R ETHINKING THE H YPERPARAMETERS FOR F INE - TUNING
A BSTRACT
Fine-tuning from pre-trained ImageNet models has become the de-facto standard for various computer vision tasks.
Current practices for ﬁne-tuning typically involve selecting an ad-hoc choice of hyperparameters and keeping them ﬁxed to values normally used for training from scratch.
This paper re-examines several common practices of setting hyperparameters for ﬁne-tuning.
Our ﬁndings are based on extensive empirical evaluation for ﬁne-tuning on various transfer learning benchmarks.
(1) While prior works have thoroughly investigated learning rate and batch size, momentum for ﬁne-tuning is a relatively unexplored parameter.
We ﬁnd that the value of momentum also affects ﬁne-tuning performance and connect it with previous theoretical ﬁndings.
(2) Optimal hyperparameters for ﬁne-tuning, in particular, the effective learning rate, are not only dataset dependent but also sensitive to the similarity between the source domain and target domain.
This is in contrast to hyperparameters for training from scratch.
(3) Reference-based regularization that keeps models close to the initial model does not necessarily apply for “dissimilar” datasets.
Our ﬁndings challenge common practices of ﬁnetuning and encourages deep learning practitioners to rethink the hyperparameters for ﬁne-tuning.
I NTRODUCTION
Many real-world applications often have a limited number of training instances, which makes directly training deep neural networks hard and prone to overﬁtting.
Transfer learning with the knowledge of models learned on a similar task can help to avoid overﬁtting.
Fine-tuning is a simple and effective approach of transfer learning and has become popular for solving new tasks in which pre-trained models are ﬁne-tuned with the target dataset.
Speciﬁcally, ﬁne-tuning on pre-trained ImageNet classiﬁcation models (Simonyan & Zisserman, 2015; He et al., 2016b) has achieved impressive results for tasks such as object detection (Ren et al., 2015) and segmentation (He et al., 2017; Chen et al., 2017) and is becoming the de-facto standard of solving computer vision problems.
It is believed that the weights learned on the source dataset with a large number of instances provide better initialization for the target task than random initialization.
Even when there is enough training data, ﬁne-tuning is still preferred as it often reduces training time signiﬁcantly (He et al., 2019).
The common practice of ﬁne-tuning is to adopt the default hyperparameters for training large models while using smaller initial learning rate and shorter learning rate schedule.
It is believed that adhering to the original hyperparameters for ﬁne-tuning with small learning rate prevents destroying the originally learned knowledge or features.
For instance, many studies conduct ﬁne-tuning of
ResNets (He et al., 2016b) with these default hyperparameters: learning rate 0.01, momentum 0.
and weight decay 0.0001.
However, the default setting is not necessarily optimal for ﬁne-tuning on other tasks.
While few studies have performed extensive hyperparameter search for learning rate and weight decay (Mahajan et al., 2018; Kornblith et al., 2018), the momentum coefﬁcient is rarely changed.
Though the effectiveness of the hyperparameters has been studied extensively for training a model from scratch, how to set the hyperparameters for ﬁne-tuning is not yet fully understood.
In addition to using ad-hoc hyperparameters, commonly held beliefs for ﬁne-tuning also include:
• Fine-tuning pre-trained networks outperforms training from scratch; recent work (He et al., 2019) has already revisited this.
• Fine-tuning from similar domains and tasks works better (Ge & Yu, 2017; Cui et al., 2018; Achille et al., 2019; Ngiam et al., 2018).
• Explicit regularization with initial models matters for transfer learning performance (Li et al., 2018; 2019).
Are these practices or beliefs always valid?
From an optimization perspective, the difference between ﬁne-tuning and training from scratch is all about the initialization.
However, the loss landscape of the pre-trained model and the ﬁne-tuned solution could be much different, so as their optimization strategies and hyperparameters.
Would the hyperparameters for training from scratch still be useful for ﬁne-tuning?
