Paper: Elastic Feature Consolidation For Cold Start Exemplar-Free Incremental Learning (ICLR-2024)

Reviewer comment (Writing):
The reviewer is unconvinced with the so-called definition of the "cold start" scenario in the present paper. In the definition provided by the author in the Introduction: "In this paper we consider EFCIL in the more challenging Cold Start scenario in which the first task is insufficiently large to learn a high-quality backbone and methods must be plastic and adapt their backbone to new tasks."

Author response:
The reviewer is correct in pointing out that the Cold Start scenario we consider is a standard protocol for *exemplar-based* methods. However, for *exemplar-free* methods, which we study exclusively in this paper, this Cold Start scenario is not used and instead a Warm Start consisting of a *large first task* is used [D, E, F, G]. For exemplar-free methods backbone drift leads to catastrophic forgetting, and therefore methods allow very little plasticity in the backbone during incremental learning. Our paper is *one of the first to consider Cold Start scenarios for exemplar-free class-incremental learning*.

[D] Prototype augmentation and self-supervision for incremental learning (CVPR 2021), Zhu et al.

[E] Fetril: Feature translation for exemplar-free class-incremental learning (WACV 2023), Petit et al.

[F] Bring evanescent representations to life in lifelong class incremental learning (CVPR 2022), Toldo et al.

[G] Self-sustaining representation expansion for non-exemplar class-incremental learning (CVPR 2022), Zhu et al.

Reviewer comment (Experiments):
The term "insufficiently large" is unclear and not quantified. The reviewer notes that the author uniformly distributes all classes among all tasks in the experiments (e.g., 10 or 5 classes learned in step 1 for CIFAR-100), which is a standard experimental protocol in existing CL literature. The reviewer questions how the author can confirm that 10 classes cannot be considered "sufficiently large" and what explicit criteria are used to determine if a task is "sufficient" or "insufficiently" large.

Author response:
The reviewer is correct in pointing out that the Cold Start scenario we consider is a standard protocol for *exemplar-based* methods. However, for *exemplar-free* methods, which we study exclusively in this paper, this Cold Start scenario is not used and instead a Warm Start consisting of a *large first task* is used [D, E, F, G]. For exemplar-free methods backbone drift leads to catastrophic forgetting, and therefore methods allow very little plasticity in the backbone during incremental learning. Our paper is *one of the first to consider Cold Start scenarios for exemplar-free class-incremental learning*.

[D] Prototype augmentation and self-supervision for incremental learning (CVPR 2021), Zhu et al.

[E] Fetril: Feature translation for exemplar-free class-incremental learning (WACV 2023), Petit et al.

[F] Bring evanescent representations to life in lifelong class incremental learning (CVPR 2022), Toldo et al.

[G] Self-sustaining representation expansion for non-exemplar class-incremental learning (CVPR 2022), Zhu et al.

Reviewer comment (Theory):
The criteria for determining whether the backbone is of "high quality" are unclear. The reviewer questions whether "quality" refers to performance on current tasks or future tasks, and why a model would need more plasticity for learning new tasks if it is not of high quality. The reviewer also argues that the model could already have high plasticity for learning new tasks given it has only learned 5–10 classes, especially considering the overparameterization of modern CNN/DNNs.

Author response:
Given the feedback of the reviewer, we think it is better to adopt a more practical definition of Cold Start. A Cold Start scenario for a dataset is when *training the backbone on the first task it is of considerably lower quality than when training on half of the dataset*. We performed an experiment on CIFAR-100 to quantify this by incrementally training a ResNet-18 backbone and testing it via linear probing (i.e. training a linear classifier with a cross-entropy loss) on an independent set of 50 classes. The results of this are given in the following table.

**Linear Probing (CIFAR-100)**:
| # of Base Classes | Linear Probe Accuracy (%) |
|----| ---- |
|10  | 49.8 |
|20  | 59.7 |
|30  | 62.0 |
|40  | 64.8 |
|50  | 67.9 | 

The backbone trained on 50 classes outperforms the one trained on 10 classes by more than 18%. In any incremental learning scenario, the backbone starting from only 10 classes will require more plasticity to achieve high incremental learning accuracy. Freezing the backbone (e.g. FeTrIL) or feature distillation do not leave enough plasticity in learning to acquire and perform well on new tasks. 

