Prospective Representation Learning for Non-Exemplar Class-Incremental Learning

Authors: wuxuan  shi
mang  ye
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-13 05:12:03

Abstract: Non-exemplar class-incremental learning (NECIL) is a challenging task that requires recognizing both old and new classes without retaining any old class samples. Current works mainly deal with the conflicts between old and new classes retrospectively as a new task comes in. However, the lack of old task data makes balancing old and new classes difficult. Instead, we propose a Prospective Representation Learning (PRL) approach to prepare the model for handling conflicts in advance. In the base phase, we squeeze the embedding distribution of the current classes to reserve space for forward compatibility with future classes. In the incremental phase, we make the new class features away from the saved prototypes of old classes in a latent space while aligning the current embedding space with the latent space when updating the model. Thereby, the new class features are clustered in the reserved space to minimize the shock of the new classes on the former classes. Our approach can help existing NECIL baselines to balance old and new classes in a plug-and-play manner. Extensive experiments on several benchmarks demonstrate that our approach outperforms the state-of-the-art methods.

Main paper: 
 introduction: 
 In recent years, deep neural networks (DNNs) have achieved great success in static scenarios. Research attention is increasingly turning to extending the learning capability of DNNs to open and dynamic environments. An important aspect is to enable the network to accumulate knowledge from new tasks as the input stream is updated (i.e., incremental learning [1; 2; 3]).Whenever a new task arrives, it is costly to retrain the model with current and old data. Not to mention that the old data is not fully available. A typical alternative is to fine-tune the network with new data directly. However, this can lead to drastic performance degradation on previously learned tasks, a phenomenon known as catastrophic forgetting [4; 5] . While storing exemplars of each class is a simple approach to mitigate forgetting, it relies on the quality of saved exemplars and faces challenges in storage and privacy, especially for sensitive domains such as medical imaging. Hence, this paper focuses on coping with catastrophic forgetting during incremental learning without storing any old samples, which is called non-exemplar class-incremental learning (NECIL) [6; 7] .In NECIL, a serious challenge is to discriminate between old and new classes without access to old data. Most methods usually start considering handling conflicts between old and new classes only when new tasks arrive. While some methods use stored prototypes to model the distribution of old classes [8; 6; 9; 10] , others extend the network structure to accommodate new classes [7; 11] . However, in the base phase (i.e., training on the first task), traditional training allows different classes to divide up all the embedding space, causing trouble for subsequent conflict resolution. As shown in Fig. 1 , in the incremental phase (i.e., training on tasks after the first one), with the influx of new classes, there are overlaps of the old and new classes in the embedding space that are difficult to discriminate. Moreover, due to the unavailability of old class samples, handling this conflict with only new task data is intractable. Instead, we suggest addressing this issue by learning prospectively at the feature level, which requires a two-pronged effort in both the base and incremental phases. Firstly, the model should make room in advance for the incoming classes in the future. Thus, the space of past classes does not need to be drastically squeezed when expanding new classes. To this end, during the base phase, we construct a preemptive embedding squeezing constraint to enforce intra-class concentration and inter-class reserved separation. Specifically, we push instances from the same class closer together and instances from different classes farther apart in a mini-batch. It allows more space to be reserved in the initial embedding space, thus making the model ready for future classes.Secondly, the model should minimize the shock and impact of the new classes on the past classes, i.e., embed the new classes into the reserved space as much as possible. However, achieving the desired embedding of new classes when the old class data is fully unavailable is difficult. Inspired by previous works [6; 10] , we try to accomplish this using prototypes (typically the class mean in the deep feature space) saved for each old class. During the incremental phase, we propose a prototype-guided representation update mechanism. Concretely, we use the network learned from previous tasks to extract features from new task samples and project these features and the saved prototypes into a latent space. In the latent space, the new class features are pushed away from the region hosting the old class and embedded as much as possible in the reserved space with the help of prototypes. We guide the update of the current model representation through the latent space to reduce the shock of the new classes on the former classes.In summary, combining the above two ideas, our Prospective Representation Learning (PRL) scheme makes the following main contributions:• We impose a preemptive embedding squeezing constraint to reserve space for future classes by reinforcing intra-class concentration and inter-class reserved separation.