In addition, most of the hyperparameters (e.g., batch size, momentum, weight decay) are frozen; will the conclusion differ when some of them are changed?
With these questions in mind, we re-examined the common practices for ﬁne-tuning.
We conducted extensive hyperparameter search for ﬁne-tuning on various transfer learning benchmarks with different source models.
The goal of our work is not to obtain state-of-the-art performance on each ﬁne-tuning task, but to understand the effectiveness of each hyperparameter for ﬁne-tuning, avoiding unnecessary computation.
We explain why certain hyperparameters work so well on certain datasets while fail on others, which can guide hyperparameter search for ﬁne-tuning.
Our main ﬁndings are as follows:
• Optimal hyperparameters for ﬁne-tuning are not only dataset dependent, but are also dependent on the similarity between the source and target domains, which is different from training from scratch.
Therefore, the common practice of using optimization schedules derived from ImageNet training cannot guarantee good performance.
It explains why some tasks are not achieving satisfactory results after ﬁne-tuning because of inappropriate hyperparameter selection.
Speciﬁcally, as opposed to the common practice of rarely tuning the momentum value beyond 0.9, we ﬁnd that zero momentum sometimes work better for ﬁne-tuning on tasks that are similar with the source domain, while nonzero momentum works better for target domains that are different from the source domain.
• Hyperparameters are coupled together and it is the effective learning rate—which encapsulates the learning rate and momentum—that matters for ﬁne-tuning performance.
While effective learning rate has been studied for training from scratch, to the best of our knowledge, no previous work investigates effective learning rate for ﬁne-tuning and is less used in practice.
Our observation of momentum can be explained as small momentum actually decreases the effective learning rate, which is more suitable for ﬁne-tuning on similar tasks.
We show that the optimal effective learning rate depends on the similarity between the source and target domains.
• We ﬁnd regularization methods that were designed to keep models close to the initial model does not necessarily work for “dissimilar” datasets, especially for nets with Batch Normalization.
Simple weight decay can result in as good performance as the referencebased regularization methods for ﬁne-tuning with better search space.
R ELATED W ORK
In transfer learning for image classiﬁcation, the last layer of a pre-trained network is usually replaced with a randomly initialized fully connected layer with the same size as the number of classes in the target task (Simonyan & Zisserman, 2015).
It has been shown that ﬁne-tuning the whole network usually results in better performance than using the network as a static feature extractor (Yosinski et al., 2014;
Donahue et al., 2014;
Huh et al., 2016;
Mormont et al., 2018; Kornblith et al., 2018).
Ge & Yu (2017) select images that have similar local features from source domain to jointly ﬁne-tune pre-trained networks.
Cui et al.
(2018) estimate domain similarity with ImageNet and demonstrate that transfer learning beneﬁts from pre-training on a similar source domain.
Besides image classiﬁcation, many object detection frameworks also rely on ﬁne-tuning to improve over training from scratch (Girshick et al., 2014; Ren et al., 2015).
Many researchers re-examined whether ﬁne-tuning is a necessity for obtaining good performance.
Ngiam et al.
ﬁnd that when domains are mismatched, the effectiveness of transfer learning is negative, even when domains are intuitively similar.
Kornblith et al.
(2018) examine the ﬁne-tuning performance of various ImageNet models and ﬁnd a strong correlation between ImageNet topaccuracy and the transfer accuracy.
They also ﬁnd that pre-training on ImageNet provides minimal beneﬁts for some ﬁne-grained object classiﬁcation dataset.
He et al.
(2019) questioned whether
ImageNet pre-training is necessary for training object detectors.
They ﬁnd the solution of training from scratch is no worse than the ﬁne-tuning counterpart as long as the target dataset is large enough.
Raghu et al.
ﬁnd that transfer learning has negligible performance boost on medical imaging applications, but speed up the convergence signiﬁcantly.
