O PTIMIZING N EURAL N ETWORKS WITH G RADIENT L EXICASE S ELECTION
Anonymous authors
Paper under double-blind review
A BSTRACT
Lexicase selection is a parent selection method for which the selection is based on assessing individual cases in random order, instead of using aggregated metrics such as loss and accuracy.
While lexicase selection and its variants have demonstrated success in various ﬁelds including genetic programming, genetic algorithms, and more recently in symbolic regression and robotics, its applications in other forms of modern machine learning have not yet been explored.
In this paper, we aim to investigate the effectiveness of lexicase selection in training deep neural networks and illustrate how its general idea may ﬁt into the context of modern machine learning at scale.
We propose Gradient Lexicase Selection, an optimization method that combines stochastic gradient descent and lexicase selection in an evolutionary fashion.
Experimental results show that the proposed method improves the generalization performance of popular deep neural network architectures on benchmark image classiﬁcation datasets, and helps models learn more diverse representations.
I NTRODUCTION
Modern data-driven learning algorithms generally deﬁne an optimization objective, e.g. , a ﬁtness function in genetic algorithms (Holland, 1992) or a loss function in deep learning (LeCun et al., 2015), that computes the aggregate performance on the training data for the purpose of acquiring the best parameters in order to solve the task.
Taking the image classiﬁcation problem as an example, most recent approaches use Cross-Entropy loss with gradient descent (Bottou, 2010) and backpropagation (Rumelhart et al., 1985) to train deep neural networks (DNNs).
Despite the success that advanced DNNs can reach human-level performance on the image recognition task (Russakovsky et al., 2015), one potential drawback for such aggregated performance measurement is that the model may learn to seek ”compromises” during the learning procedure.
To give an example, consider a situation that may happen during the training phase for a batch of ten cases: nine cases are correctly predicted with high probabilities, but one is wrong.
The aggregated loss may cause the model learning to compromise the wrong case for higher probabilities on other cases, which may lead the optimization process to be stuck at local optima.
This potential optimization problem can be referred to as an uncompromising problem (Helmuth et al., 2014), that is, a problem for which it is not acceptable for a solution to perform sub-optimally on any one of the cases in exchange for better performance on others.
It becomes relevant in the context of deep learning where the optimization objective may introduce the behaviors of seeking compromises.
In order to improve the generalization of DNNs, it is important to maintain the diversity and generality of the representations contributed by every training case.
From the literature, uncompromising problems have been recently explored in genetic programming (GP) and genetic algorithms (GAs) for tasks such as program synthesis.
Among many methods that aim to mitigate this problem, lexicase selection (Helmuth et al., 2014; Spector, 2012) and its variants (Helmuth et al., 2016; Helmuth & Spector, 2015; Helmuth et al., 2014; Liskowski et al., 2015) have been shown to outperform others (Fieldsend & Moraglio, 2015; Galvan-Lopez et al., 2013; Krawiec & Liskowski, 2015) in a number of applications.
Instead of using an aggregated ﬁtness function for parent selection, lexicase selection gradually eliminates candidates as it proceeds to look at how the population fares at each data point in a shufﬂed dataset, in which way it can bolster the diversity and generality in populations.
Recent work also shows that lexicase selection can be used in rule-based learning systems (Aenugu & Spector, 2019), symbolic regression (La Cava et al., 2016), machine learning (La Cava & Moore, 2020b;a), and evolutionary robotics (Huizinga & Clune, 2018; La Cava & Moore, 2018) to improve model generalization, especially in situations of partial or missing data.
In this work, we aim to explore the application of lexicase selection in the task of optimizing deep neural networks.
Taking advantage of the commonly-used gradient descent and backpropagation methods, we introduce Gradient Lexicase Selection, an optimization method for training deep neural networks that not only beneﬁt from the efﬁciency of gradient-based learning but also improves the generalization of the networks using the outline of lexicase selection method in an evolutionary fashion.
We test the proposed method on the basic image classiﬁcation task on three benchmark datasets (CIFAR-10 (Krizhevsky et al., 2009), CIFAR-100 (Krizhevsky et al., 2009), and SVHN (Netzer et al., 2011)).
Experimental results show that gradient lexicase selection manages to improve the performance of the DNNs consistently across six different popular architectures (VGG (Simonyan & Zisserman, 2015), ResNet (He et al., 2016), DenseNet (Huang et al., 2017), MobileNetV2 (Sandler et al., 2018), SENet (Hu et al., 2018), EfﬁcientNet (Tan & Le, 2019)).