• We propose a prototype-guided representation update strategy that utilizes the saved prototypes to reduce the impact of expanding new classes on old ones.• Extensive experiments on four benchmarks suggest the superior performance of our approach over the state-of-the-art. We also provide a detailed analys of our method.2 Related WorkTo facilitate comparison of future work with our method, we provide detailed values of the accuracy curves in Tab. 3, Tab. 4 and Tab. 5, where 'A' represents the CIFAR-100 dataset, 'B' represents the TinyImageNet dataset and 'C' represents the ImageNet-Subset dataset, respectively. Evaluation on Large Datasets and Robustness. To further demonstrate the effectiveness of our method, we evaluated it on a large-scale dataset -ImageNet-1K. For ImageNet-1K, we allocate 500 classes for the base phase and 50 classes for each of the 10 incremental phases. As shown in Table 6 , our method shows an improvement of 1.9% compared to the suboptimal results. The results of our method are obtained by averaging three replicate experiments, and we set a different random seed for each run. To illustrate the stability of our method, we report the standard deviation of these three results. As shown in Tab. 6, the random seed has little impact on the results of our approach. Plug-and-play with other NECIL methods. Existing NECIL methods mainly focus on backwardlooking means of resolving conflicts between old and new classes, which does not contradict our prospective learning. Therefore, we integrate PRL into the existing NECIL methods. Tab. 7 illustrates the performance gains achieved by incorporating PRL in these methods. In the setting of the CIFAR dataset with three different lengths of task sequences, PRL improved accuracy by an average of 2.7% for IL2A [38] and 2.3% for PASS [6] , which demonstrates the good compatibility of our method. 
class-incremental learning: 
 Mainstream CIL methods can be roughly divided into three categories: rehearsal-based methods, regularization-based methods, and structure-based methods.Rehearsal-based methods store a portion of seen data in a fixed-size memory buffer and replay it as new data arrives. Based on the stored data, some works use knowledge distillation techniques to protect existing knowledge [12; 13; 14; 15] , while others regularize the gradient to make more efficient use of the stored samples [16; 17; 18] . Additionally, several works design new strategies for memory management instead of simple random sampling. [19; 20; 21; 22] . Although rehearsal-based methods effectively mitigate catastrophic forgetting, they are encumbered by privacy concerns and become impractical under stringent storage constraints.Regularization-based methods estimate the importance of different parameters for past tasks and then limit the updating of these important parameters when learning new tasks [23; 24; 25; 26] . In incremental learning, the storage of importance weights becomes essential. However, these methods are encumbered by constraints on model parameters, consequently impeding knowledge transfer and leading to suboptimal performance, particularly in long-sequence task streams.Structure-based methods accommodate knowledge from new tasks by dynamically modifying the network structure. Some works extend the network by assigning new parameters of different forms to new tasks [27; 28; 29; 30] . While this approach adeptly manages extended task sequences and sustains the performance of established classes, the linear growth of network parameters with the number of tasks and the necessity for reasoning across multiple forward propagations pose significant challenges. Parameter fusion [31] and selecting partial parameters for expansion [32] mitigates this problem to some extent. An alternative is to mask part of the parameters that are highly correlated with the previous task at the parameter level or unit level [33; 34; 35; 36] . Their performance is limited by the backbone obtained on the first task.Recently, some works begin to focus on NECIL [8; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46] , due to privacy and memory concerns [47; 48; 49] , where the algorithms have no access to any past data. Li et al. [50] combine knowledge distillation with fine-tuning in a first attempt at incremental learning without a memory buffer. Zhu et al. [38] propose class augmentation and semantic augmentation to address the representation bias and classifier bias caused by the lack of old task data. Yin et al. [37] use model inversion technology to generate samples from previous tasks to alleviate forgetting. Based on [37] , Gao et al. [39] introduce relation-guided knowledge distillation to address the distributional gap between generated data and real data.Zhu et al. [6] combat catastrophic forgetting for the first time by preserving prototypes and augmenting them. Yu et al. [8] address the problem of prototype outdating in