There are many literatures on hyperparameter selection for training neural networks from scratch, mostly on batch size, learning rate and weight decay (Goyal et al., 2017; Smith et al., 2018; Smith & Topin, 2019).
There are few works on the selection of momentum (Sutskever et al., 2013).
Zhang & Mitliagkas (2017) proposed an automatic tuner for momentum and learning rate in SGD.
There are also studies on the correlations of the hyperparameters, such as linear scaling rule between batch size and learning (Goyal et al., 2017; Smith et al., 2018; Smith, 2017).
However, most of these advances on hyperparameter tuning are designed for training from scratch and have not examined on ﬁne-tuning tasks for computer vision problems.
Most work on ﬁne-tuning simply choose ﬁxed hyperparameters (Cui et al., 2018) or use dataset-dependent learning rates (Li et al., 2018) in their experiments.
Due to the huge computational cost for hyperparameter search, only a few works (Kornblith et al., 2018; Mahajan et al., 2018) performed large-scale grid search of learning rate and weight decay for obtaining the best performance.
T UNING H YPERPARAMETERS FOR F INE - TUNING
In this section, we ﬁrst introduce the notations and experimental settings, and then present our observations on momentum, effective learning rate and regularization.
The ﬁne-tuning process is not different from learning from scratch except for the weights initialization.
The goal of the process is still to minimize the objective function L = 1 N (cid:80) Ni =1 (cid:96) ( f ( x i , θ ) , y i ) + λ 2 (cid:107) θ (cid:107) 22 , where (cid:96) is the loss function, N is the number of samples, x i is the input data, y i is its label, f is the neural network, θ is the model parameters and λ is the regularization hyperparameter or weight decay.
Momentum is widely used for accelerating and smoothing the convergence of SGD by accumulating a velocity vector in the direction of persistent loss reduction (Polyak, 1964; Sutskever et al., 2013; Goh, 2017).
The commonly used Nesterov’s Accelerated Gradient (Nesterov, 1983) is given by:
[Equation]
where θ t indicates the model parameters at iteration t .
The hyperparameters include the learning rate η t , batch size n , momentum coefﬁcient m ∈ [0 , 1) , and the weight decay λ .
3. E XPERIMENTAL S ETTINGS
We evaluate ﬁne-tuning on seven widely used image classiﬁcation datasets, which covers tasks for ﬁne-grained object recognition, scene recognition and general object recognition.
Detailed statistics of each dataset can be seen in Table 1.
We use ImageNet (Russakovsky et al., 2015), Place365 (Zhou et al., 2018) and iNaturalist (Van Horn et al., 2018) as source domains for pre-trained models.
We resize the input images such that the aspect ratio is preserved and the shorter side is 256 pixels.
The images are normalized with mean and std values calculated over ImageNet.
For data augmentation, we adopt the common practices used for training ImageNet models (Szegedy et al., 2015): random mirror, random scaled cropping with scale and aspect variations, and color jittering.
The augmented images are resized to 224 × 224.
Note that state-of-the-art results could achieve even better performance by using higher resolution images (Cui et al., 2018) or better data augmentation (Cubuk et al., 2018).
We mainly use ResNet-101-V2 (He et al., 2016a) as our base network, which is pre-trained on
ImageNet (Russakovsky et al., 2015).
Similar observations are also made on DenseNets (Huang et al., 2017) and MobileNet (Howard et al., 2017).
The hyperparameters to be tuned (and ranges)
[Table]
are: learning rate (0.1, 0.05, 0.01, 0.005, 0.001, 0.0001), momentum (0.9, 0.99, 0.95, 0.9, 0.8, 0.0) and weight decay (0.0, 0.0001, 0.0005, 0.001).
We set the default hyperparameters to be batch size 256 1 , learning rate 0.01, momentum 0.9 and weight decay 0.0001.