In addition, we perform further ablation studies to analyze the effectiveness and robustness of the proposed method from various perspectives.
We investigate the trade-offs between exploration and exploitation by analyzing the effects of population size and momentum, and also show that our algorithm can produce better representation diversity, which is advantageous to the generalization of DNNs.
B ACKGROUND AND R ELATED W ORK Preliminaries of Lexicase Selection
Lexicase selection is initially proposed as a parent selection method in population-based stochastic search algorithms such as genetic programming (Helmuth et al., 2014; Spector, 2012).
Follow-up work has shown that lexicase selection can effectively improve behavioral diversity and the overall performance and on a variety of genetic programming problems (Helmuth et al., 2016;
Helmuth &
Spector, 2015; Helmuth et al., 2014; Liskowski et al., 2015).
The key idea in lexicase selection is that each selection event consider a randomly shufﬂed sequence of test cases.
As a result, lexicase selection sometimes selects specialist individuals that perform poorly on average but perform better than many individuals on one or more other cases.
A more detailed description of lexicase selection is appended in Sec. 7.1.
Unlike methods such as tournament selection that use a single ﬁtness value and thus tend to always select generalist individuals that have good average performance, lexicase selection does not base selection on an aggregated measure of performance.
Such a difference allows lexicase selection to maintain higher population diversity by prioritizing different parts of the dataset during each selection event through the ordering of the cases.
It has been shown empirically on a number of program synthesis benchmark problems that lexicase selection substantially outperforms standard tournament selection and typically maintains higher levels of diversity (Helmuth et al., 2016).
In a more general context, lexicase selection can be used in any case where a selection procedure occurs with regard to performance assessment of multiple candidates with a set of test cases.
Recent work also explores the usage of lexicase selection in rule-based learning systems (Aenugu &
Spector, 2019), symbolic regression (La Cava et al., 2016), machine learning (La Cava & Moore, 2020b;a), and evolutionary robotics (Huizinga & Clune, 2018; La Cava & Moore, 2018).
In this work, we aim to explore the effectiveness of lexicase selection in the context of deep learning optimization from the perspective of improving the diversity of gradient-based representation learning for better generalization.
While there are also other parent selection methods (Fieldsend & Moraglio, 2015; Galvan-Lopez et al., 2013; Krawiec & Liskowski, 2015) that have been proposed to achieve similar goals, in this work we focus on investigating the usage of lexicase selection in deep learning.
Further discussion of comparisons between lexicase selection to other selection methods are out of the scope of this work.
Deep Neuroevolution and Population-based Methods
While backpropagation (Rumelhart et al., 1985) with gradient descent has been the most successful method in training DNNs with ﬁxed-topology in the past few decades (LeCun et al., 2015), there are also attempts to train DNNs through evolutionary algorithms (EAs).
Such et al.
(2017) proposed a gradient-free method to evolve the network weights by using a simple genetic algorithm, and was able to evolve a relatively deep network (with 4 million parameters) and demonstrated competitive results on several reinforcement learning benchmark problems. Jaderberg et al. (2017) proposed population-based training to optimize both model and the hyperparameters.
Cui et al.
(2018) proposed to alternate between the SGD step and evolution step to improve the average ﬁtness of the population.
Ding & Spector (2021) demonstrate the usage of selection-inspired methods as regularization of DNNs.
The most relevant recent work is Pawełczyk et al. (2018), which did a pilot study on combining simple GA schema with gradient-based learning, where gradient training is used as part of the mutation process.
Although the method was tested only on one dataset, the results were encouraging and offered some insights on a deeper combination of GAs and gradient learning.
Following this trend, this paper aims to explore a more efﬁcient evolutionary framework that takes advantage of both SGD and lexicase selection, to improve the network generalization by treating image recognition as an uncompromising problem.
From a broader perspective that is also closely related to this work, there has been a surge of interest in methods for Neural Architecture Search (NAS) (Elsken et al., 2019), where evolutionary algorithms gain high popularity.
A majority of methods (Floreano et al., 2008; Liu et al., 2017;
Miikkulainen et al., 2019; Real et al., 2019; 2017; Stanley & Miikkulainen, 2002; Xie & Yuille, 2017) use EA to search neural network topologies and use backpropagation to optimize network weights, and some others (Stanley & Miikkulainen, 2002) use EA to co-evolve topologies along with weights.
While our method can be easily extended to the NAS problem, this work focuses on training various ﬁxed-topology networks in order to make fair comparisons to show the signiﬁcance of using lexicase selection to improve model generalization.
