CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability
Authors: Martin Mundt
Steven Lang
Quentin Delfosse
Kristian Kersting
Venue: ICLR 2022 Poster (2022)
Keywords: continual learning
lifelong learning
machine learning evaluation

Abstract: What is the state of the art in continual machine learning? Although a natural question for predominant static benchmarks, the notion to train systems in a lifelong manner entails a plethora of additional challenges with respect to set-up and evaluation.  The latter have recently sparked a growing amount of critiques on prominent algorithm-centric perspectives and evaluation protocols being too narrow, resulting in several attempts at constructing guidelines in favor of specific desiderata or arguing against the validity of prevalent assumptions.  In this work, we depart from this mindset and argue that the goal of a precise formulation of desiderata is an ill-posed one, as diverse applications may always warrant distinct scenarios. Instead, we introduce the Continual Learning EValuation Assessment Compass: the CLEVA-Compass. The compass provides the visual means to both identify how approaches are practically reported and how works can simultaneously be contextualized in the broader literature landscape.  In addition to promoting compact specification in the spirit of recent replication trends, it thus provides an intuitive chart to understand the priorities of individual systems, where they resemble each other, and what elements are missing towards a fair comparison.  

Published as a conference paper at ICLR 2022
CLEVA-COMPASS: A CONTINUAL LEARNING EVALUATION ASSESSMENT COMPASS TO PROMOTE RESEARCH TRANSPARENCY AND COMPARABILITY
Martin Mundt', Steven Lang', Quentin Delfosse' & Kristian Kersting" 1.2.3 1 AI and Machine Learning Group, Dept. of Computer Science, TU Darmstadt, Germany
Centre for Cognitive Science, TU Darmstadr, Germany
3 Hessian Center for Al (hessian.Al), Darmstadt, Germany
(martin mundt,, steven lang, quentin delfosse, kersting)ecs. weceecc.tuudaaeee tu-darustadt.
ABSTRACT
What is the state of the art in continual machine learning? Although a natural question for predominant static benchmarks, the notion to train systems in a life- long manner entails a plethora of additional challenges with respect to set-up and evaluation. The latter have recently sparked a growing amount of critiques on prominent algorithm-centric perspectives and evaluation protocols being too nar- row, resulting in several attempts at constructing guidelines in favor of specific desiderata arguing against the validity of prevalent assumptions. In this work, we depart from this mindset and argue that the goal of a precise formulation of desiderata is ill-posed one, as diverse applications may always warrant distinct scenarios. Instead, we introduce the Continual Learning EValuation Assessment Compass: the CLEVA-CCopaas The compass provides the visual means to both identify how approaches are practically reported and how works can simultane- ously be contextualized in the broader literature landscape. In addition promot- ing compact specification in the spirit of recent replication trends, it thus provides an intuitive chart to understand the priorities individual systems, where they resemble each other, and what elements are missing towards a fair comparison.
INTRODUCTION
Despite the indisputable successes of machine learning, recent concerns have surfaced over the field heading towards potential reproducibility crisis (Henderson et al.) 2018), as previously identified in other scientific disciplines (Baker 2016). Although replication may largely be assured through modern software tools, moving beyond pure replication towards reproducibility with factual inter- pretation of results is accompanied by tremendous remaining challenges. Specifically for machine learning, recent reproducibility initiatives (Pineau et al.) 2021) nicely summarize how differences in used data, miss- or under-specification of training and evaluation metries, along with frequent over-claims of conclusions beyond gathered empirical evidence impose persisting obstacles in our current literature. Similar conclusions have been reached in related works focused on specifics of inforcement learning (Li & Talwalkar 2019), neural architecture search (Lindauer & Hutter 20020), human-centered machine learning model cards (Mitchell et al. 2019), or general dataset sheet spec- ifications (Bender & Friedman 2018 Gebru et al.) 2018). which all make valuable propositions to overcome existing gaps through the creation of standardized best-practice (check llists.
It should thus come as no surprise that the emerging work in continual learning is no stranger to the above challenges. Superficially, continual learning has the intuitive objective of accumulating infor- mation and learning concepts over time, typically without the ability to revisit previous experiences, frequently also referred to as lifelong learning (Chen Liu 2018). However, there is no unique agreed-upon formal definition beyond the idea to continuously observe data, where the time compo- nent holds some practical implication on changes in the objective, the evolution of concept labels, or general statistical shifts in the data distribution. The majority of modern surveys ambiguously conflate these factors as a sequence of tasks (Parisi et al.) 2019:; Lesort et al.] 2019 Hadsell et al. 2020 Lesort et al. 2020 Biesialska et al. 2021). Much in contrast prevalent static benchmarks,

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Figure 1: Per year machine learning publications. Left: cumulative amount across keywords with continuous components that influence continual learning practice, see Section 2 Right: increasing use of "continual", demonstrating a shift from the preceding emphasis on "lifelong". Data queried using Microsoft Academic Graph (Sinha et al. 2013), based on keyword occurrence in the abstract.
the question of reproducibility, interpretation of results, and overall comparability becomes even more complex function of nuances in employed data, training and evaluation protocols.
The latter fact has sparked various attempts consolidation critique. On the one hand, several works have made important suggestions for continual learning desiderata with respect to evaluation protocols (Farquhar & Gall 2018, Díaz-Rodríguez et al., 2018 Kemker al. (018)) and catego- rization of incremental training set-ups (van de Ven & Tolias 2019) Lesort et al., (02T). On the other hand, empirical assessments have demonstrated that performance and comparability break down rapidly if often unexposed protocol aspects deviate (Plülb & Gepperth, 2019; Delange et al.) 202Th. Following the broader exposition of the recent reviews of Mundt al. (2020a) and Delange et al. (202I), evaluation becomes convoluted because intricate combinations of elements originating from various related machine learning paradigms affect continual learning practice. As the number of publications across these paradigms increases, see Figure [ reproducibility, comparability, and interpretation of results thus also become increasingly difficult.
In this work, we follow in the footsteps of previous reproducibility works to promote transparency and comparability of reported results for the non-trivial continual learning case. Rather than adding to the ongoing discussions on desiderata violation of assumptions, posit that the development of distinct applications warrants the existence of numerous continual scenarios. Based on respec- tively highlighted evaluation nuances and their implications when absorbed into continual learn- ing, we derive the Continual Learning EValuation Assessment (CLEVA) Compass. The CLEVA- Compass provides a compact visual representation with unique two-fold function: 1. it presents an intuitive chart to identify a work's 's priorities and context in the broader literature landscape, 2. it enables direct way to determine how methods differ terms of practically reported metrics, where they resemble each other, or what elements would be missing towards fairer comparison.
In the remainder of the paper, we start by sketching the scope of continual learning in Section 2 first by outlining the differences when going from static benchmarks to continual learning, followed by an exposition of evaluation nuances emanating from related machine learning paradigms. Section and illustrate its necessity and utility at the
3 we then proceed to introduce the CLEVA-CCmppe hand of several continual learning works. Before concluding, we summarize auxiliary best-practices proposed in related prior works and finally discuss limitations and unintended use of the CLEVA- Compass. To encourage general adoption, both for prospective authors to add methods and for application oriented practitioners identify suitable methods, we supply various utilities: template for direct inclusion into LaTeX, a Python script, and the CLEVA CLEVA-Compass Graphical User Interface (GUT), together with repository aggregate methods' compasses, all detailed in Appendix C and publicly available at
com/ml ml www.reeeeeeeeyeeee Compass
2 THE SCOPE AND CHALLENGES OF CONTINUAL LEARNING EVALUATION
As already argued, there is no unique agreed-upor formal definition continual learning. One of the few common denominators across the continual machine learning literature is the understanding
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that catastrophic interference imposes a persisting threat training models continually over time (McCloskey & Cohen 1989, Ratclifi 1990). That is, continuously employing stochastic optimiza- tion algorithms to train models on sequential (sub-)sets of varying data instances, without being able to constantly revisit older experiences, comes with the challenge of previously learned param- eters being overwritten. As this undesired phenomenon is stark contrast with accumulation of knowledge time, preventing model forgetting is thus typically placed the center of continual learning. Consequently, the focus has been placed on a model-centric perspective, e.g. by proposing algorithms to alleviate forgetting through rehearsal of data prototypes in training, employing various forms of generative replay, imposing parameter and loss penalties over time, isolating task-specific parameters, regularizing the entire model's functional
treating it from perspective of dynami- cally adjustable capacity. We defer to review papers for taxonomies and details of algorithms (Parisi
al. 2019 Lesort al. 2019 Hadsell al. 2020 Lesort al. 2020 Biesialska al. 2021).