To avoid insufﬁcient training and observe the complete convergence behavior, we use 300 epochs for ﬁne-tuning and 600 epochs for scratch-training , which is long enough for the training curves to converge.
The learning rate is decayed by a factor of 0.1 at epoch 150 and 250.
We report the Top-1 validation (test) error at the end of training.
The total computation time for the experiments is more than 10K GPU hours.
3. E FFECT OF M OMENTUM AND D OMAIN S IMILARITY
Momentum 0.9 is the most widely used value for training from scratch (Krizhevsky et al., 2012;
Simonyan & Zisserman, 2015; He et al., 2016b) and is also widely adopted for ﬁne-tuning (Kornblith et al., 2018).
To the best of our knowledge, it is rarely changed, regardless of the network architectures or target tasks.
To check the inﬂuence of momentum on ﬁne-tuning, we ﬁrst search for the best momentum value for ﬁne-tuning on the Birds dataset with different weight decay and learning rate.
Figure 1(a) shows the performance of ﬁne-tuning with and without weight decays.
Surprisingly, momentum zero actually outperforms the nonzero momentum.
The optimal learning rate also increases when the momentum is disabled as shown in Figure 1(b).
[Figure]
To verify this observation, we further compare momentum 0.9 and 0.0 on other datasets.
Tableshows the performance of 8 hyperparameter settings on 7 datasets.
We observe a clear pattern that disabling momentum works better for Dogs, Caltech and Indoor, while momentum 0.9 works better for Cars, Aircrafts and Flowers.
[Table] [Table]
Interestingly, datasets such as Dogs, Caltech, Indoor and Birds are known to have high overlap with
ImageNet dataset 2 , while Cars and Aircrafts are identiﬁed to be difﬁcult to beneﬁt from ﬁne-tuning from pre-trained ImageNet models (Kornblith et al., 2018).
According to Cui et al.
(2018), in which the Earth Mover’s Distance (EMD) is used to calculate the similarity between ImageNet and other domains, the similarity to Dogs and Birds are 0.619 and 0.563, while the similarity to Cars, Aircrafts and Flowers are 0.560, 0.556 and 0.525 3 .
The relative order of similarities to ImageNet is
Dogs, Birds, Cars, Aircrafts and Flowers which aligns well with the transition of optimal momentum value from 0.0 to 0.9.
Following the similarity calculation, we can also veriﬁed Caltech and Indoor are more close to ImageNet than Cars/Aircrafts/Flowers (Table 3.3). To verify the connection between momentum and domain similarity
, we further ﬁne-tune from different source domains such as Places-365 and iNaturalist, which are known to be better source domains than ImageNet for ﬁne-tuning on Indoor and Birds dataset (Cui et al., 2018).
We may expect that ﬁne-tuning from iNaturalist works better for Birds with m = 0 and similarly, Places for Indoor.
Indeed, as shown in Table 3, disabling momentum improves the performance when the source and target domain are similar, such as Places for Indoor and iNaturalist for Birds.
Small momentum works better for ﬁne-tuning on domains that are close to the source domain One explanation for the above observations is that because the Dogs dataset is very close to ImageNet, the pre-trained ImageNet model is expected to be close to the ﬁne-tuned solution on the Dogs dataset.
In this case, momentum may not help much as the gradient direction around the minimum could be much random and accumulating the momentum direction could be meaningless.
Whereas, for [Figure] faraway target domains (e.g., Cars and Aircrafts) where the pre-trained ImageNet model could be much different with the ﬁne-tuned solution, the ﬁne-tuning process is more similar with training from scratch, where large momentum stabilizes the decent directions towards the minimum.
An illustration of the difference can be found in Figure 2.
Connections to early observations on decreasing momentum Early work (Sutskever et al., 2013) actually pointed out that reducing momentum during the ﬁnal stage of training allows ﬁner convergence while aggressive momentum would prevent this.
They recommended reducing momentum from 0.99 to 0.9 in the last 1000 parameter updates but not disabling it completely.
