NASOA: Towards Faster Task-oriented Online Fine-tuning

1 INTRODUCTION . Fine-tuning using pre-trained models becomes the de-facto standard in the field of computer vision because of its impressive results on various downstream tasks such as fine-grained image classification ( Nilsback & Zisserman , 2008 ; Welinder et al. , 2010 ) , object detection ( He et al. , 2019 ; Jiang et al. , 2018 ; Xu et al. , 2019 ) and segmentation ( Chen et al. , 2017 ; Liu et al. , 2019 ) . Kornblith et al . ( 2019 ) ; He et al . ( 2019 ) verified that fine-tuning pre-trained networks outperform training from scratch . It can further help to avoid over-fitting ( Cui et al. , 2018 ) as well as reduce training time significantly ( He et al. , 2019 ) . Due to those merits , many cloud computing and AutoML pipelines provide fine-tuning services for an online stream of upcoming users with new data , different tasks and time limits . In order to save the user ’ s time , money , energy consumption , or even CO2 emission , an efficient online automated fine-tuning framework is practically useful and in great demand . Thus , in this work , we propose to explore the problem of faster online fine-tuning . The conventional practice of fine-tuning is to adopt a set of predefined hyperparameters for training a predefined model ( Li et al. , 2020 ) . It has three drawbacks in the current online setting : 1 ) The design of the backbone model is not optimized for the upcoming fine-tuning task and the selection of the backbone model is not data-specific . 2 ) A default setting of hyperparameters may not be optimal across tasks and the training settings may not meet the time constraints provided by users . 3 ) With the incoming tasks , the regular diagram is not suitable for this online setting since it can not memorize and accumulate experience from the past fine-tuning tasks . Thus , we propose to decouple our faster fine-tuning problem into two parts : finding efficient fine-tuning networks and generating optimal fine-tuning schedules pertinent to specific time constraints in an online learning fashion . 1The efficient training model zoo ( ET-NAS ) has been released at : https : //github.com/NAS-OA/ NASOA Recently , Neural Architecture Search ( NAS ) algorithms demonstrate promising results on discovering top-accuracy architectures , which surpass the performance of hand-crafted networks and saves human ’ s efforts ( Zoph et al. , 2018 ; Liu et al. , 2018a ; b ; Radosavovic et al. , 2019 ; Tan et al. , 2019b ; Real et al. , 2019a ; Tan & Le , 2019 ; Yao et al. , 2020 ) . However , those NAS works usually focus on inference time/FLOPS optimization and their search space is not flexible enough which can not guarantee the optimality for fast fine-tuning . In contrast , we resort to developing a NAS scheme with a novel flexible search space for fast fine-tuning . On the other hand , hyperparameter optimization ( HPO ) methods such as grid search ( Bergstra & Bengio , 2012 ) , Bayesian optimization ( BO ) ( Strubell et al. , 2019a ; Mendoza et al. , 2016 ) , and BOHB ( Falkner et al. , 2018 ) are used in deep learning and achieve good performance . However , those search-based methods are computationally expensive and require iterative “ trial and error ” , which violate our goal for faster adaptation time . In this work , we propose a novel Neural Architecture Search and Online Adaption framework named NASOA . First , we conduct an offline NAS for generating an efficient fine-tuning model zoo . We design a novel block-level and macro-structure search space to allow a flexible choice of the networks . Once the efficient training model zoo is created offline NAS by Pareto optimal models , the online user can enjoy the benefit of those efficient training networks without any marginal cost . We then propose an online learning algorithm with an adaptive predictor to modeling the relation between different hyperparameter , model , dataset meta-info and the final fine-tuning performance . The final training schedule is generated directly from selecting the fine-tuning regime with the best predicted performance . Benefiting from the experience accumulation via online learning , the diversity of the data and the increasing results can further continuously improve our regime generator . Our method behaves in a one-shot fashion and doesn ’ t involve additional searching cost as HPO , endowing the capability of providing various training regimes under different time constraints . Extensive experiments are conducted on multiple widely used fine-tuning datasets . The searched model zoo ET-NAS is more training efficient than SOTA ImageNet models , e.g . 5x training faster than RegNetY-16GF , and 1.7x faster than EfficientNetB3 . Moreover , by using the whole NASOA , our online algorithm achieves superior fine-tuning results in terms of both accuracy and fine-tuning speed , i.e . improving around 2.1 % accuracy than the best performance in RegNet series under various tasks ; saving 40x computational cost comparing to the BOHB method . In summary , our contributions are summarized as follows : • To the best of our knowledge , we make the first effort to propose a faster fine-tuning pipeline that seamlessly combines the training-efficient NAS and online adaption algorithm . Our NASOA can effectively generate a personalized fine-tuning schedule of each desired task via an adaptive model for accumulating experience from the past tasks . • The proposed novel joint block/macro level search space enables a flexible and efficient search . The resulting model zoo ET-NAS is more training efficient than very strong ImageNet SOTA models e.g . EfficientNet , RegNet . All the ET-NAS models have been released to help the community skipping the computation-heavy NAS stage and directly enjoy the benefit of NASOA . • The whole NASOA pipeline achieves much better fine-tuning results in terms of both accuracy and fine-tuning efficiency than current fine-tuning best practice and HPO method , e.g . BOHB . 