We feel that it is important to distinguish model *capacity* from learning *plasticity*. When a model is trained on a small first task it is true that the model retains a large *capacity* to learn new tasks, however without adequate *plasticity* when learning new tasks -- which is a property of the *learning method* and not only of the *model* itself -- this capacity cannot be effectively exploited because the backbone is not allowed the plasticity needed to exploit its available capacity.

Our experimental results, ablations, and analyses we provide show that Elastic Feature Consolidation is very effective at manging this additional plasticity needed to acquire new tasks in both Cold Start and Warm Start scenarios. We feel that this is an important and novel contribution since, until now, the emphasis has been almost entirely focused on Warm Start scenarios for exemplar-free continual learning -- scenarios in which backbone plasticity is significantly less important.

Reviewer comment (Novelty):
The overall definition and motivation for the "cold-start" scenario are not solid. The reviewer argues the paper could still stand out without mentioning the "cold-start" scenario by simply discussing the strong regularization effect of existing methods and the need for more flexible regularization. The necessity of introducing the "cold-start" scenario is thus questioned.

Author response:
It is important to distinguish Cold from Warm Start scenarios precisely because that is a characteristic that distinguishes scenarios requiring plasticity in learning (and thus effective and targeted regularization) from those that do not. Our aim is to give names to these two scenarios and to emphasize the importance of treating them differently.

Reviewer comment (Writing):
The term "cold start" in machine learning conventionally refers to situations with no data to start with (e.g., in recommendation systems). Given that 5–10 classes still means thousands of images, the reviewer argues this can hardly be considered a "cold" start.

Author response:
We recognize the point the reviewer makes, but at the same time believe that our experimental comparison with the state-of-the-art in our Cold Start scenarios illustrates that the two types of scenarios indeed need to be handled quite differently in class-incremental learning. Moreover, from a terminological point of view it is very useful to distinguish scenarios beginning with a first task significantly larger than the following tasks (Warm Start) from those that do not (Cold Start).

These points raised by the reviewer have stimulated much discussion and we recognize that the issue is more nuanced than our treatment in the original submission makes it seem. We will integrate elements of this discussion in the final version of the paper in order to stimulate further discussion on the relationship between model capacity and plasticity in incremental learning, and Warm versus Cold starts for class-incremental learning.

Reviewer comment (Experiments):
The details of Figure 2 are missing. The reviewer asks what dataset and experimental protocols were used, whether conclusions are influenced by the number of classes, and whether the number of training samples in the initial task impacts the conclusion. The reviewer requests mean and standard deviations, and repeated experiments to support the empirical observation.

Author response:
We agree that some details were missing and thank the reviewer for pointing this out. This analysis is, however, independent of the setting employed (in the paper we use CIFAR-100 - Warm Start - 5 step). To make this more clear we ran our analysis again considering the following settings: CIFAR-100 (WS) - 10 Step, CIFAR-100 (CS) - 10 Step (see the new Appendix J in the updated manuscript). Our findings are confirmed in both scenarios. Indeed, the perturbation in significant directions changes the posterior probability of the model in a significant way (in this case the first task), and the matrix $E_3$ captures all important directions in feature space up through task 3. This demonstrates the independence of our claim from the first task dimension and the number of classes in each incremental step.

To illustrate robustness across different seeds, we also show the average accuracy drop when adding Gaussian perturbations along principal and non-principal directions (separately) with respect to a regular class incremental learning scenario up to the task 3 (also in the new Appendex K, Figure A8). This plot reveals that perturbations along non-principal directions have no impact on accuracy (indicated by three dashed, overlapped lines on the $x$-axis), while perturbations along the significant direction consistently degrade the performance of our models.

Reviewer comment (Experiments):
Lack of experiments on Imagenet-1k dataset.

Author response:
We performed single runs of 10-Step Cold Start and Warm Start scenarios for EFC on ImageNet-1K and compare with FeTrIL, the closest to ours in terms of performance on the other scenarios. Given the limited time and our limited computational resources, it was not possible to run other baselines on ImageNet-1K at this time.