Recent work (Liu et al., 2018; Smith, 2018) showed that a large momentum helps escape from saddle points but can hurt the ﬁnal convergence within the neighborhood of the optima, implying that momentum should be reduced at the end of training.
Liu et al.
ﬁnd that a larger momentum introduces higher variance of noise and encourages more exploration at the beginning of optimization, and encourages more aggressive exploitation at the end of training.
They suggest that at the ﬁnal stage of the step size annealing, momentum SGD should use a much smaller step size than that of vanilla SGD.
When applied to ﬁne-tuning, we can interpret that if the pre-trained model lies in the neighborhood of the optimal solution on the target dataset, the momentum should be small.
Our work identiﬁes the empirical evidence of disabling momentum helps ﬁnal convergence, and ﬁne-tuning on close domains is a good exemplar.
3. C OUPLED H YPERPARAMETERS AND THE V IEW OF E FFECTIVE L EARNING R ATE
[Figure]
Now that we had discovered the effect of momentum by ﬁxing other hyperparameters and only allowed momentum to change.
But note that the two difﬁcult scenarios shown in Figure 2 (b) and (c) might also be mitigated by increasing or decreasing learning rate.
That is, hyperparameters are coupled and varying one hyperparameter can change the optimal values of the other hyperparameters that lead to the best performance.
In addition, optimal values of certain hyperparameters depend on the values of other hyperparameters in systematic ways.
For example, learning rate is entangled with batch size, momentum and weight decay.
There is a notion of effective learning rate (ELR) (Hertz et al., 1991; Smith et al., 2018; Smith & Le, 2018) for SGD with momentum: η (cid:48) =
η/ (1 − m ) , which was shown to be more closely related with training dynamics and ﬁnal performance rather than η .
The effective learning rate with m = 0 .
9 is 10 × higher than the one with m = 0 .
if other hyperparameters are ﬁxed, which is probably why we see an increase in optimal learning rate when momentum is disabled in Figure 1(b) and
Appendix A.
It is the effective learning rate that matters for ﬁne-tuning performance Because hyperparameters are coupled, looking at the performance with only one hyperparameter varied may give a misleading understanding of the effect of hyperparameters.
Therefore, to examine the effect of momentum, we should report the best result obtainable with and without momentum, as long as other hyperparameters explored are sufﬁciently explored.
We re-examine previous experiments that demonstrated the importance of momentum tuning when the ELR η (cid:48) =
η/ (1 − m ) is held ﬁxed instead of simply ﬁxing learning rate η .
Figure 3 shows that when η (cid:48) is constant, the best performance obtained by m = 0 .
9 and m = 0 are almost equivalent when other hyperparameters are allowed to change.
However, different ELR does result in different performance, which indicates its importance for the best performance.
It explains why the common practice of changing only learning rate generally works, though changing momentum may results in the same result, they both change the ELR.
In fact, as long as the initial learning rate is small enough, we can always search for the optimal momentum as it is an ampliﬁer, making the ELR larger by a factor of 1 / (1 − m ) . Therefore, momentum does determine the search ranges of learning rate.
Optimal ELR depends on the similarity between source domain and target domain Now that we have shown ELR is critical for ﬁne-tuning performance, we are interested in the factors that determine the optimal ELR for a given task.
Previous work (Smith & Le, 2018) found that there is an optimum ELR which maximizes the test accuracy.
However, the observations are only based on scratch training on small datasets (e.g., CIFAR-10); the relationship between ELR and domain similarity, especially for ﬁne-tuning, is still unexplored. To examine this, we search the best ELR on each ﬁne-tuning task and reports in Fig. 4 the best validation error obtained by each ELR while allowing other hyperparameters to change.
It shows the optimal ELR depends on both source domain and target domain.
