Design and Evaluation for Robust Continual Learning

1 INTRODUCTION . Continual learning is the ability to learn from new non-stationary data without catastrophic forgetting ( McCloskey & Cohen , 1989 ; Goodfellow et al. , 2013 ; Kemker et al. , 2018 ) previously learned experiences ( Thrun & Mitchell , 1995 ; Hsu et al. , 2018 ; Parisi et al. , 2019 ) . Unlike transfer learning approaches which are focused on one-directional transfer from a source to a target task ( Bengio , 2012 ; Yosinski et al. , 2014 ) and are not concerned with forgetting the source task , continual learning requires the model to maintain acceptable performance for the source task while learning the new target task . Various techniques have been developed to tackle catastrophic forgetting ; however , the majority of the methods require a task oracle ( identifier ) to identify which task each input example belongs to ( Kirkpatrick et al. , 2017 ; Shin et al. , 2017 ; Sener & Koltun , 2018 ) . Often in experimental evaluations , a human plays the role of the oracle , with no intrinsic justification for the choice of tasks . Typical evaluations of continual learning methods generate synthetic tasks by splitting or permuting the input data and presenting the resulting tasks in sequence ( van de Ven & Tolias , 2019 ; Aljundi et al. , 2019a ) . For example , in SplitMNIST , a common dataset sequence used in continual learning , the ten digits are partitioned into chunks composed of two or more digits . Often , the data is split into five chunks , and each chunk has only two digits to classify . A task oracle ( identifier ) may label the chunks of the incoming data sequence as tasks . Specifically , we use chunk to avoid implicitly assuming these matches with task labels as this may provide unintentional information for performance boost . We may still choose a task oracle to have the same sequence but will need to be cognizant of the assumption . In Section 2 , we propose six rules to help design experiments to test and understand these assumptions . In general , a task oracle is not available , and a clear separation of tasks is unlikely to be defined or known ( Caccia et al. , 2020 ) . Real-world applications are unlikely to exhibit strict sequencing of tasks ; instead , they may exhibit mixing and transitioning between input distributions . It is important to be careful with assuming having a task oracle as the extra information provided will unfairly bias the evaluation of continual learning methods . Hence , in this work , we consider a variety of input sequences that may occur in a continual learning problem . We demonstrate the more difficult scenario , which we called unrestricted scenario , where only limited task information is supplied to the methods during training . Farquhar & Gal ( 2018 ) and van de Ven & Tolias ( 2019 ) suggest that continual learning research should shift away from task oracle-based methods . Although we share this sentiment , we do believe that a task oracle may still play a role in continual learning research , despite limiting real-world applications . However , researchers must apply robust experimental design to surface assumptions about learning scenarios . In this work , we investigate the effects of task identification and experimental protocols in continual learning . Firstly , we propose six rules for robust evaluation of continual learning methods . Secondly , we extend published continual learning methods to support non-sequential tasks and evaluate the methods with a more extensive range of input arrival sequences and task partitionings to demonstrate the shortcoming of existing assumptions and protocols . We show a sequential split input sequence is most prone to catastrophic forgetting and current experimental protocols may not be evaluating the continual learning methods fairly . Finally , we discuss the application of the six rules on experimental design and interpretation of new experiments . We suggest the continual learning researchers should consider three different sequences : data , task identifier and evaluation identifier when designing experiments . 2 EXPERIMENTAL DESIGN FOR CONTINUAL LEARNING . Experimental and implementation choices have a significant effect on the performance of continual learning methods . While it is infeasible to consider all choices and their effects , it is still desirable to be able to fairly compare different methods and interpret the results . We propose six rules to aid in the design of robust experiments for continual learning . 2.1 RULE 1 : TEST EXTREMES . Although it is infeasible to test all possible setups , experiments should cover extreme settings that can test the continual learning method to give a more expository understanding of the methods . When we allow different ordering of experimental sequences , there is an exponential number of possible sequences of data . In particular , the effects of the methods should compare to extreme settings . Continual learning experiments consider the sequential setting where the data are split into different sequences where each chunk of the split contains only a subset of classes . For example , splitting should be randomized rather than following some chosen order . In this case , we consider the extreme of the composition of the sequence , that is , grouping classes and their order of arrival to be random rather than alphabetical , numerical , or human categorization ( cognitive or artificial ) . Typical experimental settings for continual learning consist of data splitting into clearly defined chunks where each contains only certain classes . The extreme in this case is the separation of tasks . Most continual learning experiments only consider the case where tasks are perfectly separated into chunks . Other separations in different extremes should also be considered , such as where the data is random , and all tasks may be present in every batch ( i.e . the normal supervised learning setting ) . This extreme provides a baseline and ensures that the method does not rely on certain signals from the sequence ’ s setup . Rule 1 : Identify and test extreme settings in experimental setup and parameters . 