The following tables give the results for both scenarios and metrics:

**ImageNet-1K 10-step Cold Start**:
|  | $A^K_{step}$ | $A^K_{inc}$|
|---|---|---|
FeTrIL | 33.86 | 48.46
EFC | **42.35** | **56.14**

**ImageNet-1K 10-step Warm Start**:
|  | $A^K_{step}$ | $A^K_{inc}$|
|---|---|---|
FeTrIL | 54.93 | 63.12
EFC | **59.34** | **66.36**

For these experiments we used the same hyperparameters and training protocol as on ImageNet-Subset (see Appendix E of the Supplementary Material). Even on the much larger ImageNet-1K dataset, these results show that in both Warm and Cold Start scenarios EFC is still very effective at maintaining plasticity throughout training. EFC is more effective at learning new tasks compared to FeTrIL, which represents the current state-of-the-art in exemplar-free class-incremental learning.

We are continuing to run additional baselines for these scenarios and will include these new results in the final version.

Reviewer comment (Experiments):
I would prefer to see ablations for more than just one dataset. In particular, in Fig 4, only CIFAR-100 is seen, in Table 2, only TinyImageNet. Figure 4(b) tells me that the EFM+PR-ACE loss is enough by itself (i.e. without prototype rehearsal) for better results than comparison methods on cold startup. But is this also true for the other datasets?

Author response:
The complete version of Table 2 was already in Table A3 of the Supplementary Material. We have added a new Appendix J that provides the requested ablations on ImageNet-Subset and TinyImageNet. We see that incorporating prototype updates in certain cases in the Cold Start Scenario allows us to surpass the performance of existing methods, something that does not always happen when using fixed prototypes. For example, FeTrIL (CS) Tiny-ImageNet - 20 step reaches 25.70% in accuracy compared to only 25.12% achieved by EFC with fixed prototypes.

Reviewer comment (Writing):
Recently some of the community have started to question the frequent assumption that we need CL methods because storage of data is expensive, or unavailable due to privacy. See for example https://arxiv.org/abs/2305.09253. I understand there's very little space to discuss the arguments for and against the importance of "exemplar free" but it would be better to at least acknowledge that there's some debate on this.

Author response:
We agree and have also noticed the debate heating up on storage versus privacy versus computational costs in class-incremental learning [C].We will add a discussion of this into the related work section.

[C] Computationally Budgeted Continual Learning: What Does Matter? (CVPR 2023), Prabhu et al.

Reviewer comment (Experiments):
This paper, and many others like it, seek a CL method that trains a network from a random initial state. However, in practical industry settings, it's nearly always better to consider transfer learning, which for an image classifier has for a long time meant starting with a model trained on ImageNet, but nowadays is also meaning self-supervised pre-training. It's puzzling why the CL community ignore this for the most part. This might be changing now, with a bunch of papers in 2023 now doing CIL with pretrained ViT models (and also ResNets - e.g. your cited ref Panos et al). It could be a simple experiment to try this and assess the benefits of using your method in this different assumption. I am raising this to highlight a potential opportunity, and to recommend you at least mention your assumption of the need to train a model from scratch rather than use pre-trained weights.

Author response:
We also agree with this point -- in fact, we always felt that Warm Start scenarios for exemplar-free CIL are a sort of simulation of starting from a pre-trained backbone. We ran an experiment starting from a backbone pre-trained on ImageNet-1K and then we apply EFCIL methods  on CIFAR-100, splitting the dataset into 20 equal splits. The table below gives these result (averaged over 5 runs).

**CIFAR-100 Equal Split 20-step (from pretrained backbone)**:
| Method | $A^K_{step}$ | $A^K_{inc}$|
|---|---|---|
LwF    | $22.48	 \pm 1.47$    |  $48.30\pm1.72$
SSRE   | $33.66 \pm  2.30$    | $46.86 \pm 2.33$
FeTrIL | $48.22	 \pm  0.47$   |  $62.30 \pm 0.91$ 
EFC    | $\textbf{56.62}  \pm  0.30$   |   $\textbf{68.81} \pm   0.85$

These results show that, even when starting from a strong backbone, the additional plasticity gained by EFC yields significant improvements over FeTrIL (which freezes the backbone).

Reviewer comment (Experiments):
Do ablations on all datasets show the same trend, i.e. that the EFM+PR-ACE loss is enough to surpass comparison methods by itself, and adding prototype rehearsal to this creates a further boost?

Author response:
See our comments on the first point above (and the new Appendix J). For all three datasets each component yields a noticeable boost in performance.

Reviewer comment (Presentation):
I found it very difficult to read the tiny font in Figure 2 and Figure 4(a), so please can you find a way to make those more easily readable?

Author response:
We increased the fonts size a little in these two figures and will make more refinements when preparing the final version.