As shown in Fig. 4 (a-c), the optimal ELR for Dogs/Caltech/Indoor are much smaller than these for Aircrafts/Flowers/Cars when ﬁne-tuned from ImageNet pre-trained model. Similar observations can be made on DenseNets and MobileNet. Though the optimal ELR value is different, the relative order of domain similarity is consistent and architecture agnostic.
We can also see a smaller ELR works better when source domain and target domain are similar, such as Dogs for ImageNet and Birds for iNat2017 (Fig. 4 (a, d-e)).
Interestingly, the optimal ELR for training from scratch is much larger and very similar across different target datasets, which indicates the distance from a random initialization is uniformly similar to different target dataset. [Figure] [Table] Optimal ELR selection based on domain similarity Now we have made qualitative observations about the relationship between domain similarity and optimal ELR.
A quantitative characterization of the relationship could reduce the hyperparameter search ranges for HPO or even eliminate HPO by accurately predicting hyperparameters.
We followed the domain similarity calculation in (Cui et al., 2018) and recalculate similarity scores for all source-target domain pairs. Note the original domain similarity calculation in (Cui et al., 2018) use pre-trained JFT (Sun et al., 2017) models as feature extractor, which are not public available. We alternatively use ImageNet pre-trained model or the source model as feature extractor.
As shown in Table 4, there is a good correlation between domain similarity score and the scale of optimal ELR.
Generally, the more similar the two domains, the smaller the optimal ELR. Though it is not strictly corresponding to the domain similarity score, the score provides reasonable prediction about the scale of optimal ELR, such as [0 . 001 , 0 . 01] , [0 . 01 , 0 . 1] , [0 . 1 , 1 . 0] and therefore can reduce the search space for optimal ELR. Based on this correlation, a simple strategy can be developed for optimal ELR selection given a frequently used source model: one can calculate domain similarities and perform exhaustive hyperparameter searches for few reference datasets, including similar and dissimilar datasets. Then given a new dataset to ﬁne-tune, one can calculate the domain similarity and compare with the scores of reference datasets, and choose the range of ELRs with the closest domain similarity.
and Learning Rate The relationship between weight decay and effective learning rate is recently well-studied (van Laarhoven; Zhang et al., 2018; Loshchilov & Hutter, 2018).
It was shown that the effect of weight decay on models with BN layers is equivalent to increasing the
ELR by shrinking the weights scales, i.e., η (cid:48) ∼ η/ (cid:107) θ (cid:107) 22 .
And if the optimal effective learning rate exists, the optimal weight decay value λ is inversely related with the optimal learning rate η .
The ‘effective’ weight decay is λ (cid:48)
=
λ/η .
We show in Figure 5 that the optimal effective weight decay is also correlated with domain similarity.
[Figure]
3. T HE CHOICE OF R EGULARIZATION
L 2 regularization or weight decay is widely used for constraining the model capacity (Hanson &
Pratt, 1989; Krogh & Hertz, 1992).
Recently Li et al. (2018; 2019) pointed out that standard Lregularization, which drives the parameters towards the origin, is not adequate in transfer learning.
To retain the knowledge learned by the pre-trained model, reference-based regularization was used to regularize the distance between ﬁne-tuned weights and the pre-trained weights, so that the ﬁnetuned model is not too different from the initial model.
Li et al.
(2018) propose L 2 -SP norm, i.e., λ 1 2 (cid:107) θ (cid:48)
− θ 0 (cid:107) 22 + λ 2 2 (cid:107) θ (cid:48)(cid:48) (cid:107) 22 , where θ (cid:48) refers to the part of network that shared with the source network, and θ (cid:48)(cid:48) refers to the novel part, e.g., the last layer with different number of neurons.
While the motivation is intuitive, there are several issues for adopting reference based regularization for ﬁne-tuning: • Many applications actually adopt ﬁne-tuning on target domains that are quite different from source domain, such as ﬁne-tuning ImageNet models for medical imaging (Mormont et al., 2018; Raghu et al., 2019).