G RADIENT L EXICASE S ELECTION
Our goal is to integrate the idea of lexicase selection to improve the generalization of DNNs, and at the same time to the greatest extent keep the efﬁciency and effectiveness of the popular gradientbased learning.
We propose Gradient Lexicase Selection as an optimization framework to combine the strength of these two methods.
An overview of the algorithm is demonstrated in Alg.
It has two major components, Subset Gradient Descent (SubGD) and Lexicase Selection, which we describe in details in this section as follows.
3. E VOLUTION WITH S UBSET G RADIENT D ESCENT (S UB GD)
First, we introduce the general evolutionary framework that uses a combination of stochastic gradient descent and evolution.
Given the network topology, we ﬁrst initialize all the parameters W 0 as the initial parent weight.
For each generation, given a population size of p , we generate p instances of the model as p offspring with the same weight as the parent weight, namely, W (0) =
W (1) = · · · = W ( p − 1) =
W 0 .
We then perform mutation on these offspring and use lexicase selection to select the parent for the next generation.
For each generation, we have p instances of the model starting with the exact same weights.
Instead of random mutations such as adding gaussian noise as commonly used in neuroevolution, we propose a gradient-based mutation method called subset gradient descent (SubGD).
Given the whole training dataset S train , we divided it into p subsets with random sampling, as
S (0) train , S (1) train , · · · , S ( p − 1) train .
We then train each model instance accordingly on one of the subsets using the normal mini-batch stochastic gradient descent.
The mutation is done when all the training data is consumed, which is one epoch in traditional deep learning.
There are several advantages of the proposed mutation method.
First, since all the offspring are trained with different non-overlapping training samples, they are likely to evolve diversely, especially when data augmentation is also included.
Secondly, each off-spring is trained using gradient descent, meaning they will be optimized efﬁciently towards the objective, comparing to random mutation methods such as gaussian noise.
Thirdly, if implemented with distributed training, all the offspring can be trained simultaneously to further reduce computation time.
In general, the subset gradient descent aims to ﬁnd a balance between exploration and exploitation toward the whole evolution process. Algorithm 1: Data: •
- set of p instances of the DNN initialized with the Result: • an optimized DNN model // K training epochs for epoch
= 1 : K do subsets ← p equal-size subsets obtained through random sampling from the entire data without replacement
Use gradient descent and backpropagation to optimize each of the p candidates on each of the p subsets respectively cases ← randomly shufﬂed sequence of data to be used in lexicase selection candidate ids ← number set of { 0 , 1 , 2 , · · · , p − 1 } parent ← None for case in cases do
Evaluate all the candidates that have their ids in candidate ids on case .
if at least one candidate makes the correct prediction then candidate ids ← the subset of the current candidate ids that the corresponding candidates make the correct prediction else
candidate
ids ← the subset of one randomly sampled id from the current candidate ids endif candidate ids contains only one single id then parent ← the corresponding candidate in candidates with the remaining id in candidate ids break end endif parent is None then parent ← a randomly selected candidate in candidates with the remaining id in candidate ids end candidates ← set of p instances of the DNN model copied with the same parameters as parent endreturn parent
3. L EXICASE S ELECTION FOR DNN S
After mutation, the offspring become candidates and we use lexicase selection to select a parent from them for the next generation.
First, a randomly shufﬂed sequence of training data points (without data augmentation) is used as the ’test’ cases for the selection purpose.
Starting from the ﬁrst training sample, we evaluate all the candidates on each case individually and remove the candidate from the selection pool if it does not make the correct prediction.
This process is repeated until if 1) there is only one instance left, which will be selected as the parent for the next generation, or 2) all the training samples are exhausted and more than one candidates survive, in which case we randomly pick a candidate from the selection pool.
For the selection process, we do not hold out another validation set because 1) if we choose to use a validation set, the validation set should have an adequate size in order to ensure its diversity and generality, which means the training set will be noticeably smaller, and thus the training performance is likely to degrade; 2) since each model instance only gets access to part of the (augmented) training data, the selection performed on the original training data is still effective, since the exact same data was never used in the mutation (training).
An important feature of lexicase selection is that it treats all the cases equally and thus there is no way to modify its selection pressure.
The motivation behind lexicase selection is to allow the survival of those models which may not perform best overall but were able to solve the given testing cases.
Such a model is likely to learn essential feature representations that allow it to make correct predictions on speciﬁc cases where all others fail.
