:
Indeed, CIFAR100 has information overlapping with PTMs, and we have discussed it in **"Discussion of the Overlap between Pretraining and Downstream Datasets."** of Appendix D.1 in the original manuscript. The 5-datasets have less information overlapping with PTMs. As mentioned in **"Datasets."** of Appendix D.1, 5-datasets contain samples such as 10 different letters (A-J) and fashion images, which are rare in ImageNet. Some examples can be found in Figure 19 (b).

Thanks for providing the reference [R2-1]. We follow the same experimental setting as in [R2-1] and further evaluate BaCE on Tiny-ImageNet, CIFAR100, and CIFAR10 in the revised manuscript. Furthermore, we also evaluate BaCE on four more challenging datasets: OmniBenchmark, ImageNet-R, ObjectNet, and VTAB. These datasets contain challenging samples that ImageNet pre-trained models cannot handle. Please refer to **"Improve 1: More experiments on the datasets with less information overlapping and the datasets with a larger number of classes"** in the global response due to the space limitation.

Reviewer comment (Experiments):
It is unfair to compare your model employing a strong pre-trained network with many baselines that don’t use pre-trained network.

Author response:
For all experiments in our paper, we fairly compare BaCE with all other baselines using exactly the same pre-trained checkpoint. We provide the links to these checkpoints in Table 6 and add more descriptions in the revised manuscript to avoid misunderstanding.

Reviewer comment (Evaluation):
When comparing your Table 1 and Table 1 in the L2P paper, I found that when L2P performs poorer in a setting than your system, you copied the result from L2P using it as a baseline, but when L2P performs better than your system, you didn't.

Author response:
We are not intent to hide the result of L2P [R2-2]. The reason why we do not compare L2P when the buffer size is zero is given in **"Baselines."** of Appendix D.1 in the original manuscript. 

From our perspective, **the prompt-based methods such as [R2-2, R2-3] primarily leverage the pretrained knowledge of PTMs instead of learning new knowledge incrementally** because the whole PTMs are frozen. As shown in Table 1 of the L2P paper, L2P does not benefit much from the data replay compared to other methods (with or without replay data). 
In contrast, BaCE and other distillation-based methods (e.g., DER++) finetune the whole PTMs and suffer from catastrophic forgetting more seriously, especially when the buffer size is limited.
As shown in Table 8,9, the distillation-based methods greatly benefit from replay data, while L2P does not.
Furthermore, the distillation-based methods, including BaCE, require no additional model components while L2P requires additionally 10$\times$5$\times$768=38.4K and 20$\times$5$\times$768=76.8K parameters for CIFAR100 and 5-datasets respectively.
Therefore, we believe that these two types of methods are not comparable.

Reviewer comment (Evaluation):
What does average accuracy mean, average incremental accuracy or average final/last accuracy computed after the last task is learned?

Author response:
The average accuracy in this paper means that the average last accuracy is computed after the last task is learned. We clarified this in the revised manuscript.

Reviewer comment (Experiments):
Too few datasets are used in the experiments.

Author response:
Indeed, CIFAR100 has information overlapping with PTMs, and we have discussed it in **"Discussion of the Overlap between Pretraining and Downstream Datasets."** of Appendix D.1 in the original manuscript. The 5-datasets have less information overlapping with PTMs. As mentioned in **"Datasets."** of Appendix D.1, 5-datasets contain samples such as 10 different letters (A-J) and fashion images, which are rare in ImageNet. Some examples can be found in Figure 19 (b).

Thanks for providing the reference [R2-1]. We follow the same experimental setting as in [R2-1] and further evaluate BaCE on Tiny-ImageNet, CIFAR100, and CIFAR10 in the revised manuscript. Furthermore, we also evaluate BaCE on four more challenging datasets: OmniBenchmark, ImageNet-R, ObjectNet, and VTAB. These datasets contain challenging samples that ImageNet pre-trained models cannot handle. Please refer to **"Improve 1: More experiments on the datasets with less information overlapping and the datasets with a larger number of classes"** in the global response due to the space limitation.

Reviewer comment (Writing):
Many parts of the paper are hard to read.

Author response:
We adjusted the format of Table 1 and the size of Table 8, 9, 13, 16, 19, Figure 12, 13, 14, 19, 20, and 21 to improve the readability.

Reviewer comment (Novelty):
First, the motivation behind constructing the causal graph is similar to [1]. [1] also point out that the path from old samples to intermediate feature can occur the interference, and they also try to resolve this issue. Though figuring out the confrontation phenomenon in CIL may be novel, constructing the causal graph as [1] is quite similar.

Author response:
The differences between BaCE and DDE [1] have been shown in Appendix C.4 and A.5 in the original version of the manuscript. The causal graph of BaCE contains two subgraphs: one for Effect_{new} and another for Effect_{old}. The subgraph of Effect_{new}is motivated by the causal graph in [1]. However, the motivation behind the causal graph between this research and [1] is different. The subgraph of Effect_{new} in this research is proposed for balancing causal effects when adapting to new classes, while the causal graph in [1] is proposed for retrieving the missing old data effects. Motivated by the issue of causal imbalance, we provide an in-depth analysis of the causalities behind the new classes' logits in Appendix C.3 and define Effect_{new} in Eq (3)(4), which is different from [1].