The ﬁne-tuned model does not necessarily have to be close with the initial model.
• The scale invariance introduced by Batch Normalization (BN) (Ioffe & Szegedy, 2015) layers enable models with different parameter scales to function the same, i e., f ( θ ) =
f ( αθ ) .
Therefore, when L 2 regularization drives (cid:107) θ (cid:107) 22 towards zeros, it could still have the same functionality as the initial model.
On the contrary, a model could still be different even when the L 2 -SP norm is small.
• L 2 -SP regularization would constrain θ (cid:48)(cid:48) to be close to θ 0 , so that (cid:107) θ (cid:107) 22 is relatively stable in comparison with L 2 regularization.
Given that ELR is approximately proportional to η/ (cid:107) θ (cid:107) 22 and a smaller ELR is beneﬁcial for ﬁne-tuning from similar domains, it may explain why L 2 -SP provides better performance.
If this is true, then by decreasing the initial ELR, L 2 -norm may function the same.
To examine these conjectures, we revisited the work of (Li et al., 2018) with additional experiments.
To show the effectiveness of L 2 -SP norm, the authors conducted experiments on datasets such as
Dogs, Caltech and Indoor, which are all close to the source domain (ImageNet or Places-365).
We extend their experiments by ﬁne-tuning on both “similar” and “dissimilar” datasets, including Birds, Cars, Aircrafts and Flowers, with both L 2 and L 2 -SP regularization (details in Appendix D).
For fair comparison, we perform the same hyperparameter search for both methods.
As expected, Table 5 shows that L 2 regularization is very competitive with L 2 -SP on Birds, Cars, Aircrafts and Flowers, which indicates that reference based regularization may not generalize well for ﬁne-tuning on dissimilar domains.
[Table]
[Table]
[Table]
[Table]
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[Table]
[Table]
[Table]
We also check the change of regularization terms during training for both methods as well as their best hyperparameters.
As shown in Figure 6, the L 2 regularization usually decrease the weights norm more aggressively, depending on the value of λ , while L 2 -SP regularization keeps the norm less changed.
We can see that the optimal learning rate of L 2 regularization is mostly smaller than
L 2 -SP, which may compensate for the decreased weight norm or increased ELR.
Interestingly, for
Dogs dataset, both regularization terms grow much larger after a few iterations and then become stable, which means constraining the weights to be close to initialization is not necessarily the reason for L 2 -SP to work even for close domains.
It also seems contradicting to previous ﬁnding (Zhang et al., 2018) that weight decay functions as increasing ELR by decreasing weight norms.
However, it might be reasonable as large norm actually decreases the ELR, which could be helpful due to the close domain similarity between Dogs and ImageNet.
[Figure]
[Figure]
[Figure]
[Figure]
[Figure]
D ISCUSSION
The two extreme ways for selecting hyperparameters—performing exhaustive hyperparameter search or taking ad-hoc hyperparameters from scratch training—could be either too computationally expensive or yield inferior performance.
Different from training from scratch, where the default hyperparameter setting may work well for random initialization, the choice of hyperparameters for ﬁne-tuning is not only dataset dependent but is also inﬂuenced by the similarity between the target and source domains.
The rarely tuned momentum value could also improve or impede the performance when the target domain and source domain are close given insufﬁciently searched learning rate.
These observations connect with previous theoretical works on decreasing momentum at the end of training and effective learning rate.
We further identify that the optimal effective learning rate correlates with the similarity between the source and target domains.
With this understanding, one can signiﬁcantly reduce the hyperparameter search space.
We hope these ﬁndings could be one step towards better and efﬁcient hyperparameter selection for ﬁne-tuning.
A CKNOWLEDGMENTS
The authors would like to thank all anonymous reviewers for their valuable feedback. A T HE E FFECTIVENESS OF M OMENTUM Searching for Optimal Momentum
To check the effectiveness of momentum on ﬁne-tuning, we can search the best momentum values for ﬁne-tuning with ﬁxed learning rate but different batch size and weight decay.
