TAG: Task-based Accumulated Gradients for Lifelong learning

1 INTRODUCTION . Lifelong learning ( LLL ) , also known as continual learning , is a setting where an agent continuously learns from data belonging to different tasks ( Parisi et al. , 2019 ) . Here , the goal is to maximize performance on all the tasks arriving in a stream without replaying the entire datasets from past tasks ( Riemer et al. , 2018 ) . Approaches proposed in this setting involve investigating the stability-plasticity dilemma ( Mermillod et al. , 2013 ) in different ways where stability refers to preventing the forgetting of past knowledge and plasticity refers to accumulating new knowledge by learning new tasks ( Mermillod et al. , 2013 ; De Lange et al. , 2019 ) . Unlike human beings , who can efficiently assess the correctness and applicability of the past knowledge ( Chen & Liu , 2018 ) , neural networks and other machine learning models often face various issues in this setting . Whenever data from a new task arrives , these models often tend to forget the previously obtained knowledge due to dependency on the input data distribution , limited capacity , diversity among tasks , etc . This leads to a significant drop in performance on the previous tasks - also known as catastrophic forgetting ( McCloskey & J. Cohen , 1989 ; Robins , 1993 ) . Recently there has been an ample amount of research proposed in LLL ( De Lange et al. , 2019 ) . Several methods , categorized as Parameter Isolation methods , either freeze or add a set of parameters as their task knowledge when a new task arrives . Another type of methods , known as Regularization-based methods , involve an additional regularization term to tackle the stability-plasticity dilemma . There are approaches based on approximate Bayesian inference , where parameters are sampled from a distribution , that suggest controlling the updates based on parameter uncertainty ( Blundell et al. , 2015 ; Adel et al. , 2019 ; Ahn et al. , 2019 ) . But these approaches are computationally expensive and often depend on the choice of prior ( Zenke et al. , 2017 ; Nguyen et al. , 2018 ) . Another class of methods , namely Replay-based methods , store a subset of examples from each task in a replay buffer . These methods apply gradient-based updates that facilitate a high-level transfer across different tasks through the examples from the past tasks that are simultaneously available while training . As a result , these methods tend to frame LLL as an i.i.d . setting . While replay-based methods are currently state-of-the-art in several LLL tasks , it is important that we explore various ways to tackle the original non-i.i.d . problem ( Hadsell et al. , 2020 ) . Hence the focus of this paper is to design efficient replay-free methods for LLL . While adaptive gradient descent based optimizers such as Adam ( Kingma & Ba , 2014 ) have shown superior performance in the classical machine learning setup , many existing works in LLL employ the conventional stochastic gradient descent for parameter update ( Lopez-Paz & Ranzato , 2017 ; Chaudhry et al. , 2019 ; Farajtabar et al. , 2020 ) . Adaptive gradient descent based optimizers accumulate gradients to regulate the magnitude and direction of updates but often struggle when the dataset arrives in a non-i.i.d . manner , from different tasks , etc . These optimizers tend to ‘ over-adapt ’ on the most recent batch and hence suffer from poor generalization performance ( Keskar & Socher , 2017 ; Chen et al. , 2018 ; Mirzadeh et al. , 2020 ) . While exploiting the adaptive nature of the gradient descent based optimizers , we alleviate catastrophic forgetting by introducing Task-based Accumulated Gradients ( TAG ) that is a wrapper around existing optimizers . The key contributions of our work are as follows : • We define a task-aware adaptive learning rate for the parameter update step that is also aware of relatedness among tasks in LLL . • As the knowledge base , we propose to additively accumulate the directions ( or gradients ) that the network took while learning a specific task instead of storing past examples . • We empirically show that our method prevents catastrophic forgetting and exhibits knowledge transfer if the tasks are related without introducing more parameters . Our proposed method , described in Section 3 , achieves state-of-the-art results and outperforms several Replay-free methods on complex datasets like Split-miniImageNet , Split-CUB , etc . For smaller episodic memory , our method also outperforms the Replay-based methods as shown in Section 4 . Note that we propose a new approach to optimizing based on the past gradients and as such it could potentially be applied along with the existing LLL methods including replay-based methods . We demonstrate the effectiveness of doing the same in the experiments . 