By letting lexicase selection guide the training process, the neural network can potentially learn more diverse representations that ﬁnally contribute to better generalization.
To better accommodate the situation of training deep models on large-scale datasets, we also make some slight modiﬁcations to the original lexicase selection algorithm in regard to the tie situations, i.e. , when all the remaining candidates fail to make the correct prediction on one case.
Original lexicase selection lets all the candidates survive because they all have the same “best” performance, which is failure.
However, in the early stages of DNN training, while all the candidates are unable to predict correctly on any case, the original lexicase selection will proceed to evaluate them until someone happens to get a correct prediction by chance, which is inefﬁcient especially on large datasets.
So we modify the algorithm to randomly select a candidate from the remaining candidates if they all fail.
The modiﬁcation improves the efﬁciency of early-stage training and has not shown any noticeable effect on the ﬁnal model performance.
E XPERIMENTAL R ESULTS
The proposed Gradient Lexicase Selection is tested on the task of image classiﬁcation, which is one of the most common benchmark problems in computer vision and deep learning in general.
We implement the algorithm on six popular DNN architectures (VGG (Simonyan & Zisserman, 2015), ResNet (He et al., 2016), DenseNet (Huang et al., 2017), MobileNetV2 (Sandler et al., 2018),
SENet (Hu et al., 2018), EfﬁcientNet (Tan & Le, 2019)).
To show the signiﬁcance of our method, we also implement the original momentum-SGD training as baselines for all the architectures.
Three benchmark datasets (CIFAR-10 (Krizhevsky et al., 2009), CIFAR-100 (Krizhevsky et al., 2009), and SVHN (Netzer et al., 2011)) are used for evaluation.
These datasets comprise 32 ×pixel real-world RGB images of common objects (CIFAR-10, CIFAR-100) and street scene digits (SVHN).
The training is done using the training set only and we evaluate the methods on the test set after the training process is ﬁnished.
Note that for illustration purposes, we only use the training dataset of SVHN without the large extra set, so the results are not comparable to other work.
4. I MAGE C LASSIFICATION R ESULTS
The image classiﬁcation results are shown in Tab.
We report the mean percentage accuracy ( acc. ) with standard deviation ( std. ) obtained by running the same experiment with three different random seeds.
The last column ( acc. ↑ ) calculates the difference of accuracy by using our method compared to baseline, where positive numbers indicate improvement.
We can ﬁrst see that by using our method, most of the architectures show signiﬁcant improvement on the testing result.
On the easier SVHN dataset, we can still observe moderate and consistent improvement.
To show the robustness of our algorithm, we use the same population size of 4 for lexicase in all the experiments, meaning the performance may be further improved if extra tuning is performed.
The ablation study on the effect of population size is described later in Sec.5.1.
Beyond those improvement, we also ﬁnd that among all the architectures, our method surprisingly fails to improve MobileNetV2 on both CIFAR-10 and CIFAR-100.
The main difference between
MobileNetV2 and other architectures is that it is a highly optimized architecture with over a magnitude less parameters comparing to other architectures.
Sandler et al.
(2018) stated that they tailor the architecture to different performance points, which can be adjusted depending on desired accuracy/performance trade-of.
As a result, it is likely that the accuracy is restricted by the model size, and even with better training strategies the performance is not going to improve.
Such results indicate that our method may not work directly with architectures that have been optimized by using other training methods.
But for other more general architectures our method work directly out-of-the-box without further tuning. [Table]
[Table]
4. C OMPARING E VOLUTIONARY A LGORITHMS
The proposed method has two major components, SubGD and Lexicase Selection. To further validate the contribution of each component, we introduce two other methods for comparison: Random Selection and Tournament Selection (Miller et al., 1995).
Using the same evolutionary framework with SubGD, random selection simply selects a random offspring for each generation, and tournament selection uses the average accuracy as the ﬁtness function for selection, which is an aggregated metric as oppose to lexicase selection.
The results are shown in Tab. 2.
We can observe that both random selection and tournament selection perform slightly better than the
SGD baseline in most cases, but the proposed gradient lexicase selection is consistently better than both methods with a signiﬁcant margin.
Random selection can be viewed as a baseline method that
[Table]
uses SGD with the same amount of computation as gradient lexicase selection, which indicates that the proposed method outperforms SGD even at the same level of computation.
Tournament selection is one of the most commonly used selection method in evolutionary algorithms, which select parents based on an aggregated ﬁtness evaluation.
As the performance of tournament selection is similar to random selection, indicating that the mechanism of lexicase selection has the major contribution to the improvement.