the current representation space by estimating the semantic drift of past tasks and compensating for it. Furthermore, Toldo et al. [9] subdivide the drift into feature drift and semantic drift and compensate for both, thereby achieving better results. Shi et al. [10] inject information about the current feature distribution into the prototype to model the distribution of past tasks. Wang et al. [51] improve the prototype augmentation method based on density to make the model more focused on features of the old class with low density. Malepathirana et al. [52] use the domain information obtained from topological relations to optimize prototype augmentation to reduce inter-class overlap. However, previous works deal with conflicts between old and new classes only after the new data arrives and lack prospective consideration.Embedding space regularization has been extensively studied in literature [53; 54; 55; 56; 57] . Chaudhry et al. [53] first propose learning tasks in different (low-rank) embedding subspaces that are kept orthogonal to each other. They learn an isometric mapping by formulating network training as an optimization problem on the Stiefel manifold. Another idea is to implement orthogonality in the gradient space. Saha et al. [58] analyze network representations after learning each task with Singular Value Decomposition (SVD) to find the basis of the subspaces and store them in memory. Moreover, several methods promote forward compatibility through regularization in the initial phase. Zhou et al. [59] assign virtual prototypes to compress embeddings of known classes and reserve space for new classes. Shi et al. [60] encourage initial CIL learners to generate representations that are similar to those of models trained jointly on all classes. Compared to previous works, we target CIL in exemplar-free scenarios (NECIL). We consider how to resolve conflicts between new and old classes during the incremental phases, in addition to reserving space in the initial phase. 
problem statement: 
 The goal of NECIL is to continually train a unified model over a series of tasks to recognize all classes learned so far. The data stream can be defined as D = {D 0 , D We adapt the paradigm of existing NECIL works [6; 7; 11; 10] as our baseline, which primarily uses knowledge distillation and prototype rehearsal. Specifically, at the base phase (i.e., t = 0), the classification model is optimized under full supervision:EQUATIONwhere L t represents the overall loss function, L ce is the cross-entropy loss.At the incremental phase (i.e., t > 0), standard fully supervised training seeks to minimize the following objective:L t = L ce (θ t , φ t ; D 0:t-1 ) + L ce (θ t , φ t ; D t ).(2) However, this is especially challenging since previous training sets D 0:t-1 are assumed to be unavailable in the NECIL setting. The absence of the first term in eq. ( 2) leads to a bias in favor of current classes in the feature extractor F θt and the classifier G φt . To address this problem, existing methods [38; 6; 10] adopt knowledge distillation and prototype rehearsal to cope with the bias. Specifically, they take the frozen feature extractor F θt-1 from the previous phase t -1 as a teacher and the current one F θt as a student. A distillation term is introduced to encourage the model to mimic the previous representation:EQUATIONwhere ∥ • ∥ 2 denotes Euclidean distance. Knowledge distillation helps maintain existing knowledge in F θt-1 , thus mitigating bias in the current feature extractor.For the bias in the classifier, we use class-representative prototypes [6] to balance the optimization. Specifically, after the training of t -1 phase, we compute a prototype p c for each class c:EQUATIONAll prototypes of learned classes P 0:t-1 = {p c , c} c∈Y0:t-1 are stored in memory. In each training iteration of current phase t, existing works [6; 38; 10] augment the memorized prototypes P 0:t-1 to P 0:t-1 and train the classifier jointly with the current data D t . In particular, the prototypes are involved in the standard classification optimization with the following objective:EQUATIONCompared to exemplar rehearsal, prototype rehearsal is more memory efficient and privacy secure. In conclusion, the overall loss function of the baseline can be expressed as:EQUATIONwhere α 1 and α 2 are the weights of the distillation loss and prototype loss, respectively. The specific implementation of prototype augmentation is not our focus. In this paper, we implement our approach based on the pipeline in PRAKA [10] . Our method can be incorporated with different augmentations and plugged into other baselines, such as PASS [6] and IL2A [38] . An overview of our Prospective Representation Learning scheme is shown in Fig. 2 . It consists of a preemptive embedding squeezing constraint in the base phase and a prototype-guided representation update strategy in the incremental phase. The specific implementation of the two components is described in the following.Preemptive Embedding Squeezing. In the base phase (t = 0), a common training paradigm of NECIL is to optimize the empirical loss over the training set D t as eq. ( 1). Without consideration of the future incremental learning, it overspreads the embedding space. As new classes come