In practice. however, alleviating forgetting is but one of a myriad of challenges in real-world formu- lations of continual learning. Several orthogonal questions inevitably emerge, which either receive less attention across literature assessment or are frequently not made sufficiently explicit. A fair assessment and factual interpretation of results rendered increasingly difficult. To provide the necessary background behind the statement, we briefly discuss newly arising questions when shift- ing from a static benchmark to continual perspective and then proceed contextualize conceivable evaluation protocol nuances in anticipation of our CLEVA-Compp
2. FROM STATIC TO CONTINUAL MACHINE LEARNING WORKFLOW
To highlight the additional challenges in continual learning consider our visualization in Figure depicting the benchmark inspired machine learning workflow as advocated by Google Cloud] 202 In the center, we find the six well-known sequential steps going from the preparation of data, to de- signing and tuning our ML model, down to the deployment of a model version to use for prediction. Naturally, these steps already contain various non-trivial questions, some which we have high- lighted in the surrounding gray boxes of the diagram. When considering popular benchmarks such as ImageNet (Deng et al. 2009), a considerable amount of effort has been made for each individ- ual workflow step. For instance, assembling, cleaning and pre-processing the dataset has required substantial resources, a decade of work has been attributed to the design of models and their opti- mization algorithms, and plenty of solutions have been developed to facilitate efficient computation
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or deployment. It is commonplace treat these aspects in isolation in the literature. In other words, it is typical for approaches to be validated within train-val-test splits, where either a model-centric approach investigates optimizer variants and new model architectures, or alternatively, data-centric approach analyzes how algorithms for data curation or selection can be improved for given models.
Much in contrast to any of the prevalent static benchmarks, establishing a similar benchmark-driven way of conducting research becomes genuinely difficult for continual learning. Instinctively, this is because already partially intertwined elements of the workflow now become inherently codependent and inseparable. Once more, we provide a non-exhaustive list of additional questions in the orange boxes in the diagram of Figure 2 Here, the boundaries between the steps are now blurred. To give a few examples: train and test sets evolve over time, we need to repeatedly determine what data to include next, an ongoing stream of data may be noisy or contain unknown elements, models might require new inductive biases and need to be extended, acquired knowledge needs to be protected but should also aid in future learning, and deployment and versions become continuous. In turn, setting specific research focus on one of these aspects attributing increased importance to only portion allows for an abundance of conceivable, yet incomparable, implementations and investigations, even when the overall goal of continual learning is shared on the surface.
2.2 EVALUATION IN THE CONTEXT OF RELATED MACHINE LEARNING PARADIGMS
Although a full approach to continual learning should ideally include all of the underlying aspects illustrated in Figure 2 many of these factors have been subject to prior isolated treatment in the lit- erature. We posit that these related machine learning paradigms have a fundamental and historically grown influence on present "continual learning" practice, as they are in themselves comprised of components that are continuous. More specifically, we believe that choices in continual learning can largely be mapped back onto various related paradigms from which continual scenarios have drawn inspiration: multi-task lcarning (Caruana 1997). transfer learning and domain adaptation (Pan & Yang 2010), few-shot learning (Fink 2005 Fei-Fei et al., (006). curriculum learning (Bengio al.
2009). , active learning (Settles 2009). open world learning (Bendale & Boult (015)), online learning
(Heskes & Kappen| 1993] Bottou 1999), federated learning (McMahan et al. [207]] Kairouz al.,
2021]. and meta-learning TTrrun & Pratt 1998). We capture the relationship with respect to set-up and evaluation between these related paradigms and continual learning in the diagram of Figure For convenience, we have added quotes of the paradigm literature definitions in the figure. On the arrows of the diagram, we indicate the main evaluation difference and respectively how paradigms can be connected to each other.
As each paradigm comes with its own set of assumptions towards training and evaluation proto- cols, it becomes apparent why the quest for strict set of desiderata can be considered as ill possed for the continual learning hypernym. In the next section, we thus introduce the CLEVA-Compass, as alternative that emphasizes transparency and comparability over strict desiderata. To further clarify this with an easy example, let us consider one of the most popular ways to construct pro- tocols in the academic continual learning literature. Here, a continual investigation is set up by defining a sequence based on extracting splits of existing benchmark datasets and introducing them sequentially (Lesort et al., 2020 Biesialska et al.] 2021: Delange al. 2021). Such scenario generally consist of individually introduced classes in image datasets like ImageNet (Deng et al., 2009, MNIST (LeCun et al. (998). CIFAR (Krizhevsky) 2009). Core50 (Lomonaco & Maltoni
2017 learning unique sounds (Gemmeke et 2017) or skills (Mandlekar et al. 2018) Fan et al.,
2018 in sequence, or simply creating chain across multiple datasets for natural language process- ing (McCann et al., 2018; Wang et al.) 2019a b) and games in reinforcement learning (Bellemare) al. 2013). Arguably, such set-up is immediately derived from conventional transfer learning practice. Following the description of Pan & Yang (2010), the distinction between transfer and con- tinual learning can essentially be brought down to the fact that both consider more than one task in sequence, but transfer learning focuses solely on leveraging prior knowledge to improve the new tar- get task, typically a unique dataset or a set of classes, whereas continual learning generally intends to maximize performance on both prior and new tasks.
Even though these popular continual benchmarks already simplify the continuous data perspective to seemingly enable comparison akin to static benchmarks, there already persists an unfortunate amount of training, evaluation, and result interpretation ambiguity. For instance, Farquhar & Gal 2018): Pfülb & Gepperth| (2019) argue that simply the knowledge of whether and when new task
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Figure 3: Relationships between related machine learning paradigms with continuous components. Popular literature definitions are provided in adjacent boxes. Arrows indicate the nuances when including perspectives into one another, showcasing the plethora of potential influences in construc- tion of continual learning approaches. Outer nodes for federated, online and meta-learning can be combined and tied with all other paradigms. Note that only one arrow between nodes is drawn for visual clarity. Respectively highlighted distinctions can be constructed in mirrored directions.
is introduced already yields entirely different results, Mai et al. (002)) consider an arbitrary amount of revisits of older data and infinite time converge on subset as unrealistic, and Hendryccs & Di- etterich (2019) contend that the presence of unknown noisy data can break the system altogether and should actively be discounted. Similar to the above inspiration from transfer learning, these works could now again be attributed to drawing inspiration from online and open world learning. In this context, online learning (Heskes & Kappen 1993 Bottou 1999) attempts to optimize performance under the assumption that each data element is only observed once, which can again be directly applied to "continual learning" (Mai et al., 202T). Likewise, open world learning (Bendale Boult 2015) allows for unknown data be present in training and evaluation phases, which needs to be identified. Naturally, the latter can also be included seamlessly into continual lcarning (Mundt et al.) 2020a). The list can be continued by iterating through all conceivable combinations of paradigms, illustrating the necessity for an intuitive way to make the intricate nuances transparent.
3 THE CLEVA-COMPASS
The subtleties of the relationships in Figure 3 provide strong indication that it is perhaps less important to lay out desiderata for continual learning. However, we concur with prior works (Gebru et al. 2018. Mitchell al. 2019: Pineau et al. 2021 that it is of utmost importance to have clear
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Figure 4: The Continual Learning EValuation Assessment (CLEVA) Compass. The inner star plot contextualizes the set-up and evaluation of continual approaches with respect to the influences of related paradigms, providing visual indication for comparability. A mark the star plots' inner circle suggests an element being addressed through supervision, whereas mark on the outer star plots' circle displays an unsupervised perspective. The outer level of the CLEVA-Compass allows specifying which particular measures have been reported in practice, further promoting transparency interpretation of results. To provide an intuitive illustration, five distinct methods have been used to fill the compass. Although these methods may initially tackle the same image datasets, it becomes clear that variations and nuances set-up and evaluation render direct comparison challenging.
exposition of choices and communicate research in transparent manner. To maintain such flexible research perspective, yet prioritize reproducibility and transparency at the same time, we introduce the Continual Learning EValuation Assessment Compass. The CLEVA-Compas: draws inspiration from evaluations in prior literature across previously detailed related paradigms, in order to provide intuitive visual means to give readers a sense of comparability and positioning between continual learning works, while also showcasing compact chart to promote result reproducibility.
The CLEVA- VEVp-assssss itself is composed of two central elements. We first detail this composition, and then use it as a basis for consecutive discussion on the compass' necessity and utility at the hand of five example continual learning methods. To provide practical basis from the start, we have directly filled the exemplary compass visualization in Figure
3.1 ELEMENTS OF THE CLEVA- :OVAPSC INNER AND OUTER LEVELS
Figuratively speaking, the inner CLEVA-Compass level maps out the explored and uncharted ter- ritory in terms of the previously outlined influential paradigms, whereas the outer level provides compact way to visually assess which set-up and evaluation details are practically reported.
The inner compass level captures the workflow items of Figure 2 and reflects the in- fluence of Figure Jn continual learning. That is, for any given method, a visual indication can be made with respect to whether approach considers, for instance, the world to be open and contain unknown instances, data to be queried actively, optimization to be conducted in a federated or on- line manner, well the set-up including multiple modalities or task boundaries being specified. Warranted by common practice, we further include an indication of whether an episodic memory is employed, generative model trained, and if multiple models are considered feasible solution.