2.2 RULE 2 : USE CLEAR BASELINES . Results derived from experiments with a dependency on the data sequence can vary depending on how the data sequence is presented . Implementation or experimentation choices that seem arbitrary may , in fact , have a significant effect that is difficult to predict . Even when an analytical derivation may be possible , the work may be tedious and ad hoc . Hence , we propose including a clearly identified baseline equivalent to a ’ random guess ’ for each experimental scenario . In experimental protocols for continual learning , extra information may be provided to the methods such that random guess baseline performance with the same extra information may not match our intuition . This means that providing a random guess classifier conforming to the experimental protocol is important to demonstrate the performance improvement attributable to the method . A concrete example of this can be seen in the multi-head task scenario experiment used in many studies ( von Oswald et al. , 2020 ; Lopez-Paz et al. , 2017 ; van de Ven & Tolias , 2019 ; Mirzadeh et al. , 2020 ) . For example , when splitting CIFAR100 into 10 tasks where each task has 10 classes , a task label is provided that limits the number of classes available to the classifier . One might naively think that random guess is 1 % ( 1 out 100 classes ) , but due to experimental setup , the baseline is 10 % ( 1 out 10 classes ) . Predicting random guess performance is difficult in more complicated experimental setups . Hence , providing a random guess classifier helps to identify the baseline of an experimental setup with ease . Rule 2 : Provide random guess implementations that conform to experimental setup for clear identification of baselines of the setting . 2.3 RULE 3 : TEST SIMPLE METHODS . Due to the extra information that naturally arises from the experimental setup in continual learning literature , it is important to provide simple methods that utilize this information . Many continual learning methods inherently uses task information which is usually not obvious . There is an attempt to move away from this paradigm towards a task-agnostic approach ( Lee et al. , 2020 ; Zeno et al. , 2018 ; Aljundi et al. , 2019a ) . However , regardless of the fairness or applicability of providing task information , when they are provided , other simple methods which also use this information should be compared to . Naive multi-model , where each model is assigned a task , is a possible simple method to consider when task information is given . Each individual model is independent and there is no sharing between the models . The experiment supplies a task label for each input example , hence we can pick the model associated with the right task . This method uses the same information given to the continual learning methods to which we want to compare . Naive multi-model may also be used in a task-agnostic setting such as using entropy from prediction as a proxy of task label or task change . All models will give an output for each example . The model with the lowest entropy output is used for classification for the example . Some continual learning researchers consider naive multi-model as the upper bound result that can not be outperformed ( Mirzadeh et al. , 2020 ; Schwarz et al. , 2018 ; Chaudhry et al. , 2019 ; He et al. , 2019 ; Titsias et al. , 2020 ) . However , we believe that this simply demonstrates that continual learning literature needs better experimental protocols . Naive multi-model uses the same information as task oracle based methods but is simpler and can not make use of sharing to help combat forgetting and improve the learning of new data von Oswald et al . ( 2020 ) , hence it should not be considered an upper bound . Some multi-task learning where tasks are considered as different objective functions ( rather than as splitting data ) do exhibit improvement through sharing Kendall et al . ( 2018 ) . In this case , each input has multiple different objectives and using a shared model is better than multiple individual models . Rule 3 : Evaluate naive or simple methods for the experimental setting . 2.4 RULE 4 : RE-EVALUATE EXISTING BEHAVIOR . Often we assume that once some behavior of a model is established , it will continue to hold in further training and testing . However , this assumption is far from the empirical results . A clear example of this is continual learning itself where forgetting happens greatly ( Kirkpatrick et al. , 2017 ) . The same data with the same training length and setup but with slightly different ordering can greatly affect the results of the models . This rule when applied to continual learning considers re-evaluating whether the previous task is learned . Continual learning experiments rarely test learning behavior by revisiting the same task . Hence , we propose to consider an experimental setup where task is recapitulated during training and how this may affect the proposed methods . One consequence of this rule is to require that methods can correctly identify data that they have previously seen , rather than considering all new data as a new task . In methods that rely on task labels , this is not an issue since task labels will be provided for the recapitulating data . However , for task-agnostic methods that must provide their own means to separate data , this becomes important . This test is useful in detecting whether task-agnostic methods can reuse their previously learned components for new data of the same task or even previously seen data . Reusing existing components is essential , since the efficiency of training is better when we use generalized features that are already learned by the model . It also impacts the evaluation process , as in the task-agnostic setting , methods are greatly affected by the choice of task . For example , if there is a task-agnostic naive multi-model method then selecting the right model will significantly improve the prediction accuracy . Model size and memory usage will continuously grow if the method is unable to detect previously seen tasks . In the extreme , every new data may be considered a new task and the method may simply remember every example . This is a problem for many continual learning methods that rely on task boundary changes ( Rebuffi et al. , 2017 ; Kirkpatrick et al. , 2017 ; von Oswald et al. , 2020 ) . Rule 4 : Perform experiments with settings that recapitulate incoming data .

Summary: This work proposes 6 rules for continual learning experiments. Specifically, they propose to  - 1) test for extreme continual learning settings,  - 2) report random guess performance  - 3) evaluate naive/simple methods  - 4) revisit old data  - 5) test for different task orders and  - 6) define performance that derive from the data.  Then,  pan empirical study on different scenarios that emerge from the combinations of those rules is provided.