Furthermore, different from the collider effect in DDE, Effect_{new} estimates the weight of neighbours in Appendix C.3 and thus fundamentally works better than DDE. The experimental result in **"The effect of KNNs in Effect_{new}."** of Appendix D.1 validates the effectiveness of BaCE.

Reviewer comment (Experiments):
Since the paper focuses on establishing the confrontation phenomenon, but does not provide a method that fully addresses it, the lack of more discussion of the limitations makes it weaker. The point of the paper seems to be juggling between establishing the phenomenon, but not delving as much into it as it could (e.g. more experiments that give insight into what is described in Sec. 4.2), and performing better than the state-of-the-art, but not necessarily tackling what the proposed method actually solves in comparison with other similar methods (e.g. where does the performance gain actually come from, is the PTM a huge effect). Some of the interesting insight is relegated to a brief description in the appendix.

Author response:
**Response to W1**: We add more in-depth experiments from the following aspects:

- Exploring the effect of KNNs in BaCE in **“The effect of KNNs in Effect_{new}.”** of Appendix D.1. The results further explain that the KNNs act as a constraint from the teacher model and thus build causal paths from both new and old data when the student models adapt to new classes.
- Evaluating BaCE on DeiT-S/16 with less information overlapping. The experimental results in **“Evaluation on DeiT-S/16 with Less Information Overlapping.”** of Appendix D.1 shows the superior performance of BaCE.
- Exploring the feature embedding distance and the probing performance with BiC, IL2M, LUCIR, DER++, and CLSER on four challenging datasets in **“The difference between causal imbalance problem and class imbalance problem.”** of Appendix D.1. Additionally, the confusion matrices of BiC, IL2M, LUCIR and BaCE are provided. 
- Evaluating BaCE on four more datasets: OmniBenchmark, ImageNet-R, ObjectNet, and VTAB. They contain challenging samples and multiple complex realms that ImageNet pre-trained models cannot handle. The experimental results in **“Evaluation on Challenging Datasets.”** of Appendix D.1 shows that BaCE outperforms competitive baselines such as DER++ and CLSER.

Reviewer comment (Presentation):
Table 1 is split into two parts and very reduced. Needs to be fixed and more readable. The same goes for most figures, where everything is too squeezed down. Formatting needs to be readable and fixed.

Author response:
**Response to W3**: We adjust the format of Table 1 and the size of Table 8, 9, 13, 16, 19, Figure 12, 13, 14, 19, 20, and 21 to improve the readability.

Reviewer comment (Evaluation):
Results in Table 8 of the appendix are a limitation that should be mentioned in the main manuscript. The proposed method can potentially be between 2x to 5x computationally more expensive during training.

Author response:
**Response to W4**: We added the limitation in the conclusion of the main manuscript.

Reviewer comment (Experiments):
The compared methods for when the buffer is empty are quite limited since EWC and LwF are very classic approaches. There is no comparison with more recent rehearsal-free methods, for example: 
- Maintaining Discrimination and Fairness in Class Incremental Learning, CVPR 2020  
- Prototype Augmentation and Self-Supervision for Incremental Learning, CVPR 2021  
- Class-Incremental Learning via Dual Augmentation, NeurIPS 2021  
- Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning, CVPR 2022  
- FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning, WACV 2023

Author response:
**Response to Q1**: Thanks for providing more comparable baselines when the buffer is empty. We will explore their performance in the future. In the current form of the manuscript, we did not compare with them for two reasons:

- The exemplar-based methods achieve superior performance in even challenging scenarios. We compare BaCE with exemplar-based methods such as BiC, LUCIR, IL2M, PODNET, DDE, DER++, Gdumb, CLSER, FOSTER, MEMO, BEEF to show the effectiveness of BaCE.

- The causal graphs show that the confrontational phenomenon still exists (i.e., imbalance causal effects) in the REPLAY setting. We validate the effectiveness of addressing imbalance causal effects by improving upon REPLAY.

Besides, one mentioned method (PASS)[R1-1] is compared in Table 10 of the revised manuscript.

[R1-1] Prototype Augmentation and Self-Supervision for Incremental Learning (CVPR2021)

Reviewer comment (Experiments):
When the PTM trained on ImageNet-21k is used for the CIFAR-100 or the 5-datasets, the fairness of this comparison is questionable. From the ablation in Table 2, it looks as if just freezing parts of the model with the PTM weights is rather competitive with most methods.

Author response:
**Response to Q2**: We use the same PTMs for all compared baselines and BaCE. Furthermore, we add more experiments on scenarios with less information overlapping. Please also refer to **"Improve 1: More experiments on the datasets with less information overlapping and the datasets with a larger number of classes"** in the global response due to the space limitation.

The IL performance on downstream datasets is indeed positively correlated with the power of PTMs. Recently, many prompt-based methods, such as L2P [R1-2] and Progressive Prompt [R1-3], have been proposed. However, these partially fine-tuning methods (including the frozen baselines in Table 3) primarily leverage the pretrained knowledge of PTMs instead of learning new knowledge incrementally. As shown in Table 8, L2P does not benefit much from the data replay compared to other methods. In other words, partially fine-tuning methods have superior performance, especially when the replay data is limited. In contrast, BaCE and other distillation-based methods can be applied to more IL scenarios with different backbones, datasets, and buffer sizes.

[R1-2] Learning to Prompt for Continual Learning (CVPR2022)

[R1-3] Progressive Prompts: Continual Learning for Language Models (ICLR2023)