In this section, we design several ablation studies to further analyze and validate the effectiveness of the proposed method.
Unless speciﬁcally mentioned, all the implementation details follows the same practices in Sec. 7.2.
5. P OPULATION S IZE
Population size is an essential hyperparameter for evolutionary algorithms.
In this work, the population size of DNNs has more constraints such as computation cost and total GPU memory, so it has to be much smaller comparing to those for classic GP problems.
We test lexicase gradient selection with population sizes of 2 , 4 , 6 , 8 .
For illustration purposes, two architectures (VGG16 and
ResNet18) are evaluated on the CIFAR-10 dataset.
The results are shown in Tab. 3.
First, we can see that lexicase is relatively robust to different population size p .
Under all the population size conﬁgurations lexicase manages to outperform baseline signiﬁcantly.
Since there have not been a trend of increased accuracy with larger population size, the generalization performance does not seem to increase with more population.
This observation aligns well with the behavior of lexicase selection in GP problems (Helmuth et al., 2018; La Cava et al., 2019), where there seems to be an optimal population size through the trade-off between exploration and exploitation.
For our method, having a larger population size not only adds more offspring for each generation, but also reduces the size of each subset used for training each offspring by SubGD.
In either way, the exploration is reduced and the exploitation is increased. There is no conﬂict between the two effects, so we do not control the size of each subset when increasing the population size. If the population gets too large, the individuals may not evolve different-enough behaviors from each other, and thus the diversity of population may become lower.
In general, we ﬁnd that with relatively small population size, we can get good results for gradient lexicase selection.
5. M OMENTUM
In deep learning, SGD with momentum (Sutskever et al., 2013; Liu et al., 2020) has been one of the most widely adopted method for training DNNs.
Momentum accelerates gradient descent by accumulating a velocity vector in directions of persistent reduction in the objective across iterations.
This accumulating behavior actually interferes with the proposed gradient lexicase selection algorithm, because model instances in the population get different gradient updates, and thus will have different momentum parameters.
To solve this issue, we propose three options: 1) No Momentum: do not use momentum at all; 2)
Reset Momentum: use high momentum rate and re-initialize the momentum parameters every epoch for each model instance; 3) Inherit Momentum: when selecting the parent model, also copy the
[Table]
momentum parameters along with the model parameters to all the instances in the next generation.
For this study, we also test two architectures (VGG16 and ResNet18) with population size of 4 on the CIFAR-10 dataset.
The results are shown in Tab. 4.
The key idea of lexicase selection is to evolve offspring with different behaviors, and make selection to prioritize one during each iteration.
In this way, the population can maintain a high diversity.
On the other hand, the way how momentum works tends to ﬁnd an aggregated direction of gradient update accumulated through time.
From the results, we can see that the Reset Momentum option works the best, indicating that if we inherit momentum, it will inﬂuence the mutation over generations and thus the selection strength of lexicase will be negatively affected.
By resetting momentum each epoch, only the mutation in the current generation is accelerated by using momentum SGD, which results in a higher diversity of offspring.
In general, the momentum options can also be viewed as different trade-offs of exploration and exploitation.
5. R EPRESENTATION D IVERSITY
While quantitative results have shown that the proposed method manage to improve the generalization of DNNs, we would like to further investigate the reasons behind for better understanding of the behavior of the algorithm.
One hypothesis is that since lexicase selection is able to increase the diversity and generality of the population in GP, it may as well help DNNs learn more diverse representation, which improves the overall model generalization.
To validate this, we analyze and compare the feature representations in ResNet-18 trained using normal SGD and gradient lexicase selection.
We take the ﬁrst 100 samples from the CIFAR-10 test set and use global max pooling to obtain the channel-wise activations of conv 4x and conv 5x layers (as deﬁned in He et al.
Fig.
1 shows the results.
We can observe that our method produces a ﬂatter distribution of activations with less frequency on 0 s and more frequency on other values.
Ioffe & Szegedy (2015); Wu & He (2018) shows that normalized distribution of layer activations can help reduce the internal covariate shift of DNNs during training, and thus improves the training efﬁciency and model generalization.
Similarly, our method manages to learn more diverse representations by incorporating lexicase selection into training framework, which is advantageous to the generalization of DNNs.
C ONCLUSION AND F UTURE W ORK
In this work, efﬁcient adaption of lexicase selection in the task of optimizing deep neural networks is explored.
We propose gradient lexicase selection, an evolutionary algorithm that incorporates lexicase selection with stochastic gradient descent to help DNNs learn more diverse representations for better generalization.