in, the embedding of old classes needs to be squeezed to make room for new ones. However, striking a balance in this process is challenging, especially without the old data. Therefore, we would like to be proactive and reserve space for future classes by squeezing the embedding of current classes in the base phase. Specifically, we impose a preemptive embedding squeezing (PES) constraint to cluster features of the same class and make features of different classes separate from each other. To reduce complexity, the PES loss is computed over a mini-batch data B = {x i , y i } n i=1 ∈ D t , which can be formulated as:EQUATIONEQUATIONEQUATION)where n is the batch size, ⟨•, •⟩ denotes the cosine similarity operator. As L P ES is minimized, the first term (1 -s) facilitates intra-class concentration, and the second term (1 + d) aims to reinforce inter-class reserved separation, as shown in Fig. 2 (A) . Since s, d ∈ [-1, 1), both terms are greater than zero. The hyper-parameter λ controls the priority ratio of intra-class constraints and inter-class constraints. Since our PES is implemented in a vectorized manner on the mini-batch, it does not incur excessive computational burden.With the preemptive embedding squeezing constraint, the optimization objective for the base phase training in eq. ( 1) can be rewritten as:EQUATION)where γ is a hyperparameter controlling the weights of loss. Get training set D t 3:for minibatch B = {x i , y i } n i=1 ∈ D t do 4: if t = 0 then 5: Compute L t = L ce + γ * L P ES 6: Update model {F θt , G φt } 7: else 8:Get prototypes set P 0:t-1Compute ComputeL t = L ce + α 1 L kd + α 2 L pro + α 3 L P GRUp c = E (x,y)∼Dt [F θt (x) | y = c] 14:Update prototypes set P 0:t-1 15: end for 16: return Model {F θt , G φt } Prototype-Guided Representation Update. In the incremental phase (t > 0), we would like to embed the new class into the previously reserved space. The plain idea is to keep the new classes well clustered and distanced from the old ones. To this end, we propose a prototype-guided representation update (PGRU) strategy, as shown in Fig. 2 (B), which employs prototypes as proxies for past classes to guide the embedding of new classes into the appropriate space. However, it is not practical to establish a relationship directly between the saved prototypes and the new class features extracted by the current model due to the continual updating of the current embedding space. To mitigate the mismatch between the old class prototype and the new class features, on the one hand, we use the frozen model from the previous phase t -1 to extract the new class features, which has been implemented in the baseline as shown in eq. ( 3); on the other hand, the new classes features and the saved prototypes are projected into a unified latent space. Then, we construct orthogonal structures between the new class features and the old class prototypes in the latent space:EQUATIONwhere | • | denotes the absolute value operator, P is a projector with parameters ϕ. Similarly, L ort is also implemented in the mini-batch to reduce computational costs. Inspired by [61] , we use a simple undercomplete autoencoder as the projector. It consists of a linear layer followed by ReLU activation that maps the features to a low-dimensional subspace and another linear layer followed by sigmoid activation that maps the features back to high dimensions. When minimizing L ort , it will promote orthogonality between the new class features and the old class prototypes. By the above operations, we would like to allow the new class of features to be embedded in appropriate positions and to keep clustering in the latent space.Our ultimate goal is to guide the update of the current model. Hence, we align the current embedding space with the latent space as:EQUATIONwhere L M SE is mean squared error (MSE) loss. In summary, the PGRU loss can be defined as:EQUATIONIn the incremental phase, the optimization objective in eq. ( 6) can be rewritten as:EQUATIONThe main procedure is summarized in algorithm 1. Implementation details. Our method is implemented with PyCIL [65] . For a fair comparison with [6] , we adopt ResNet-18 [66] as the backbone network. The batch size is set to 64 for CIFAR-100 and TinyImageNet and 128 for ImageNet-Subset and ImageNet-1K. During training, the model is optimized by the Adam optimizer with β 1 = 0.9, β 2 = 0.999 and ϵ = 1e -8 (weight decay 2e-4). For ImageNet-1K, the learning rate starts at 0.0005 for all phases. The learning rate decays to 1/10 of the previous value every 70 epochs (160 epochs in total) in the base phase and every 45 epochs (100 epochs in total) in each incremental phase. For other datasets, the learning rate starts from 0.001 and decays to 1/10 of the previous value every 45 epochs (100 epochs in total) for all phases. We use λ = 0.5 and γ = 0.1 for all datasets. Regarding the loss weights, for comprehensive performance considerations and with reference to previous studies [6; 51] , we set α 1 = 10, α 2 = 10, and α 3 = 2 for training. We conduct our experiments on an RTX4090 GPU.Metric. We evaluate the methods in terms of average incremental accuracy. Average incremental accuracy A T is computed as the average of the accuracy of all phases (including the base phase) and is a fair metric to compare the overall incremental performance of different methods:EQUATIONwhere a t is the average accuracy over all seen classes on phase t.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?Answer: [NA]Justification: The paper does not include theoretical results.Guidelines:• The answer NA means that the paper does not include theoretical results.• All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced. • All assumptions should be clearly stated or referenced in the statement of any theorems.• The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition. • Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material. • Theorems and Lemmas that the proof relies upon should be properly referenced. 