Inner level:

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In practice, propose the use of star diagram, where we mark the inner circle if particular approach follows and is influenced by the relevant axis in a supervised way. Correspondingly. the outer circle of the star diagram is marked if the respective component is achieved in a more general form without supervision. It is important to note that this level of supervision is individual to each element. That is, just because method has been used for an unsupervised task, does not automat- ically imply that all marked elements are similarly approached without supervision or vice versa. To give an example, one could consider continual unsupervised density estimation, where multiple models could each solve one in a sequence of unsupervised tasks/datasets, but a task classifier is re- quired to index the appropriate model for prediction. We provide one respective example, including Tigure@o chosen methods outlined in the upcoming section, for all inner compass level elements to further clarify the role of supervision in Appendix
We have intentionally chosen the design of the inner compass level as star diagram. These diagrams have been argued to provide an ideal visual representation when comparing plot elements, due to the fact that drawn shape is enhanced by contours (Fuchs et al. 2014). Shape has been shown to allow a human perceiver to quickly predict more facts than other visual properties (Palmer 1999). In addition. the closed contours of star plots have been discovered to result in faster visual search (Elder& & Zucker 1993), and in turn, supporting evidence has been found for resulting geometric region boundaries being prioritized in perception (Elder & Zucker 1998). This is aligned with the primary goal of the inner compass level, i.e. exposing the conscious choices and attributed focus of specific continual learning works, in order for researchers to find a better contextualization and assess if and when comparison of methods may be sensible.
Whereas the compass' star plot contextualizes the relation to linked paradigms with respect to comparability of set-up and evaluation protocols, the outer level places emphasis on the clarity with respect to reproducibility and transparency of specifically reported results. In essence, we propose to indicate a mark on the outer level for each measure that an approach practically reports their empirical investigation. Together, the inner and outer levels thus encourage methods to be considered in their specific context, and comparisons to be conducted in a fair manner by promoting disclosure of the same practical assessment. Note that a practical report of additional measures beyond the required amount for a full overlap is naturally further welcome. We describe level in Table where we also briefly summarize
Outer level:
the list of measures on the outer CLEVA- the relevance of each metric for continual learning and give credit to the corresponding prior works which have provided a previous highlight of their respective implications.
3.2 NECESSITY AND UTILITY: CEMAPSSS IN LIGHT OF FIVE EXAMPLES
To provide the necessary intuition behind above descriptions for inner and outer CLEVA-Compass levels, we have filled the example illustration in Figure 4 based on five distinct continual learning methods: OSAKA (Caccia et al.) 2020), FedWeIT (Yoon et al., 2021 1), A-GEM (Chaudhry et al.) 2019). VCL (Nguyen et al. 2018). and OCDVAE (Mundi et al. 2020b a). We visualize the compass for these continual vision methods because they have all initially conducted investigations on the same well-known type of image datasets, such as incrementally introducing classes over time in MNIST (LeCun et al. 1998) and CIFAR (Krizhevsky) (2099). Inspired by conventional machine learning practice, we might be tempted to benchmark and judge each approach's 's efficacy based on its ability to e.g. alleviate catastrophic forgetting. Our CLEVA-Compass directly illustrates why this would be more than insufficient, as each respective approach can be seen to differ drastically in involved set-up and evaluation protocols. For example, without delving into mathematical details, it suffices to know that Fed prioritizes a trade-off with respect to communication, parameter and memory efficiency in a federated setting. In contrast, OSAKA considers a set-up that places both an emphasis on online updates as well as considering an open world evaluation protocol, where unknown class instances can be encountered with a given chance. Arguably, such nuances can easily be missed, especially the more approaches overlap. They are typically not apparent when only looking at a result table on catastrophic forgetting, but are required as context to assess the meaning of the actual quantitative results. In other words, the compass highlights the required subtleties, that may otherwise be challenging to extract from text descriptions, potentially be under- -nde--specified, and prevents readers to misinterpret results out of context in light of prominent result table display.
With above paragraph in mind, we note that a fully filled compass star plot does not necessarily in- dicate the "best" configuration. This can also best be understood based on the five concrete methods
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Table 1: Description of conceivable measures on the outer level of the CLEVA-COmpasS. Further
details and literature suggestions for mathematical definitions are summarized in Appendix A
Measure
Description and relevance for continual learning
What data is introduced sequentially. The number of data instances is primary indicator for sample efficiency and provides the context for c.g. few-shot settings (Fink, 2005).
Data per task
The order in which tusks introduced, even if randomly sampled in practice. The order has significant impact obtainable continual performance (Mundt al., 2020a) depending the constructed curriculum (Bengio al., 2009).
Task order
Task specific parts of reported losses or metrics allow for a deeper assessment of each task's 's cvolution over time, c.g. "new" und "base" for first and most recent tusk, in addition to the overall average "all" (Kemker et al., 2018).
Per task metrics
The numher of optimization steps is crucial to gauge empirical convergence. The number of optimization steps on revisited data also distinguishes sequences of continual offline and truly online scenarios (Mai et al., 2021).
Optimization steps
Amount of data that is generated, if any. The quality and number of data instances sampled from generative model determine the effectiveness of rehearsal (Lesort et al., 2019)
Generated data
Amount of original data retained a buffer, if any. Rehearsing instances becomes a trivial solution the more the buffer approximates the original dataset size (Delange et al., 2021).
Stored data
Amount of overall parameters. A trivial solution to continual learning would be to allocate increasing amounts of separate and independent parameters over time, motivating a desire for parameter efficiency
Parameters
et al., 2018).
How much memory is used. Provides combined perspective on data storage and model parameter efficiency Doiz-Rodriguez et al., 2018).
Memory
Practically used computation time. Different algorithms and operations can consume dra- matically different compute time, in additional dependence on hardware, even when imple- mented in the same software (Barham & Isard, 2019).
Compute time
Number of multiply catriily--ccumulaee operations are an alternative to reporting compute require- ments, in a way that is not inherently tied to specific soft- and hardware (Sze et al., 2017).
MAC operations
Communication costs start to play critical role distributed decentralized federated perspective, where time spent on many rounds of communication can rapidly exceed that of model computations (McMahan al., 2017).
Communication
The amount of forgetting is a way to quantify the difference between maximum knowledge gained about the task throughout the learning process in the past and the knowledge that is currently still held about it (Chaudhry et al., 2018).
Forgetting
Forward transfer determines the influence that observed task has future task (Lopez- Paz Ranzato, 2017). quantifying the ubility for "zero-shot" learning (Fei-Fei al., 2006).
Forward transfer
Backward transfer (BWT) captures the improvement or deterioration an already observed task ex experiences when learning a new task (Lopez-Paz & Ranzato, 2017).
Backward transfer
Openness of the world describes the proportion hetween data points that assumed to originate from the investigated data distribution and potentially unknown, corrupted or perturbed instances (Scheirer al., 2013).
Openness
in Figure 4 On the one hand, some aspects, e.g. the use of external episodic memory in A-GEM Chauddhry et al. 2019) or existence of multiple models in VCL (Nguyen al. 2018), could be inter- preted as either an advantage or disadvantage, which is best left to decide for the respective research and its interpretation in scope of the underlying pursued applications. On the other hand, discrep- ancy between methods in the CLEVA-Compar should be compared very cautiously and discussed rigorously when taken out of their context, and that much work would be required to make a practical alignment for these specific cases. As such, the CLEVA- CEEA--ss is not a short-cut to dubious "state-of-the-art" claims, but instead enforces transparent comparison and discussion of empirical set-ups and results across several factors of im- portance. In complete analogy, we remark that a filled outer level does not necessarily imply the most comprehensive way to report. Once more, this is because some practical measures may not apply to a specific method in a certain application context, e.g. reports of generated/stored data or communication costs when the method doesn't 't rehearse and is not trained in federated setting.
indicates that these continual learning approaches
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4 UNINTENDED USE AND COMPLEMENTARY RELATED EFFORTS
We have proposed the CLEVA-Compass to promote transparent assessment, comparison, and inter- pretation of empirical set-ups and results. As the compass a conceivable approach for a compact visual representation, there is a natural limit to what it should cover and what it should be used for.
One aspect that should be emphasized, is that the CLEVA-Compass is not intended to speculate whether a specific method can in principle be extended. Instead, it prioritizes which context is taken into consideration in current practice and lays open unconsidered dimensions. Specifically, if paper does not report a particular compass direction, this may suggest that the method is not feasible in this scenario. However, it could also simply mean that the authors have not yet conducted the respective evaluation. A faithfully filled compass makes this transparent to researchers and should thus not be based on speculations. For instance, surveys (Parisi et al. 2019 Delange et al. 2021) commonly attribute several continual learning algorithms with the potential for being "task agnostic", but no corresponding theoretical or empirical evidence is provided. This has lead to empirical assessments, such the ones by (Farquhar & Gal 2018;; Pfülb & Gepperth (201)), to be "surprised" by the fact that certain methods do not work in practice when this factor is actually varied. Conclusively, if it is hypothesized that a method's capabilities extrapolate, it should be separated from the CLEVA-Compass' factual representation to avoid overly generalized, potentially false, conelusions. Its ultimate utility will thus depend on faithful use in the research community.