Experimental results show that the proposed method can improve the performance of several popular DNN architectures on benchmark image classiﬁcation datasets.
Several ablation studies further validate the robustness and advantages of our method from different perspectives.
More speciﬁcally, we investigate the trade-offs between exploration and exploitation by analyzing the effects of population size and momentum.
We also show that our algorithm can produce better representation diversity, which is advantageous to the generalization of DNNs.
The goal of our method is to improve the generalization performance rather than speed up the optimization.
Our method is potentially valuable to many real-world problems, especially those safetycritical applications like autonomous vehicles, where higher cost of computation during training is acceptable for better generalization performance.
There are also several factors to consider regarding the computation cost: 1) our method expects parallel training of model instances, so the optimal training time can be reduced to naive SGD training with modern cloud computing facilities; 2) we have shown that with relatively small population sizes ( 4 × naive SGD), our method can already achieve signiﬁcantly better performance; 3) with the same amount of computation, the naive method (random selection baseline in Sec.4.2) can not achieve the same performance as ours.
There are several limitations of our work.
As described in Sec.
4.1, the current lexicase may not work with architectures that have been highly optimized, indicating a potential correlation between network architecture and lexicase selection.
For future directions, we look forward to explorations on how lexicase can be used in optimizing neural architectures along with the parameters, and the integration of lexicase in neural architecture search in general.
R EPRODUCIBILITY S TATEMENT
We submit our source code as the supplementary material for the review process, which can be used to reproduce the experimental results in this work.
The conﬁgurations and implementation details are described in Sec. 7.2.
A PPENDIX 7. L EXICASE S ELECTION
Lexicase selection is a parent selection method in population-based stochastic search algorithms such as genetic programming (Helmuth et al., 2014; Spector, 2012).
The lexicase selection algorithm is outlined in Alg. 2.
Algorithm
Data:
• cases - randomly shufﬂed sequence of data samples to be used in selection • candidates - the entire population of programs
Result:
• an individual program to be used as a parent
case in cases do candidates return candidate end endreturn a randomly selected candidate in candidates
The key idea in lexicase selection is that each selection event consider a randomly shufﬂed sequence of test cases.
With the speciﬁc ordering, only individuals whose error is minimal among all the already-considered cases are allowed to survive.
Moving forward through the sequence of cases, the selection is done when there is only one candidate left or until all the test cases have been gone through, in which case we select randomly from the remaining candidates.
Since the ordering of test cases is randomized for each selection event, every test case gets the opportunity to be prioritized when being put at the beginning of the sequence.
As a result, lexicase selection sometimes selects specialist individuals that perform poorly on average but perform better than many individuals on one or more other cases.
7. I MPLEMENTATION D ETAILS AND H YPERPARAMETERS
Each network architecture with baseline SGD training and its corresponding counterpart with gradient lexicase selection are trained with identical experimental schemes.
We use SGD with momentum instead of the popular adaptive methods (such as Adam) because, despite the popularity of those methods, some recent works (Luo et al., 2018; Wilson et al., 2017) observe that the solutions found by those methods actually generalize worse (often signiﬁcantly worse) than SGD.
We did experiments with Adam and tuned the learning rate for several trials, but the results are signiﬁcantly worse than the SGD counterpart.
This work focuses more on the generalization performance rather than the training speed, so we follow the common practice to use SGD with momentum for both baseline training and SubGD.
However, it is very likely that some most recent optimization methods, such as Luo et al.
(2018), can achieve faster training as well as the same generalization performance as
SGD.
We follow standard practices and perform data augmentation with random cropping with padding and perform random horizontal ﬂipping during the training phase (no augmentation is used during selection phase).
The input images are normalized through mean RGB-channel subtraction for all the phases.
For both baseline and lexicase, we use SGD with momentum of 0 .
For lexicase, we use the Reset Momentum option that re-initialize the momentum parameters for each epoch, which is explained in detail later in Sec. 5.2.
The batch size is set to 128 for CIFAR-10 and 64 for CIFAR-100 and SVHN.
The initial learning rate is set to 0 .
1 and tuned by using Cosine Annealing (Loshchilov & Hutter, 2017).
We set the total number of epochs as 200 for baseline training and as 200( p +1) for gradient lexicase selection, where p is the size of population.
For each epoch in lexicase, the mutation only uses 1 /p of the training data to train each model instance, so we keep the total iterations of weight update of lexicase training similar to baseline training to ensure convergence.