comparison with sota: 
 We compare our method with the state-of-the-art (SOTA) methods of NECIL (EWC [23] , LwF_MC [67] , MUC [68] , SDC [8] , PASS [6] , SSRE [7] , SOPE [11] , POLO [51] , PRAKA [10] and NAPA-VQ [52] ). "Fine-tuning" refers to continuously fine-tuning the network on the new task with only cross-entropy loss. "Joint" means that when learning a new task, all data from past tasks are available to jointly train the model, which can be considered as an upper bound of the CIL model. The results reported for PASS are obtained with self-supervised learning.The quantitative comparisons of average incremental accuracy are reported in Tab. 1. In comparison with the SOTA, our method improves by 1.4% and 6.0% on CIFAR-100 and TinyImageNet datasets, respectively. To further investigate the behavior of different methods on larger data, we also evaluated their performance on ImageNet-Subset. Compared with suboptimal results, PRL achieves an average improvement of 3.6%. The outstanding performance on ImageNet-Subset demonstrates the reliability of our method. To provide a more nuanced view of the changes in performance of the different methods over the course of incremental learning, we show accuracy curves for CIFAR-100, TinyImageNet and ImageNet-Subset in Fig. 3 . The accuracy of our method remains ahead as we continue to learn new tasks. By prospective learning, our approach demonstrates strengths early on that will be maintained and even enlarged over the course of continuously learning new tasks. To analyze the impact of each component in our method, we perform several ablation studies on CIFAR-100 and TinyImageNet datasets. We use the prototype augmentation technique in [10] as eq. ( 5) in our baseline. As shown in Tab. 2, The first line shows the performance of our baseline model.Our baseline is strong due to using the prototype augmentation in [10] . Even on the strong baseline model, both preemptive embedding squeezing (PES) constraint and prototype-guided representation update (PGRU) strategy can bring considerable performance improvements. Furthermore, the table shows that PES plays a more central role than PGRU. This is reasonable since the space reserved by PES for future classes is the basis for the PGRU to guide new classes to embed in the representation space during the incremental phase.Visualization. To analyze the impact of PRL on representation learning, we visualize the embedding space of 2D feature vectors on CIFAR-100 (5 phases) with t-SNE [69] in Fig. 4 . Specifically, we (1) Plasticity and stability analysis. An incremental learner should acquire new knowledge of the current task for the sake of plasticity and also preserve knowledge from previous tasks for the sake of stability [70; 71] . We present an analysis of the plasticity and stability of the different methods in Fig. 6 . First, we observe a gradual decline in average performance on past tasks during incremental learning. This is rational because experiencing more tasks also results in heavier catastrophic forgetting. Nonetheless, our method exhibits better stability due to less degradation and consistently superior average performance on old tasks. Then we turn our attention to the current task and also found a performance degradation as more and more tasks are learned. This corresponds to a gradual reduction in plasticity since tasks are sampled uniformly from the set of possible tasks, which is consistent with observations from previous studies [72; 73] . PRAKA [10] starts with good performance, but its plasticity degrades as more tasks are learned. NAPA-VQ consistently performs poorly on the current task, which is also in line with the results in Fig. 5 . Remarkably, PRL maintains a good performance on the current task and has yet to show a visible decline. In general, our method achieves a better trade-off between stability and plasticity.Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?Answer: [Yes] Justification: Please refer to Sec. 4.1. 
conclusion and limitation: 