2 RELATED WORK . Neural Architecture Search ( NAS ) . The goal of NAS is to automatically optimize network architecture and release human effort from this handcraft network architecture engineering . Most previous works ( Liu et al. , 2018b ; Cai et al. , 2019b ; Liu et al. , 2018a ; Tan et al. , 2019a ; Xie et al. , 2019 ; Howard et al. , 2019 ) aim at searching for CNN architectures with better inference and fewer FLOPS . Baker et al . ( 2017 ) ; Cai et al . ( 2018 ) ; Zhong et al . ( 2018 ) apply reinforcement learning to train an RNN controller to generate a cell architecture . Liu et al . ( 2018b ) ; Xie et al . ( 2019 ) ; Cai et al . ( 2019b ) try to search a cell structure by weight-sharing and differentiable optimization . Tan & Le ( 2019 ) use a grid search for an efficient network by altering the depth/width of the network with a fixed block structure . On the contrary , our NAS focuses creating an efficient training model zoo for fast fine-tuning . Moreover , the existing search space design can not meet the purpose of our search . Generating Hyperparameters for Fine-tuning . HPO methods such as Bayesian optimization ( BO ) ( Strubell et al. , 2019a ; Mendoza et al. , 2016 ) , BOHB ( Falkner et al. , 2018 ) achieves very promising result but require a lot of computational resources which is contradictory to our original objective of efficient fine-tuning . On the other hand , limited works discuss the model selection and HPO for fine-tuning . Kornblith et al . ( 2019 ) finds that ImageNet accuracy and fine-tuning accuracy of different models are highly correlated . Li et al . ( 2020 ) ; Achille et al . ( 2019 ) suggest that the optimal hyperparameters and model for fine-tuning should be both dataset dependent and domain similarity dependent ( Cui et al. , 2018 ) . HyperStar ( Mittal et al. , 2020 ) is a concurrent HPO work demonstrating that a performance predictor can effectively generate good hyper-parameters for a single model . However , those works don ’ t give an explicit solution about how to perform finetuning in a more practical online scenario . In this work , we take the advantage of online learning ( Hoi et al. , 2018 ; Sahoo et al. , 2017 ) to build a schedule generator , which allows us to memorize the past training history and provide up-and-coming training regimes for new coming tasks on the fly . Besides , we introduce the NAS model zoo to further push up the speed and performance . 3 THE PROPOSED APPROACH . The goal of this paper is to develop an online fine-tuning pipeline to facilitate a fast continuous cross-task model adaption . By the preliminary experiments in Section 4.1 , we confirm that the model architectures and hyperparameters such as the learning rate and frozen stages will greatly influence the accuracy and speed of the fine-tuning program . Thus , our NASOA includes two parts as shown in the Figure 1 : 1 ) Searching a group of neural architectures with good accuracy and fast training speed to create a pretrained model zoo ; 2 ) Designing an online task-oriented algorithm to generate an efficient fine-tuning regime with the most suitable model under user ’ s time constraint . 3.1 CREATING AN EFFICIENT TRAINING MODEL ZOO ( ET-NAS ) BY NAS . The commonly used hand-craft backbones for fine-tuning including MobileNet ( Sandler et al. , 2018 ) , ResNet ( He et al. , 2016 ) , and ResNeXt ( Xie et al. , 2017 ) . Recently , some state-of-the-art backbone series such as RegNet ( Radosavovic et al. , 2020 ) , and EfficientNet ( Tan et al. , 2019b ) are developed by automated algorithms for higher accuracy and faster inference speed . However , the objective of our NAS is to find a group of models with good model generalization ability and training speed . Suggested by Kornblith et al . ( 2019 ) , the model fine-tuning accuracy ( model generalization ability ) has a strong correlation between ImageNet accuracy ( r = 0.96 ) . Meanwhile , the training speed can be measured by the step time of each training iteration . Thus , our NAS can be formulated by a multi-objective optimization problem ( MOOP ) on the search space S given by : max A∈S ( acc ( A ) , −Ts ( A ) ) subject to Ts ( A ) ≤ Tm ( 1 ) whereA is the architecture , acc ( . ) is the Top-1 accuracy on ImageNet , Ts ( . ) is the average step time of one iteration , and Tm is the maximum step time allowed . The step time is defined to be the total time of one iteration , including forward/backward propagation , and parameter update . Search Space Design is extremely important ( Radosavovic et al. , 2020 ) . As shown in Figure 2 , to enable an efficient model , we propose a novel flexible joint block-level and macro-level search space to enable simple to complex block design and fine adjustment of the computation allocation on each stage . Unlike existing topological cell-level search space such as DARTS ( Liu et al. , 2018b ) , AmoebaNet ( Real et al. , 2019a ) , and NASBench101 ( Dong & Yang , 2019 ) , ours is more compact and avoids redundant skip-connections which have great memory access cost ( MAC ) . Our block-level search space is more flexible to adjust the width , depth ( for each stage ) , when to down-sample/raise the channels . In contrast , EfficientNet only scales up/down the total width and depth by a fixed allocation ratio , and RegNet can not change the number/type of operations in each block .

Summary: This works aims at task-oriented fine-tuning from pre-trained ImageNet models. It proposes a Neural Architecture Search and Online Adaption framework (NASOA) to perform fast task-oriented model fine-tuning. The NASOA first employ an offline NAS to select a group of models and then pick up the most suitable model from this group via an online schedule generator.