Reviewer comment (Theory):
In this method, each task is divided into disjoint classes, and the class boundaries are known for each task. However, can this method be extended to cases where the task boundaries are unknown for each task?

Author response:
1-Our Elastic Feature Consolidation (EFC) is designed for the *offline* class-incremental learning in which the full dataset is divided into multiple tasks and multiple epochs are performed during each training session. In this scenario *offline* implicitly assumes knowledge of task boundaries at training time, since whenever a new set of data arrives it is interpreted as a new incoming task. During inference however, the task boundary is not available. For EFC, knowledge of task boundaries is essential both to compute the Empirical Feature Matrix and to save the "previous model" to be used in the regularization term.

To the best of our knowledge the existing literature on offline continual learning rarely addresses scenarios involving class overlap incremental sessions. A similar scenario was recently explored in [A] in which overlapping occurs between classes and each training session may include either new examples or a combination of old and new classes. However, even this setting requires task boundaries for transitioning from one session to the next. In theory, EFC could be applied to this benchmark and we find the idea of applying our method in such scenarios to be intriguing.

Task-free incremental learning [B], in which there are no task boundaries for either training or inference, is more commonly addressed in online continual learning (which considers a single pass over all data). Adapting our method to this context is not trivial, as EFC requires multiple forward passes through the network (for the EFM computation at the end of training). We think that adapting EFC for task-free online settings, although non-trivial, might be possible using moving averages of models used to compute the EFM, and we may consider this possibility for future work.

[A] General Incremental Learning with Domain-aware Categorical Representations (CVPR22), Xie et al.

[B] Task-Free Continual Learning (CVPR19), Aljundi et al.

Reviewer comment (Writing):
This method calculates the Empirical Feature Matrix (EFM), which determines the significant feature direction from a prior task, the mechanism of which remains unclear. Could you please provide a comprehensive explanation for better comprehension?

Author response:
2-Computing the EFM after each training session uses the training images of the current task as input to the network. Its computation involves two calculations:
* The *local feature matrix* per sample (Eq.5, Eq.7), computed using the derivative of log output probabilities wrt features.
* The expectation of the local feature matrices over the current train set (Eq.6).

The Empirical Feature Matrix (EFM) identifies feature directions important to prior tasks. Consider the EFM $E_t$, computed after training task $t$, and its eigenvector decomposition:
$$
E_t = U_t^T \Lambda_t U_t,
$$
where $U_t, \Lambda_t$ represents the eigenvector and eigenvalues of $E_t$. The eigenvectors with non-zero eigenvalues represent the *principal directions* for the prior tasks. By construction, applying perturbations in these directions the performance of the previous-task classifiers degrade (Fig.2). Conversely, the *non-principal* directions are associated with zero eigenvalues and perturbing along them does not have any impact on model output. This behavior is justified by its interpretation in terms of KL-divergence (Eq.8).

We exploit this property by using the EFM into the regularization loss (Eq. 9).  Let's focus on the term in which it appears and consider its spectral decomposition. Using the paper notation and defining $\delta(x)=f_t(x) - f_{t-1}(x)$ to denote the feature drift for $x \in \mathcal{X}_t$, we can express the regularization term as:

$$
\delta(x)^T  E_{t-1} \delta(x) = (U_{t-1} \delta(x))^T  \Lambda_{t-1} (U_{t-1} \delta(x))
$$

The plasticity in training is given by the diagonal matrix containing the eigenvalues of the EFM. In particular, we regularize feature drift in principal directions of the EFM with a strength determined by its eigenvalues.

Reviewer comment (Evaluation):
In Table 2, the results for the cold start issue demonstrate undesirable outcomes. Could you please provide an explanation in the main paper to justify this?

Author response:
3-The results in Table 2 refer to the memory-efficient variants of our method. In the Warm Start scenario, storing one covariance per class has a significant impact since the covariances remain fixed. With respect to the Cold start(CS) scenario in this specific setting, the feature drift is mitigated by the fact that we learn half of all the classes during the first task. On the contrary, in CS, the representations are more prone to changes, and having per-class fixed covariances is less crucial. Consequently, the differences in CS Scenario between the variants of our method are practically negligible. Theoretically, both scenarios, especially CS, could be improved if we design a method to update these matrices during the incremental steps. However, as we highlight in the limitations paragraph, this remains an open point.