2 RELATED WORK . Methods proposed in LLL are broadly categorized into three classes : Regularization-based , Parameter Isolation and Replay-based methods ( De Lange et al. , 2019 ; Masana et al. , 2020 ) . Regularization-based methods prevent a drastic change in the network parameters as the new task arrives to mitigate forgetting . These methods further are classified as data-focused ( Li & Hoiem , 2017 ; Triki et al. , 2017 ) and prior-focused methods ( Nguyen et al. , 2018 ; Ebrahimi et al. , 2019 ) . In particular , Elastic Weight Consolidation ( EWC ) ( Kirkpatrick et al. , 2017 ) , a prior-focused method , regularizes the loss function to minimize changes in the parameters important for previous tasks . Yet , when the model needs to adapt to a large number of tasks , the interference between task-based knowledge is inevitable with fixed model capacity . Parameter Isolation methods ( Rusu et al. , 2016 ; Xu & Zhu , 2018 ; Serra et al. , 2018 ) such as ( Aljundi et al. , 2017 ) assign a model copy to every new task that arrives . These methods alleviate catastrophic forgetting in general , but they rely on a strong base network and work on a small number of tasks . Another closely related methods , called Expansion-based methods , handle the LLL problem by expanding the model capacity in order to adapt to new tasks ( Sodhani et al. , 2018 ; Rao et al. , 2019 ) . Li et al . ( 2019 ) propose to learn task-specific model structures explicitly while retaining model primitives sharing , decoupling from model parameter estimation . Replay-based methods maintain an ‘ episodic memory ’ , containing a few examples from past tasks , that is revisited while learning a new task ( Riemer et al. , 2018 ; Jin et al. , 2020 ) . For instance , Averaged Gradient Episodic Memory ( A-GEM ) ( Chaudhry et al. , 2018b ) , alleviating computational inefficiency of GEM ( Lopez-Paz & Ranzato , 2017 ) , uses the episodic memory to project the gradients based on hard constraints defined on the episodic memory and the current mini-batch . Experience Replay ( ER ) ( Chaudhry et al. , 2019 ) uses both replay memory and input mini-batches in the optimization step by averaging their gradients to mitigate forgetting . Task-relatedness ( Li & Hoiem , 2017 ; Jerfel et al. , 2019 ; Shaker et al. , 2020 ) or explicitly learning task representations ( Yoon et al. , 2017 ) is also an alternative approach studied in LLL . Efficient Lifelong Learning Algorithm ( ELLA ) ( Ruvolo & Eaton , 2013 ) maintains sparsely shared basis vectors for all the tasks and refines them whenever the model sees a new task . Rao et al . ( 2019 ) perform dynamic expansion of the model while learning task-specific representation and task inference within the model . Orthogonal Gradient Descent ( OGD ) ( Farajtabar et al. , 2020 ) maintains a space based on a subset of gradients from each task . As a result , OGD often faces memory issues during run-time depending upon the size of the model and the subset ( Bennani & Sugiyama , 2020 ) . Unlike OGD , we accumulate the gradients and hence alleviate the memory requirements by orders for each task . A recent work ( Mirzadeh et al. , 2020 ) argues that tuning the hyper-parameters gives a better result than several state-of-the-art methods including A-GEM and ER . They introduce Stable SGD that involves an adjustment in the hyper-parameters like initial learning rate , learning rate decay , dropout , and batch size . They present this gain in performance on simplistic benchmarks like Permuted MNIST ( Goodfellow et al. , 2013 ) , Rotated MNIST and Split-CIFAR100 ( Mirzadeh et al. , 2020 ) . Another related work ( Gupta et al. , 2020 ) also employs an adaptive learning rate while requiring a small episodic memory . But it is based on a meta-learning setting and hence beyond the scope of our paper . 3 METHOD . 