 In this work, we consider the conflict between old and new classes in NECIL from a prospective view. In the base phase, we construct a preemptive embedding squeezing constraint to reserve space for future classes by enforcing intra-class concentration and inter-class reserved separation. In the incremental phase, we propose a prototype-guided representation update (PGRU) strategy, which reduces the impact on the old class during model update by keeping the new class embedding away from the old class prototype. In cases where exemplars cannot be saved, waiting until the conflict arrives could exacerbate the problem, and we offer a novel solution. Through extensive experiments on four public benchmarks, our method exhibits excellent average performance and can provide a good balance between stability and plasticity. However, since the number and distribution of unknown classes cannot be predicted, how to rationally allocate the space of base classes in prospective learning is open to further discussion.Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?Answer: [Yes] Justification: We have provided a link to the code repository on the first page.Guidelines:• The answer NA means that paper does not include experiments requiring code. • Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted. 
experimental setting/details: 
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experiment statistical significance: 
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 Appendix: A.To investigate the sensitivity of our method to the hyper-parameters λ and γ, we performed ablation experiments on three settings (5 phases, 10 phases and 20 phases) of the CIFAR-100 dataset. In Fig. 7 we show the impact of λ, which controls the priority ratio of intra-class constraints and inter-class constraints. A smaller λ means that the preemptive embedding squeezing (PES) is more concerned with intra-class concentration. Conversely, for a larger λ, more emphasis is placed on inter-class separation. When the value of λ is either too large or too small, the performance of our method degrades, indicating that there is a need to maintain a certain balance between the intra-class constraint and the inter-class constraint. The best performance is achieved when λ is equal to 0.5, suggesting that for prospective learning in NECIL, intra-class concentration could be more important than inter-class separation.We also provide an analysis of the impact of the hyper-parameters γ in Fig. 8 . The performance of our method is stable on the 5 phase setting. The performance of the model gradually increases as γ increases on the 10-phase and 20-phase settings, peaking at γ = 0.1. However, continued increase in the values of γ leads to a decline in model performance. We argue that too large loss weights cause PES to interfere with the optimization of cross-entropy for classification performance. In addition, our method is more sensitive to the values of λ and γ when there are more tasks (20 phases) to learn.For the hyperparameter of loss weight, we set α 1 = 10, α 2 = 10, and α 3 = 2 by default. When a sensitivity analysis is performed on one of the hyperparameters, default settings are used for the remaining hyperparameters. As shown in Fig. 9 , the left column shows the effect of changing the value of each hyperparameter on the average incremental accuracy of our method, and the right EQUATIONImpact of hyperparameters on on the new and old tasks Figure 9: The figures in the left column show the effect of changing the value of each hyperparameter on the average incremental accuracy of our method on CIFAR-100 dataset, where 'P' denotes the number of incremental phases. The figures in the right column show the effect of changing the value of each hyperparameter in the last phase on the accuracy on the new and old tasks on CIFAR-100 dataset (P=10), respectively.column shows the effect of changing the value of each hyperparameter in the last phase on the accuracy on the new and old tasks, respectively.Among the three hyperparameters in eq. ( 14), α 1 and α 2 are common in previous NECIL methods and represent the weights of distillation loss and prototype loss, respectively. The main role of these two loss functions is to maintain the pre-existing knowledge of the model. Therefore, as shown in Fig. 9 (d) and Fig. 9 (e), as α 1 and α 2 get larger, the optimization of the model will be biased towards maintaining stability at the expense of plasticity, resulting in the model performing better on the old task and worse on the new task. It can be seen that as the value of α 1 and α 2 increases to a certain level its performance improvement on old tasks slows down. Excessively large values of and will bring much less gain on the old task than they will hurt performance on the new task. For the consideration of comprehensive performance and with reference to previous works [6; 51] , we set α 1 = 10 and α 2 = 10 for our method.Then α 3 controls the loss of the Prototype-Guided Representation Update (PGRU) proposed in this paper. In Fig. 9 (c), as α 3 increases PGRU comes into play. The effect of increasing α 3 on the overall performance of the algorithm fluctuates, which may be caused by overly strict constraints on the learning of new class representations. Overall, our algorithm is robust to the hyperparameters.Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. 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In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed. • While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. , with an open-source dataset or instructions for how to construct the dataset). (d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility.In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results. 