Apart from this unintended use of the CLEVA-Compass, there also exist several orthogonal aspects, which have received attention in prior related works. These works mainly encompass check-list for quantitative experiments (Pineau et al., 202I), the construction of elaborate dataset sheets (Gebru (et al.) 2018), and the creation of model cards (Mitchell et al., 2019). These efforts should not be viewed in competition and present valuable efforts in the rigorous specification of complementary aspects. On the one hand, the check-list includes imperative questions to assure that theorems and quantitative experiments are presented correctly, think of the check-mark for reported standard devi- ations and random seeds. In Table of Appendix [D] we summarize why it is crucial to still consider these aspects, why they are even particularly important in continual learning. On the other hand, dataset sheets and model cards present a verbose description of essential aspects to consider in the creation of datasets and models, with a particular focus on centanc and ethical aspects. We stress that these perspectives remain indispensable, as novel datasets and their variants are regularly suggested in continual learning and the CLEVA-COmpas does not disclose intended use with spect to ouman-centered application domains, ethical considerations, or their caveats. We give some examples for the latter statement in Appendix [ In addition to these central documents that capture primarily static complementary aspects of machine learning, the CLEVA- LLEVA-Comppss thus sheds light onto fundamental additional features inherent to continual learning practice.
Due to their complementarity, we believe it is best to report both the prior works of the above para- graph and the CLEVA-Compass together. In fact, collapsing thorough descriptions of datasets and ethical considerations into one compact visual representation would likely oversimplify these com- plex topies. However, we acknowledge that the present form of the CLEVVA-Compe contains room to grow, as it is presently largely tailored to statistical perspective of machine learn- ing. Important emerging works that factor in recent progress from fields such as causality would thus be hard to capture appropriately. We imagine that future CLEVA-Compar include more elements on the inner star plot, or include additional reporting measures on the outer level, once corresponding advances have matured, see Appendix C for our future vision.
nevertheless
updates may therefore
CONCLUSION
In this work, have detailed the complexity involved in continual learning comparisons, based on the challenges arising from a continuous workflow and the various influences drawn from re- lated machine learning paradigms. To provide intuitive means contextualize works with respect to the literature, improve long-term reproducibility and foster fair comparisons, we have introduced the Continual Learning EValuation Assessment (CLEVA) Compass. We encourage future contin- ual learning research to employ the visually compact CLEVA-Comper representation to promote transparent future discussion and interpretation of methods and their results.

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ACKNOWLEDGEMENTS
This work has been supported by the project "safeFBDC Financial Big Data Cluster" (FKZ: 01MK21002K), funded by the German Federal Ministry for Economics Affairs and Energy as part of the GAIA-x initiative, the Hessian Ministry of Higher Education, Research, Science and the Arts (HMWK) project "The Third Wave of AI," and the German Federal Ministry of Education and Research and HMWK within their joint support of the National Research Center for Applied Cybersecurity ATHENE.
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APPENDIX
We include four sections with additional information in the appendix. In Appendix A we clabo- rate further mathematical definitions for the measures on the outer CLEVA-Compass level, as described in Table [ of the main body. In Appendix [B] we provide a set of examples to further clarify the role of supervision in the various elements of the inner compass level. The five meth- ods in the example main body compass illustration of Figure are included in the discussion here, with additional examples serving the purpose to enhance the reader's s intuition of how supervision could manifest in the remaining possibilities. Our CLEVA-Compian and repository are then explained Section E Appendix [ provides further examples for, and underlines the indispensability of, com- plementary efforts with respect to the check- -ists, dataset sheets and model cards introduced in prior related works.
graphical user interface, code, Appendix [ Finally, as additional detail to the main body's 's
A CLEVA-COMPAR METRIC: MATHEMATICAL DEFINITIONS
We have motivated the measures to report in continual learning in the description of the outer CLEVA-Compass level in Table [ of the main body. There, we have also included references to prominent prior works that have outlined specific proposals. Although our work is not meant to serve as a full fledged review paper, we briefly quote corresponding mathematical definitions, in order to provide the reader with a more self-contained overview. For this purpose, we emphasize that the following provides conceivable ways to craft measures for specific applications, which typically considered to be classification. In multiple of the below cases, several ways to report the same concept are thus listed. However, note that definitions are mostly either compatible and the exact expression transformable, tailored to particular use case.
We remark that we attempt to stay as close as possible to the authors' original notation (with only minor modifications to ensure consistency and resolve potential ambiguity in variable overlap), in order to facilitate understanding for the reader when going back for a more in-depth look at the original works. To make the explicit mathematical expressions accessible, we thus also follow the assumption that variable refers to observed tasks in 1 = 1,...,,. T. This should not hinder generality of evaluation, as the knowledge of the task could be assumed be known for testing purposes or strict boundaries between tasks could practically be further relaxed in below definitions.
Per task metrics
Classification accuracy per task: Lopez-Paz & Ranzato 2017) consider constructing an accuracy matrix a E RTx,, where aig is the test classification accuracy of the model on the j-th task after observing the last sample from task i. The final average accuracy is then defined as:
1 T
ar
"Base", "new" & "all" classification accuracy: Accuracy is split into three task specific parts. Closely following the description of Kemker et al. (2018): Onew.t is the test accuracy for task f after task t has been learned, Olbase,t the test accuracy on the first task (base set) after new tasks have been leamed, Call,t is the test accuracy of all the test data for the classes seen up to this point, and Ocidaal is the offline accuracy on the base set, which is assumed be the ideal performance. These accuracies can then be used to define the normalized quantities between [0, 1]:
Olbsse.t
Diaee
(2)
T
Olideal
Onew
Onew,
(3)
T
1=2
1
Gall,
Dall
(4)
T
Olideal
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Forgetting
Forgetting quantifies the difference between the maximum knowledge gained about the task through- out the learning process in the past and the knowledge the model currently has about it. For classifi cation, Chaudhry et al. (2018) define forgetting for the j-th task after the model has been trained up to task as:
f] iE (E
max
Gij
dz.jo Vj L.
(5)
The average forgetting, after task has bee learned, is then defined as:
1
F.
f
(6)
1=1
Forward transfer
Classification forward transfer: The definition for per task accuracy has inspired expressions for forward and backward transfer (Lopez-Pai Pazzz-Pazz & Ranzato 2017). For consistency with the above no- tation, we employ the notation according Chaaudry et al. (2018). There, forward transfer (FWT) for classification is defined as the influence of the performance on future tasks j >> t when the model learning task
FWT 4t-1, b,.
where by is the accuracy on task j of a random baseline. The average forward transfer is then defined as:
FWT,
Σ J=2
FWT
Note, that is fixed to j- - in the expression for the average.
Learning Curve Area (LCA); LCA is inspired by above definitions. Chaudhry al. (2019) define the average b-shot performance after the model has been trained for all tasks as
T
%
t=1
where b is the mini-batch number. LCA at 3 is the area of the convergence curve Z₁ as a function b [0, 3]:
8
LCAs B +
Zy
Σ Zo.
(10)
B + 1 bm0
That is, LCAg the average 0-shot performance.
Online codelength: Motivated by the propositions of Kemker al. 2018) and Chaudhry et al. (2019), metric to measure the "adoption rate of an existing model to a new task is introduced in the context of natural language processing (Biesialska et al. 2021). Termed the online codelength E(D), a similar measure to area under the learning curve is given by:
N
((D) logg |v|
Σ
loga p (y: xs; 0D; .)
(11)
Here, |y/ is the number of possible class labels in the dataset D and Op.., are the parameters trained on a particular subset of the dataset.
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Backward transfer
Related to the concept of forward transfer for classification, backward transfer (BWT) describes the influence of the performance previous tasks t when the model is learning task t (Lopez-Paz Paz & Ranzato 2017:
Chaudhry et al. 2018):
BWT = at.j - Oj5
(12)
The average backward transfer can then be defined as:
BWT, = Σ BWT
1
(13)
Note that a positive value for BWT implies a retrospective improvement of task, whereas negative value for BWT coincides with quantification of model forgetting. Similar to the accuracy based definitions for forward and backward transfer, analogous expressions can be constructed on the basis of measuring task losses.