3.1 LIFELONG LEARNING SETUP . In this section , we introduce the notations and the LLL setup used in the paper . We focus on the standard task-incremental learning scenario which is adopted in the numerous state-of-the-art LLL methods . It involves solving new tasks using an artificial neural network with a multi-head output where each head is associated with a unique task and the task identity is known beforehand ( Lopez-Paz & Ranzato , 2017 ; van de Ven & Tolias , 2019 ) . We denote the current task as t and any of the previous tasks by τ . The model receives new data of the form { X ( t ) , D ( t ) , Y ( t ) } where X ( t ) are the input features , D ( t ) is the task descriptor ( that is a natural number in this work ) and Y ( t ) is the target vector specific to the task t. We consider the ‘ single-pass per task ’ setting in this work following ( Lopez-Paz & Ranzato , 2017 ; Riemer et al. , 2018 ; Chaudhry et al. , 2019 ) . It is more challenging than the multiple pass setting used in numerous research works ( Kirkpatrick et al. , 2017 ; Rebuffi et al. , 2017 ) . The goal is to learn a classification model f ( X ( t ) ; θ ) , parameterized by θ ∈ RP to minimize the loss L ( f ( X ( t ) ; θ ) , Y ( t ) ) for the current task t while preventing the loss on the past tasks from increasing . We evaluate the model on a held-out set of examples of all the tasks ( ≤ t ) seen in the stream . 3.2 TASK-BASED ACCUMULATED GRADIENTS . The specific form of our proposed method depends on the underlying adaptive optimizer . For ease of exposition , we describe it as a modification of RMSProp ( Tieleman & Hinton , 2012 ) here and call it TAG- RMSProp . The TAG versions of other methods such as the Adagrad ( Duchi et al. , 2011 ) and Adam are available in Appendix A.1 . A Naive RMSProp update , for a given learning rate η , looks like the following : Vn = βVn−1 + ( 1− β ) g2n ; 1 ≤ n ≤ N θn+1 = θn − η√ Vn + gn ( 1 ) where θn is the parameter vector at step n in the epoch , gn is the gradient of the loss , N is the total number of steps in one epoch , Vn is the moving average of the square of gradients ( or the second moment ) , and β is the decay rate . We will use TAG-optimizers as a generic terminology for the rest of the paper . We maintain the second moment V ( t ) n for each task t in the stream and store it as the knowledge base . When the model shifts from one task to another , the new loss surface may look significantly different . We argue that by using the task-based second moment to regulate the new task updates , we can reduce the interference with the previously optimized parameters in the model . We define the second moment V ( t ) n for task t for TAG-RMSProp as : V ( t ) n = β2V ( t ) n−1 + ( 1− β2 ) g2n where β2 is constant throughout the stream . We use V ( t ) n to denote a matrix that stores the second moments from all previous tasks , i.e. , V ( t ) n = { V ( 1 ) N , ... , V ( t−1 ) N , V ( t ) n } of size ( t × P ) . Hence , the memory required to store these task-specific accumulated gradients increases linearly as the number of tasks in the setting . Note that each V ( τ ) N ( where τ < t ) vector captures the gradient information when the model receives data from a task τ and does not change after the task τ is learned . It helps in regulating the magnitude of the update while learning the current task t. To alleviate the catastrophic forgetting problem occurring in the Naive RMSProp , we replace Vn ( in Eq . 1 ) to a weighted sum of V ( t ) n . We propose a way to regulate the weights corresponding to V ( t ) n for each task in the next section .

Summary: The authors present an approach for lifelong learning where each task is processed in a single pass. They propose to adapt the learning rate of the training algorithm depending on the current task's similarity to the previously observed tasks. The learning rate is decreased when there are many dissimilar tasks to avoid catastrophic forgetting. The paper presents an adapted RMSProp algorithm, but the procedure can be adjusted to Adagrad and Adam as well. The presented evaluation on computer vision datasets shows that the proposed modification helps to increase the final accuracy of those methods while keeping the forgetting low. When compared to baselines from literature under the conditions of the single pass setting, the proposed method achieves better final accuracy and keeps approximately the same forgetting rate.