Decision: This paper introduces a Prospective Representation Learning (PRL) scheme designed to manage conflicts between old and new classes in non-exemplar class-incremental learning. The proposed approach includes strategies for both the base and incremental stages, effectively reserving space in the learned representation and maintaining its structure as new classes are introduced. Experimental results demonstrate the effectiveness of PRL.

The paper has received mixed reviews, with one reviewer increasing their score after the rebuttal, while others did not respond to the updated results provided by the authors. The final scores after the rebuttal phase are (6, 5, 5, 4).

After a careful review, the AC finds that the paper does introduce a degree of novelty and the results validate its effectiveness. Given the significance of this approach to the field of class-incremental learning (CIL) and its potential to inspire future research, the AC suggests this paper as a borderline accept.

If the paper is accepted, the authors should carefully address the concerns raised by the reviewers, particularly the need for more detailed comparisons and in-depth analysis with baselines using CE losses, as well as further discussions of the related works mentioned by the reviewers.

Reviews: review: paper_summary: paper_summary: The paper proposes a method to deal with incremental classification task in which no exemplars from the previously seen classes can be saved for usage during training on the newly arriving classes. The proposed method squeezes the embedding distribution of the current classes to reserve space for forward compatibility with future classes and reduces the impact of introducing new classes by trying to restrict the embeddings of the new classes in the regions not occupied by the previously seen classes. The method uses the class prototype of the previously seen classes but does not use any exemplars. The method uses Preemptive Embedding Squeezing and Prototype-Guided Representation Update to achieve the above goals.
strength_weakness: strength_weakness: The paper should include a separate section to discuss the difference between this method and other methods that employ feature space compression for incremental learning.
The standard deviation in the results between different runs is not mentioned.
Apart from accuracy improvement, does the proposed method involve fewer/more parameters as compared to the other compared methods? Is there any difference in training or testing time for the proposed method as compared to the others.
questions: questions: The paper should include a separate section to discuss the difference between this method and other methods that employ feature space compression for incremental learning.
The standard deviation in the results between different runs is not mentioned.
Apart from accuracy improvement, does the proposed method involve fewer/more parameters as compared to the other compared methods? Is there any difference in training or testing time for the proposed method as compared to the others.
limitations: limitations: The paper mentions a limitation in that it is not able to rationally allocate the space of base classes since the number and distribution of unknown classes cannot be predicted. However, the authors did not experimentally demonstrate any such issue, which can be possibly done by changing the order of the classes between multiple runs.
paper_summary: paper_summary: This paper focuses on non-exemplar class-incremental learning, specifically addressing the challenge of balancing old and new classes. The author introduces Prospective Representation Learning (PRL), which involves constructing a preemptive embedding squeezing constraint to allocate space for future classes. Additionally, the author proposes a prototype-guided representation update strategy.
strength_weakness: strength_weakness: 1.What is the meaning of IIC in equation 9? The paper does not explain its meaning; I guess it stands for PES.
2.Many previous works have studied mapping class centers to different subspaces (orthogonal). The paper should compare similar works to highlight the differences and advantages of the proposed method. As indicated by the following references: [1,2,3].
3.The PASS paper also uses prototype augmentation (and proposes other methods), but your baseline is higher than PASS, especially in TinyImageNet P=20, by almost 10%. The author should explain the advantages of using prototype augmentation in the baseline or provide experimental results without prototype augmentation.
4.In equation 14, there are many hyperparameters, /alpha1,2,3. The author should provide more sensitivity analysis of the hyperparameters to make the experiments more thorough.

[1] Chaudhry A, Khan N, Dokania P, et al. Continual learning in low-rank orthogonal subspaces[J]. Advances in Neural Information Processing Systems, 2020, 33: 9900-9911.
[2] Guo Y, Hu W, Zhao D, et al. Adaptive orthogonal projection for batch and online continual learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2022, 36(6): 6783-6791.
[3] French R M. Dynamically constraining connectionist networks to produce distributed, orthogonal representations to reduce catastrophic interference[C]//Proceedings of the sixteenth annual conference of the cognitive science society. Routledge, 2019: 335-340.
questions: questions: 1.Many previous works have studied mapping class centers to different subspaces (orthogonal). The paper should compare similar works to highlight the differences and advantages of the proposed method. As indicated by the following references: 1, 2.
2.The PASS paper also uses prototype augmentation (and proposes other methods), but your baseline is higher than PASS, especially in TinyImageNet P=20, by almost 10%. The author should explain the advantages of using prototype augmentation in the baseline or provide experimental results without prototype augmentation.
limitations: limitations: Yes
paper_summary: paper_summary: This work introduces a Prospective Representation Learning (PRL) scheme to prepare the model for handling conflicts of the balance between the old and new classes in Non-exemplar class-incremental learning (NECIL). The author proposes to squeeze the embedding distribution of the current classes in the base phase and make the new class features away from the saved prototypes of old classes in the incremental phase, improving the balance of old and new classes in the existing NECIL baselines.
strength_weakness: strength_weakness: 1.  Few analysis was conducted on the loss weight α in formula 14.
2.  There are some grammar or writing errors in the text, such as the repeated appearance of the "propose" on line 111.
questions: questions: 1. Why not perform Preemptive Embedding Squeezing (PES) on new classes during the incremental phase? Will this not cause overlap in the feature space of the new classes?
2. What does the $L_{IIC}$ of Formula 9 indicate? I do not find the corresponding explanation. (ps: I guess the author may have mistakenly wrote $L_{PES}$ as $L_{IIC}$)
3. What do the old class prototypes of formula 11 and the potential spatial features of formula 12 specifically refer to, and is there a relationship between them?
limitations: limitations: None
paper_summary: paper_summary: This paper aims to solve the catastrophic forgetting in non-exemplar class-incremental learning. The authors propose Prospective Representation Learning (PRL) to prepare representation space for classes in later tasks in advance. Such forward compatible method first squeezes the embedding distribution of the current classes to reserve space for forward compatibility with future classes, then makes the new class features away from the saved prototypes of old classes. Extensive experiments are performed to demonstrate that the method is effective.
strength_weakness: strength_weakness: 1. The novelty of the method could be one of the concerns. There are previous works considering forward-compatible incremental learning [1,2]. The embedding space reservation is not a new concept, which has been proposed in [2]. This paper lacks related works and discussion on the advantages over other forward compatible methods and comparison with them.
2. Lack of evaluation on large datasets like Imagenet1000.