Openness
Classification openness: In the context of classification, Seasiffictionn,,eccierr et al. (2013) provide definition to capture the continuum between completely closed world approach, where train and test examples are fully known to belong to the investigated task, and an open world, where various unknown instances can be observed. They propose indicate the following quantity:
2 Nurain
0 Nust + NNtaget
(14)
This equation defines openness as a ratio between the number of classes used for training Nerain to the number of classes to be identified Ntarget and the number of classes used in testing Niest-
Openness as probability to encounter unknown instances: An alternative to the above definition for openness, particularly outside the context of classification, can be to indicate probability with which -usto--disttibution instances are encountered. For instance, Hendrycks & Dietterich (219) define probability for sample be perturbed corrupted. Similarly, Caccia et al. (2020) indicate a sampling probability that dictates how many data instances are drawn with different, currently unknown, class labels.
Other measures the outer CLEVA-Compass level
The majority of the other measures on the CLEVA-COmpass outer level are self-explanatory. For example, the size of the used dataset number of optimization steps simple scalar quantities to report. Nevertheless, there can exist alternatives to a straightforward report of e.g. number of model parameters or employed memory. For completeness, we mention some transformed alterna- tive forms for these raw measures, according to the proposition of Díaz- Rodriguz et al. (2018).
Model size efficiency: Instead of reporting the number parameters model over time, it may also be sensible to provide relative specification in terms of quantifying the growth of the model size:
Σ
Mem(0,)
1=
Mom (8,)
MS min
1,
(15)
N
with (0. describing the number of parameters of the model at time step
Samples storage size efficiency: Quantifying the memory used by the stored samples can similarly be reported in relation to the overall dataset size:
SSS min Σt
Mem( Mem D
(16)
N
Here, Mem(M,) (M,) ) is the size of the stored samples in the memory, and Mem (D) the size of the overall observed dataset.
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Computational efficiency: As combination of raw MAC operations and number of opti- mization steps, an additional quantity for computational efficiency can be expressed as the following ratio:
Opst
(ps(P.)
CE min 1,
(17)
N
Here, (Ds( is the number of operations needed to learn the (training) dataset Dr, and Ops 14 (Dt) is the number of operations required to do one forward and one backward pass on Dz.
B CATEGORIZATION OF SHOWN METHODS AND FURTHER EXAMPLES FOR THE CLEVA-COMPASS" INNER LEVEL
In the main body, Section 3 Figure we have provided an example CLEVA-COmpass illustration based on five continual learning methods. These methods have been deliberately picked empha- size the compass' utility and necessity in light of the large exhibited differences. Correspondingly, the inner and outer levels have been described in detail the main body, based on the related paradigms of Figure 3 and the detailed measures of Table [T] The example illustration highlights why the CLEVA-Compass is an important asset in identifying how methods relate in practice, ex- posing some of the typically unmentioned assumptions on set-up and evaluation, without requiring detailed mathematical understanding of proposed techniques. However, our chosen example meth- ods naturally only span a subset of overall possibilities. This is particularly true if recall that there is a distinction between an unsupervised and supervised approach for every individual element of the CLEVA- LLEVA--ss star plot, see the description in main body Section 3.1
In this appendix section, we thus provide examples for imaginable scenarios for every element on the inner compass level, both to enhance our intuition for the already shown methods and to empha- size why the distinction of supervision is crucial in the remaining settings. In the main body, we have included one such motivating example, an unsupervised continual density estimator, which in independence of being an unsupervised method itself, can have flexible requirements for supervi- sion with respect to e.g. training multiple models in the continual setting. We emphasize, that the primary goal of such examples is to provide further clarification and that they are not meant to be viewed as exclusive definitions. For this purpose, the below list contains examples with and without supervision for each compass element, going clockwise from the "six o' "clock" position of the com- pass and picking up the already visualized methods where applicable. Note that a short version of these examples is also provided in small "hover-over" tooltips in our CLEVA-Compp Graphical User Interface, which will be detailed in the upcoming section, Appendix C
Task agnostic: A method is said to be task agnostic if for prediction in a deployed model it does not require any additional information for which task the data instance originates from. A super- vised manifestation for such information, in independence of whether the task itself supervised or not, would be explicitly include time-step or task label into the learning process to condition the prediction. The visualized OSAKA and A-GEM methods are examples of this, where a "con- text" or respective -tasr-deseriptor" variable is explicitly conditioned unsupervised variant, or fully task-agnostic approach, would be able to inherently provide a correct prediction for any data instance from any previously observed task without any such information. In stark contrast, no mark on either supervised or unsupervised portion of the task agnostic star plot element would indicate that an approach is not capable of solving the task assignment challenge at all, implying that task oracle is required for prediction. The latter is a typical, sometimes unex- posed, assumption many continual learning works that focus on other challenges, such as VCL, but is often argued to be unrealistic.
and later inferred. A fully
Task order discovery: One way evaluate continual learning methods is to investigate the var- ious measures on the CLEVA-Compass in a fixed sequence of benchmark data, corresponding to no visual mark this star plot element for the majority of our example methods. An alternative, inspired by curriculum learning, would be to let the approach decide which task is meaningful to learn next. On the example of classifier, rather intuitively, if a method can choose an improved task
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order, it does in supervised fashion if it e.g. makes use of prospective class labels to distinguish which class would be best to learn next. An example of this is the visualized OCDVAE method, which constructs class specific meta-recognition model to assess similarity measure. On the flip side of this example, if the model discovers an improved task order hased only on e.g. divergences or distances in any constructed feature space that do not require labels to compute, the task order could be said to be unsupervised.
Active data query: A traditional fixed sequence benchmark setting has no active data query com- ponent, observable in the majority of the visualized compass examples. Similar to above task order discovery, but inspired from the perspective query data (in independence of whether data is available in pool, stream, etc.). This way, an approach would actively choose what data instances to include next into optimization. As such, a measure of utility for prospective inclusion of a data instance is generally constructed. Correspond- ingly, this utility measure can either require presence of supervision, as shown in the class-specific meta-recognition model of OCDVAE, can be entirely unsupervised.
active learning, an alternative could be to actively
Multiple modalities: If an approach only learns on one modality, such as text or images, then no indication of multi-modality marked in the CLEVA-Compass. This is the setting for four of our example compass methods, with exception of OCDVAE that handles transformed audio and visual data in supervised fashion. If the constructed system able to handle multiple sources, a distinction between whether the multi-modality aspect is unsupervised or supervised condenses to the difference between whether or not the system requires a label on which modality an instance originates from, e.g. to condition specific computation for this modality.
Open world: In an open world, the additional challenge for a learner is to robustly identify un- known, sometimes corrupted or perturbed, and potentially meaningless data instances. As an exam- ple for no indication of this aspect on the CLEVA-Compane the majority of blackbox deep learning methods do not inherently posses robust identification mechanisms, which the case in three of our shown example methods. If a mechanism is however included to recognize instances that deviate from the data distribution observed during training, then it would supervised if its conception requires class label, say classifier entropy or similar supervised quantity. OSAKA and OCDVAE are two examples for the latter, the first of which identifies anomalics with respect to a supervised loss and the second of which relies on identifying anomalies with respect to statistical meta- recognition model based on class-means A respective unsupervised example could be a difference in e.g. a reconstruction loss or divergence measure with respect arbitrary feature spaces.
Online: In an online setting, a learner faces the additional difficulty of not being allowed to revisit observed data instances. No mark on the respective compass star plot thus implies that data is revisited. In continual learning, this corresponds to the common setting where several tasks are learned in sequence, but within each task several "epochs" are trained to convergence, as practically conducted in all but one of the chosen example methods. When moving to the online setting, the increased stochasticity alongside with data drifts become an increasing challenge. Example methods to address this scenario, by making sure that a consistent model is trained, could then be supervised or unsupervised. For instance, one could regularize parameter deviations over time according to a supervised importance measure, assume supervised representation pre-training step in OSAKA, or rely on various unsupervised quantities, such as exponential moving averages.
Federated: As one conceivable distinction, and once more independently of a task to be solved, federated scenarios could be supervised or unsupervised with respect to the federated implementa- tion, akin to the examples of multiple models or modalities. For instance, we could assume that there exist large number of devices in network, but there are groups corresponding to different regions, different processing devices, labels on how many devices are participating actively, and so on. In that sense, communication in federated learning could provide additional supervised infor- mation that could be exploited to steer model training, optimize sub-model parts, or conversely go down a fully unsupervised route and simply average communicated updates without any label of the aforementioned characteristics. The example visualization if FedWelT is an instance of the latter unsupervised setting.
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Multiple models: We have provided an example for the role of supervision in of multiple models in the main body. We repeat this example here: multiple employed models could each solve supervised unsupervised task. In the supervision the level of multiple models, an additional mechanism could be required to index the appropriate model for prediction, such as proposed in our FedWelT example that employs specific attention based masks. If the correct model is automatically queried to provide correct predictions independently of which task an input belongs to this level is unsupervised. Finally, no visual mark on the compass corresponds to the scenario where only single model is used.