[1] Shi et al. Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning.

[2] Zhou et al. Forward Compatible Few-shot Class-Incremental Learning.
questions: questions: 1. What are the advantages of PRL over other forward-compatible methods?
2. Can you provide the evaluation results on large datasets?
limitations: limitations: The paper has a section of limitation.

Comments: title: Please respond to the authors' rebuttal and comments if you haven't already done so
Official Comment by Authors
Thank You for the response
Responses to Comment by Reviewer SYbV
Official Comment by Reviewer SYbV
Discussion period instructions
Official Comment by Authors
comment: Dear Reviewers,

As the discussion period is nearing its end, with less than one day remaining, I kindly urge you to respond to the authors' rebuttal and latest comments if you haven't already done so. Please don't hesitate to ask any remaining questions, as this will be our final opportunity to interact with the authors before the next phase of the review process.

Thank you very much for your dedication and effort.

Best regards,

AC
Thank you for taking the time to read our response and increasing your score! We are glad to hear that our response addressed your concern.
The authors' response resolves most of my concerns, I decide to raise my score.
Thank you for your response. We suppose that you may have misunderstood our response and our experiments in rebuttal.

Our proposed PES aims to construct a better initial embedding space. Therefore, in our initial submitted version, **PES is only performed in the first phase**. We have done extensive experiments in our submitted version (Table 2 and Figure 3) to **demonstrate the effectiveness of this manner.** 

We also list the results in the following. Results demonstrate that PES can significantly improve the forward compatibility of model since it reserves space to prepare for future classes. For continual learning on incremental new classes, to make new classes embedded in the space previously reserved, PES was not included in the incremental phase in our submitted version.

|     | CIFAR-100 (5 phases) | CIFAR-100 (10 phases) | CIFAR-100 (20 phases) | TinyImageNet (5 phases) | TinyImageNet (10 phases) | TinyImageNet (20 phases) |
| --- | :---: | :---: | :---: | :---: | :---: | :---: |
| baseline | 69.25 | 68.52 | 65.93 | 55.04 | 54.15 | 51.65 |
| baseline w/ PES | 70.57 | 69.64 | 67.58 | 57.08 | 55.84 | 53.58 |

In the first round of review, as you suggested, we add experiments that using PES in the incremental phase, shown in the rebuttal, which lead your misunderstandings. The results have verfied that **adding PES in the incremental phase indeed does not bring in further improvement**. It means that **our original design in submitted version is reasonable and effective**. We do not need PES in the incremental phase.

We hope this response clarifies your misunderstandings.
Thanks to the authors for their detailed responses, but I still have some concerns about the PES method.

Using only the CE loss can also achieve good performance as the authors mentioned, and based on the results provided on the CIFAR100, the improvement is not significant.
Dear Reviewers,

The authors have provided comprehensive rebuttals and tried to address the concerns raised in your reviews. Please take the time to review their responses carefully. If you have any further questions or require additional clarification, please engage in a discussion with the authors. Thank you for your continued efforts.

AC
Dear reviewer SYbV,

We thank you sincerely for your time and effort in reviewing our manuscript and providing valuable suggestions. We have provided detailed responses to your questions and hope that they adequately address your concerns. If you need further clarification or have any other questions, please feel free to discuss them with us! We are more than willing to continue our communication with you.
We would greatly appreciate it if you would update the rating by synthesizing other reviewers' comments as well as our responses.