Uncertainty: Perhaps the least intuitive of the listed items, uncertainty could be believed to rep- resent inherently unsupervised quantity. We note however that uncertainty, especially in the way it is used in deep neural network approximations, is not always practically accurate and useful. As such, some uncertainty measures can typically require need for calibration in order to provide meaningful values, e.g. the entropy of classifier predictions (note that we do not delve into dispute of whether such a quantity should indeed be seen as a formal "uncertainty", but simply acknowl- edge the occurrence of such uses in the literature). Such calibration procedures can be interpreted as providing a supervised signal of what uncertainty "should look like". In contrast, if the method provides inherent uncertainties, such as argued in e.g. the fully Bayesian neural network viewpoint of VCL, can be said to be unsupervised. Naturally, many approaches also do not provide any uncertainty estimates at all.
Generative: Many methods that solve a specific task are not generative, e.g. consider a typical deep discriminative classifier of the form P(ylar), where denotes data and y denotes labels. A supervised generative variant would correspond to model that learns the joint distribution p(a, v) instead, whereas unsupervised generative models will learn or approximate only p(æ). Even when our objective is the classification example, the first of these variants will now also base decisions on the underlying nature of the data distribution. In current practice it is likely that an unsupervised generative model also primarily performs an unsupervised task (although gled representations might in principle and prospectively allow classification approaches without supervised training). Nevertheless, the other way around, the distinction is still important because not every unsupervised task necessarily requires a generative model. We have included the shown Bayesian approach of VCL as an unsupervised generative example. However, is worth mentioning that the authors also investigate a supervised variant in their work. As supervision could always be included as an additional term into fully unsupervised perspectives, we have marked the method on the outer unsupervised ring.
note that disentan-
An episodic memory is constructed to effectively rehearse so called exem- plars or prototype data instances. The challenge here is to assure that the constructed memory data subset is representative of the observed data as a whole. Intuitively, if a method does not employ an auxiliary episodic memory, no mark is indicated on the CLEVA-Compass. mechanism for this episodic memory relies on labels in the data, e.g. by approximating per class mean in OCDVAE, then it is supervised. fill the episodic memory by sampling random data instances, such as suggested by A-GEM, or by employing unsupervised algorithms such as k-means clustering, as practically proposed in VCL.
Episodic memory:
the construction
straightforward unsupervised example would be to
C CREATING A CLEVA-COMPASS: CODE, GRAPHICAL USER INTERFACE AND METHODS REPOSITORY
To make the CLEVA-Compas as accessible as possible and disseminate in a convenient way, we provide three options for practical use.
1. We provide LaTeX template for the CLEVA-Compast making use of the TikZ library to draw the compass within LaTeX. We envision that such template makes it easy for other authors include compass into their future submission, where they can adapt the naming and values of the entries respectively.
2. We further provide a Python script to generate the CLEVA-Compass. In fact, because the use of drawing in LaTeX with Tikz. may be unintuitive for some, we have written
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CLEVA Compass Generator
Example
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The CLEVA- CLEMA-Compass GUL With the help of this application, users can interactively customize and construct their CLEVA-Compass visualization or import existing ones.
Python script that automatically fills the above LaTeX template, so that it can later simply included into a LaTeX document. The Python script takes a path a ISON file that needs to be filled by the user with the CLEVA-Compas options. We further provide default JSON file that is easy to adapt.
3. To further lower the barrier for dissemination and use, we also provide CLEVA-Compass Graphical User Interface (GUI). The GUI makes easy for users simply "click to- gether" their desired compasses, save images or export to LaTeX, and conversely import already existing compass methods.
refer to our public repository for the outlined code and downloadable GUI: hitpps:/ggt thub. htw.mmmoommmmmmmmmmmmm/ aetomm/mlresearreeeeeeeee recearcch//oe ccc/CLEVA Compass
Code requirements and descriptions are explained in a corresponding README file there. In the following, concentrate the details of our CLEVA-Compir GUI. A visualization depicted Figure 5 We continue by explaining its main elements, functionality and its connection to the provided repository to accumulate methods.
Creating CLEVA-Compias entries: One of the key functionalities in the GUI is to quickly and intuitively add new methods (entries) into individual or existing CLEVA-Compaat visualizations (see paragraph below for loading of existing methods). For this purpose, the GUI contains Label field at the top left, intended to enter the method's name and authors, followed by the clickable op- tions for the items contained the inner and outer compass levels. As detailed in the explanation of the main body Section 3.1 this involves the level of supervision ticular measures that have been reported on the outer level. Upon clicking the Add Compass Entry button, the method will be visualized and will show up the bottom left list of visualized entries. The compass visualization will automatically adapt and scale with amount of entries (see also last paragraph for recommendations amount of methods and colors). Should user wish to adapt
the inner level and the par-
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or revise an entry, we offer buttons to Delete Compass Entry, Update Compass Entry, and Reload Preview. The remaining buttons provide various saving and loading functionality, detailed below.
We have detailed the levels and elements of the CLEVA-Compan throughout the main paper, with additional examples for further intuition. As it may be difficult to recall all the paradigm descriptions and described measures, we provide a "hover- over" functionality in the GUI. When placing the cursor above an item, e.g. specific continual learning metric detailed in Table IT a small box will show with the corresponding description. In the GUI Figure [ this is illustrated at the example of backward transfer. In this way, user is not require to swap back and forth between GUI and paper descriptions when designing their own CLEVA-Compass. In the same spirit, we have included small "example hints" for the inner level compass elements, in analogy to the descriptions of the ones provided in Appendix B
"Hover-over" tooltips for examples and hints:
Saving and exporting functionality: As described at the beginning of this section, there are mul- tiple interface choices to make the compass accessible. In this spirit, GUI also provides various exporting and saving functionalities. The most straightforward of these is the Export to Image button, which allows save the visualized CLEVA-COmpar in either SVG PNG formats. In correspondence with our Python script, we also enable the option to Export Entry to File, which we build-upon share and syne with our repository, detailed in the next paragraph. Lastly, make easy to include the Compass as TikZ code in a LaTeX document and thus allow for citation that is linked reference lists, we finally provide the functionality to Export to Tex file. The latter creates the necessary TikZ code that generates the compass when La LaTeX is compiled.
Loading and the CLEVA-Compase repository to accumulate methods: The final not yet de- scribed element of the GUI are the Import Entry from File(s) and Download Methods buttons. The Import Entry from File(s) functionality serves the purpose to enable users to load already existing CLEVA-Compass visualizations, in the form of loading their methods' JSON representations. As such, users will not have to replicate each and every method that has already been visualized in the CLEVA-Compass by hand. In addition to this, list of existing methods, which at the point of writing this paper consists of the five methods of the main body, is provided in our public repository. By using the Downloud Methods button the GUT will automatically synchronize the up-to-date list of available methods and enable interactive selection. Our vision is that prospective papers can contribute their own visualizations to this repository, so the amount of published methods and their representations grows into comprehensive repository. We strongly believe that this can help foster transparency in our community for prospective continual learning authors, but also in terms of creating more straightforward overview of the set-up and evaluation practices of continual learning approaches for application engineers and practitioners. As a side note, we note that this attempt at cataloguing works and their "rolling" aggregation is separate from proposing prospective adaptation and extensions of the CLEVA- CLEVA-Compass (think of the example of including causality in our main body's 's outlook). For such major content and functionality updates, we subjec- tively envision "discrete release" model, where prospective changes are encouraged to first undergo further stages of peer review, before being finally included into CLEEVA-Compass repository up- date. Although this may initially appear to slow down adoption of new methods, we argue in favor of this approach to limit the risk of a fixed set of researchers and a tiny portion of the community controlling such fundamental changes that steer the course of continual learning.
CLEVA-Compan
A note on number of methods and colors: Upon careful examination, one may note that we have chosen to include only six discrete colors as options for CLEVA-COmpass visualizations. Rather than adding e.g. a color wheel to pick various colors, this presents deliberate design choice. It is tied to the fact that we believe that there is little utility and value in visualizing more than six methods at maximum in one compass. The natural reason for this is that it becomes hard to distinguish methods after a certain point of overlap. However, we do not see this as a limitation, because star plots are well-known to provide excellent capabilities for visual comparison when placed side by side. In other words, when we place two LLEVA--oopasses side by side an easy and quick comparison is enabled, even if only one method were to be visualized in each.
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Table 2: Auxiliary items from the machine learning reproducibility check-list (left-column) (Pineau et al. 2021) and their prevailing relevance for continual learning (right column).
Paper reproducibility check-list: did you?
Additional importance for continual learning
Reproduction becomes even more challenging, descriptions alone tend to overlook nuances in the precise way data is sampled continuously and models are adapted over time.
include the code, data, and instructions needed to reproduce the main experimental results
Training details become particularly important, preeisc data sequences determine comparability and methods can be subject to additional hyper- parameters that are now tunable per task.
specify all the training details (e.g. data splits, hyper-parameters, how they were chosen)
In addition the inherent optimization stochas- ticity, many continual methods introduce further random components, c.g. in random data sub set extraction or task order randomization.
report error bars (e.g. with respect the random seed after running experiments multiple times)
Continual learning methods often judged by their ability to adapt knowledge quickly or com- pute updates on-the-fly on data streams, which is directly tied to employed compute environments.
include the amount of compute and the type of re- sources used type of GPUs, cluster, or cloud)
use existing or curate/release new assets (e.g.
Particular caution should be exercised empiri- cal continual investigations are frequently derived from available existing repositories and datasets.
code, data, models): cite the authors, mention the license, include any new assets, obtain consent
D IMPORTANCE OF EXISTING CHECK-LISTS AND DOCUMENTATION PROPOSALS FOR CONTINUAL LEARNING
We have introduced the CLEVA-COmpass and have motivated its utility in the paper's main body. At the same time, in Section 4 we point out that there are several prior works that remain indispens- able and can be considered orthogonal to our proposition. This due to the fact that these works have both a different focus in terms of their intended use and have chosen a significantly different presentation format. Whereas our CLEVA CVVA-CCopass provides a compact visual representation to contextualize continual approaches within related literature and contrasts their practical protocols, related works on model cards (Mitchell et al. 2019) and dataset sheets (Gebru et al.) 2018) adopt a more verbose approach towards specification of elements surrounding data and intended model use. In addition to these efforts, the machine learning conference reproducibility check-list (Pineau et al.) 2021 includes further general guidelines, that are meant to ground researchers in their practical ma- chine learning reporting. The corresponding check-list items illustrate more general best-practice items when dealing with machine learning experiments. We provide few examples in this Ap- pendix Section to give additional explanations of why both the CLEVA-COmpass and mentioned related works are complementary.
Machine learning reproducibility check-list: The items on the check-list have been proposed as guidelines towards general best-practice behavior in the context of recent reproducibility initiatives (Pineau et al. 2021). In particular, the main check-list's portion of empirical desiderata can be seen as at the center of commendable machine learning practice. This portion includes propositions for quantitative experiments, such as visualizing error bars, reporting random seeds, or detailing how hyper-parane param-parameters were tuned and chosen. analogous argument can be made with respect to the inclusion of assumption explanations and proofs for involved theoretical statements. Consequently, the cieck-list can be regarded as auxiliary to the CLEVA-Compass, as the latter does not capture elements of general good practice with respect to figures, central tendency in results, parameter tuning. In fact, we believe that many of the check-list items become even more important in the context of continual learning and the CLEVA-Compase In Table 2 we thus indicate the entailed additional importance of selected check-liss elements, when considered within continual learning.
hyper-
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Model cards: Mitchell et al. (2019) have suggested that every machine learning model should be accompanied with a so called model card. Such a model card is typically a one page overview, which summarizes: the choice of model, its intended use in terms of application, factors with respect to human-centric use, an indication of evaluated metrics, potential concrete results, along with any ethical considerations and caveats. In direct comparison with the CLEVA-COmpass, the perspec- tive of model card could be summarized as primarily being tailored towards a static view of the human-centric application intent, in order make deployment, limits, and ethical considerations more transparent and fair. The corresponding lengthy description provided in model cards is thus completely complementary the continual factors captured in the LLEVA-Compass, which do not reflect similar focus. In fact, posit that it is even necessary to provide more detailed specifi- cation for ethical concerns, as they are hard to capture in a compact visual representation. To pick up concrete examples, model cards encourage the textual descriptions for: "person or organization developing the model", 'model date, version and type". "license", "where to send questions", "in- tended use", human-centric "relevant factors including groups, instrumentation and environments" (i.e. personal characteristics, employed sensors, deployment environment), "unitary and intersec- tional results", "ethical considerations", "caveats", and on. These factors are orthogonal to the CLEVA-Compass, but remain equally important to consider.
Dataset sheets: Gebru et al. (2018) have proposed accompany every dataset with dataset sheet. Such document naturally complementary to the CLEVA-COmpass, as the latter merely captures the way in which data used across continuous tasks, but not the underlying pipeline behind dataset's creation and assembly. The prerequisite to specify the following aspects thus remains: composition, collection process, pre-processing, cleaning, labeling, distribution, maintenance, and ethical considerations. Dataset sheets are thus an important additional effort, which provide several questions and guidelines for standardized way to document the various elements surrounding the former dataset aspects. Any new continual learning benchmark should therefore continue to provide a comprehensively documented dataset sheet.
Finally, for the sake of completeness, we also mention that there exist recent propositions for continual lcarning software tools, such as the Avalanche (Lomonaco et al.) 2021) and Sequoia (Normandin et al. 2021) libraries top of the popular machine learning frame- work PyTorch (Paszke et 2019). These efforts are naturally auxiliary in the sense that they do not expose method's 'ss  context, its caveats, ethical considerations application intend, but rather just provide the means for pure replication of particular experiments (if data loading, methods, random seeds are properly specified in the code).
Software for replication:
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Reviews:
[{"review_id": "z4MT3NdXLv", "reviewer": "Reviewer_BGUM", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The work has nicely depicted different machine learning paradigms and their relationships with continual learning. Also it represents the difficulties and challenges arises from the continuous nature of this paradigm which makes the comparison of existing works challenging. The figures have efficiently summarize the main components in the paper. However, some main points of the research still remains unclear after reading the paper. The unclear parts are mentioned here:\n\n1) In CLEVA there are two inner and outer circles which show whether a component is achieved in a supervised way or without supervision. Even-though an example is provided further explanation of this two layers can be beneficial to get the importance and difference of this two levels.\n\n2) Highlighting influential paradigms and items that their extraction from text is challenging and sometimes misinterpreted is the only benefit of using CLEVA? The main positive effects and gains that CLEVA has still is not clear to me. \n\n3) Authors stated that CLEVA can not be used for indicating the context that the proposed continual learning works can be applied, or showing superiority of one work versus other than, therefore how can it be used for assessment? In other word, what are the main advantages of CLEVA which can encourage researchers to use that for presenting their works.\n\n\n  ", "summary_of_the_paper": "This work proposes CLEVA-compass which provides visual means to easily compare different continual learning works and provides a checklist to promote results reproducibility. Indeed, existing variations in the problem setup and evaluation of continual learning make the direct comparison of works in this field challenging. CLEVA tries to summarize each work in two different level: 1) each work's influential paradigms and items and 2) the measures reported in the work that can be used for reproducibly.", "summary_of_the_review": "The main motivation and the benefits of the proposed compass is not clear. As an assessment tool the main advantages that this work can provide to the community is not clear to me. The better comparison of four works in terms of this compass in the text could be one way to show the superiority of the world of using CLEVA versus not using it.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "lGojlzenebE", "reviewer": "Reviewer_RMSx", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The problem of relevant evaluation of continual learning approaches is a major one, and worth addressing. The authors do a great job presenting the different aspects of the evaluation (Fig 2) and explaining why they are meaningful. The distinction between static and continual evaluation is important, and definitely impacts the evaluation methods chosen and the metrics that can be used for comparing across them, as the authors rightly note.\nI particularly liked this statement: \"it is typical for approaches to be validated within train-val-test splits, where either a model-centric approach investigates optimizer variants and new model architectures, or alternatively, a data-centric approach analyzes how algorithms for data curation or selection can be improved for given models\" -- it is a great way of comparing and contrasting different approaches. (I would argue that many of the arguments that the authors raise for continual learning apply for ML in general to different extents, actually, since in practice ML approaches are deployed in a way that is similar to continual learning -- i.e. production-ready ML systems are a often tacitly a form of continual learning without explicitly stating so.\nI appreciated the open and transparent approach proposed for CLEVA. One thing I'm not 100% sure about is how practically it can be used, i.e. operationalized. This will undoubtedly become clearer when the code is released, but will it be via a UI or something similar? I feel like just showing the compass itself would not be enough for a full understanding of its scope, and how it can be applied. \nI do find that there is a gap between theory and practice in the current proposal (which is highlighted quite well by the authors in section 4), and additional work is needed in order to better plan out its application in practice. i.e. \"The CLEVA-Compass should thus not be used to conclude a method’s superiority and its utility will depend on faithful use in the research community.\" -- this is an important aspect of model evaluation that researchers will be looking for, and so if CLEVA doesn't allow it, this can get in the way of its widespread application.\nThis statement is not clear to me: \"We believe it is best to avoid attempts at combining the prior works of the above paragraph with\nthe CLEVA-Compass.\" -- why is this the case?\n", "summary_of_the_paper": "This paper proposes an approach for a more nuanced assessment of continual learning which provides a visual representation that enables the identification of a given method's context with regards to the broader literature, and enables the comparison of two methods in terms of reported metrics.", "summary_of_the_review": "This is an interesting approach to evaluating continual learning. The transparent and multi-faceted approach is interesting and unique, and can be truly useful for those working in the community.\nI feel that there is, however, a gap to be addressed between the more theoretical (and informative) usage of CLEVA and the more applied usage of it as a tool in guiding ML research. While section 4 of the paper does address this to some extent, the fact that the authors do not recommend using CLEVA to compare models, or alongside other works cited in that paragraph -- this would merit some additional thought and development.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "jDBSfiOt7pW", "reviewer": "Reviewer_JwDM", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strength:\n- The paper is very well structured, clearly explaining the authors' concerns about the evaluation in Continual Learning. This structure helps to motivate the problem and present the solution adequately.\n- I agree with the authors that comparing methods is not trivial. Different methods take different assumptions on the same problem, focusing on different metrics, which cause confusion and make the comparison tricky. CLEVA-Compass is a clever way to mitigate this problem by clearly identifying which metrics are reported and the contexts of the experiments.  \n- A significant problem raised by the authors is the transparency of the evaluation. For the most part, current incentives are to improve accuracy and decrease forgetfulness, often making unfair comparisons between methods or creating new metrics to \"surpass\" previous methods. Using CLEVA-Compass makes it more explicit about the context that each method is experimenting with, mitigating unfair comparisons.\n- The CLEVA-Compass visualization is an excellent way to summarize information in a single visualization. Comparing more than two methods on the same inner level can be confusing, but multiple visualizations can solve this problem.\n\nConcerns:\n- My main concern with CLEVA-Compass is the selection of the different paradigms and metrics of the inner and outer levels. I understand that this is a vision of the authors, showing the areas and metrics relevant to Continual Learning. However, Continual Learning is inevitably evolving and merging with new scenarios, for example, self-supervision. As mentioned in the paper, CLEVA-Compass can evolve to adapt, but this can encourage people to create new factors (or metrics) that benefit their proposed method. Understanding that the latter is part of what you want to avoid with CLEVA-Compass: How are you planning to adapt your compass continuously? Or do you hope that the community will manage these modifications?\n- A minor concern I have with CLEVA-Compass is the definition of the inner level points. I may not have understood it correctly, but I have two doubts:\n    1. The definitions of each paradigm depend only on the definition given in Figure 3? These points may have ambiguous areas where concepts overlap and thus generate confusion.\n    2. Regarding the 3 phases in the inner level, I think it can generate confusion when an unsupervised method is used in one area and not in another (as shown in the example of page 7, paragraph 1). If the area is ambiguous, in which part of the visualization the point is.\nI am not an expert in all paradigms mentioned, so this ambiguity may not exist. Still, it is a concern that I have.\n", "summary_of_the_paper": "In this work, the question arises of a better way to evaluate different methods in Continual Learning. The authors explain the problems of having a correct evaluation in Continual Learning, describing the complex scenario that Continual Learning confronts by being the intersection of multiple related areas. With this motivation, the authors present CLEVA-Compass or Continual Learning EValuation Assessment Compass, a visual representation that improves the comparison and transparency of different methods in Continual Learning. CLEVA-Compass includes two levels. The inner level shows which paradigms influence the method, and the outer level shows the setup and evaluation metrics presented in the method.", "summary_of_the_review": "The paper addresses a crucial topic in Continual Learning, and I think it may interest many people. Evaluating and comparing different methods transparently is relevant not only for Continual Learning but for all areas of Machine Learning, as mentioned by the authors. My only concern is the questions I leave to the authors, which I hope they can answer.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "_xXRwcZUmR", "reviewer": "Reviewer_FRys", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Pros:\n\nThe paper correctly identifies current challenges of defining CL research evaluations. The hypothesis that the abstract CL setting is a non-trivial multi-objective optimization problem has been well articulated in a review paper in Trends in Cognitive Sciences by Hadsell et al. 2020, but probably it has been given in several forms much earlier. The non-trivial term here has the technical meaning that no one solution exists which dominates all other solutions in terms of all objectives, hence trade-offs are needed. The only way to set trade-offs is to go to specific application domains and linearise the multiple objectives according to the needs of each domain. In that sense, the paper is correct that several rational definitions of CL are needed, assuming that the hypothesis outlined by Hadsell et al. is correct, as mounting evidence seems to suggest.\n\nThe paper coherently, albeit briefly, reviews the literature, ultimately arriving at a classification strategy. This type of review works help illuminate other sciences, e.g. neuroscience, and usually have dedicated publishing tracks in journals. Indeed, most practitioners in those disciplines relish review papers. Perhaps it is time for such tracks in machine learning main conferences, especially for subfields which deal with fundamental issues such as agreeing on definitions and evaluations.\n\nCons:\n\nNo formal system is given to precisely define terms, or at least outline how one should be developed. While this may be out of scope for one conference paper, the lack of such a system is an important source of confusion in CL research. At the end of the day any learning system should either optimise a precise criterion, at least approximately, or no criterion at all. This should be made clear upfront, with a discussion of all the assumptions such learning settings make.\n\n\n\nNote to authors:\n\nWhile the authors wish that the proposed categorization will not lead to a flurry of SOTA claims, it is safe to say that SOTA claims have already been made for most ways to cut the CL “cake”; even worse, SOTA claims are anecdotally thought to be a requirement for CL works to be published in main ML conferences. Hence, while the authors’ intentions are noble, the incentive structure may already be established against such hopes. At the end of the day, if quasi vacuous SOTA claims are what it takes for progress to be made in CL research, it is hard to argue against it, noble principles be damned. One may also take the view that SOTA claims made based on apple-oranges comparisons are not productive and may even stifle progress. However, the authors of this paper find a third option, proposing that such comparisons are productive exactly because they are correct if the “cake” is cut finely enough. While this entire paragraph of the review is somewhat in gest, it is included in order to serve as one of many possible subjective evaluations of the state of affairs in CL research, a path which was paved with good intentions.\n\nPerhaps a fourth possible solution to this conundrum would be to acknowledge limitations of current algorithms and evaluation metrics, and naturally replace grand claims with precisely defined setups which aim to solve CL problems which other ML researchers encounter today:\nFor example:\nSupervised or semi-supervised learning of very large-scale models on very large-scale (mostly unbalanced) and ad-hoc created datasets requires many passes through these datasets. Very little progress can be made in one pass, and SOTA continual learning algorithms do not seem to help.\nStandard deep reinforcement learning *is* a continual learning problem with its own set of trade-offs due to agents being required to sample their own training data. Yet most current CL research does not seem to apply or help in practice.\nMulti-agent RL is a highly complex data distribution shift problem. Training GANs or adversarially robust models against many types of attacks are natural dataset shift training scenarios, yet little help seems to be available from CL research.\n\nThe question arises: is a more structured classification of ongoing CL research into more fine-grained and fragmented  tracks going to improve the current state of affairs, making CL research more useful for ML in general? I personally find this question difficult to answer ahead of time, and will leave it for the research community.\n\n\nMisc:\nThe A-GEM reference on page 8 (Chaudhry et al., 2018) may not be entirely accurate. I believe A-GEM was introduced in (Chaudhry et al., 2019), perhaps a small confusion could have occurred there.\n", "summary_of_the_paper": "This paper argues that the goal of precisely formulating abstract desiderata for continual learning (CL) is ill-posed because different applications may always favour distinct scenarios. Following this logic, the paper proposes a categorisation system which aims to make CL research comparisons more structured. The paper goes on to review the challenges of meaningful evaluation of CL algorithms, particularly in terms of systematic comparisons of different works with somewhat different sets of assumptions, using the notion of “catastrophic forgetting” as a working example. Aspects which sometimes are overlooked in other machine learning research, e.g. dataset preprocessing and balancing, become important aspects in CL research and cannot be ignored. Furthermore, many machine learning paradigms may have natural extensions to non-i.i.d. learning scenarios, or at least various multi-task formulations of interest. Since many of these satellite formulations inherit metrics and assumptions of their respective paradigms, it naturally creates a problem formulation challenge for CL research.\nThe paper proposes a multi-objective radar/spider web chart (CLEVA-Compass) as a classification heuristic for CL research evaluation setups. The diagram is based on a well conceived collection of conceptual and practical desiderata for CL algorithms. The paper proposes how such a diagram could be used by future CL research and anticipates unintended uses. Limitations of the proposed classification strategy, as well as complementary challenges of dataset design, are discussed towards the end of the paper.\n", "summary_of_the_review": "I believe it is timely to allow review papers into main ML conferences, so on balance I vote for acceptance. It is worth mentioning that several position papers cited in the text, e.g. Pineau et al. 2021, Mitches et al. 2019, Bender & Friedman, 2018 have all been published in journals and/or conference proceedings. \n", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "Not applicable"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This review paper presents a way of comparative assessment of continual learning. Reviewers all agreed that this work is interesting, unique with comprehensive coverage of the CL space. The proposed categorization, CLEVA-Compass, and its GUI have great potential to facilitate future CL work.
Decision: Accept (Poster)