Taking Birds dataset as example, Figure 7 provides the convergence curves for the results shown in Figure 1(a), which shows the learning curves of ﬁne-tuning with 6 different batch sizes and weight decay combinations.
Zero momentum outperforms the nonzero momentum in 5 out of 6 conﬁgurations.
[Figure]
Optimal learning rate increases after disabling momentum.
Figure 8 compares the performance of with and without momentum for each dataset with different learning rate.
Note that the optimal learning rate generally increase 10x after changing m from 0.9 to 0.0, which is coherent with the rule of effective learning rate.
[Figure]
B D OMAIN S IMILARITY
The domain similarity calculation based on Earth Mover Distance (EMD) is introduced in the section 4.1 of (Cui et al., 2018) 4 .
Here we brieﬂy introduce the steps.
In (Cui et al., 2018), the authors ﬁrst train ResNet-101 on the large scale JFT dataset (Sun et al., 2017) and use it as a feature extractor.
They extracted features from the penultimate layer of the model for each image of the training set of the source domain and target domain.
For ResNet-101, the length of the feature vector is 2048.
The features of images belonging to the same category are averaged and g ( s i ) denotes the average feature vector of i th label in source domain S , similarly, g ( t j ) denotes the average feature vector of j th label in target domain T .
The distance between the averaged features of two labels is d i,j = (cid:107) g
( s i ) − g ( t j ) (cid:107) .
Each label is associated with a weight w ∈ [0 , 1] corresponding to the percentage of images with this label in the dataset.
So the source domain S with m labels and the target domain T with n labels can be represented as S =
{ ( s i , w s i ) } mi =1 and T = { ( t j , w t j ) } ni =1 .
The EMD between the two domains is deﬁned as
[Equation]
where the optimal ﬂow f i,j corresponds to the least amount of total work by solving the EMD optimization problem.
The domain similarity is deﬁned as
[Equation]
where γ is 0.01.
Note that the domain similarity value is not ranging from 0 to 1.
Due to the unavailability of the large-scale JFT dataset (300x larger than ImageNet) and its pre-trained
ResNet-101 model, we cannot use it for extracting features for new datasets, such as Caltech256 and
MIT67-Indoor.
Instead of using the powerful feature representation, we use our pre-trained ImageNet model (ResNet-101) as the feature extractor.
Table 4 compares the domain similarities calculated by different pre-trained models and we can see some consistent patterns across different architectures: e.g., The 1st and 2nd highest similarity scores are Caltech and Dogs across architectures; the 3rd and 4th highest similarity scores refers to Birds and Indoor; the most dissimilar datasets are Cars, Aircrafts and Flowers, though the relative orders for the dissimilar datasets (Cars, Aircrafts and Flowers) are not exactly the same.
An alternative way is to use the source model directly as the feature extractor for both source domain and target domain, which may captures the transfer learning process more precisely than a uniform feature extractor.
C T HE E FFECTIVENESS OF BN M OMENTUM
Kornblith et al.
(2018) conducts extensive ﬁne-tuning experiments with different hyperparameters. One observation they made is that the momentum parameter of BN layer is essential for ﬁnetuning.
They found it useful to decrease the BN momentum parameter from its ImageNet value to max(1 − 10 /s, 0 . 9) where s is the number of steps per epoch.
This will change the default BN momentum value (0.9) when s is larger than 100, but only applies when the dataset size is larger than 25.6K with batch size 256.
The maximum data size used in our experiments is Caltech-256, which is 15K, so this strategy seems not applicable.
We further validate the effect of BN momentum by performing a similar study as to ELR.
The goal is to identify whether there is an optimal BN momentum for a given task.
For each dataset, we ﬁne-tune the pre-trained model using previously obtained best hyperparameters and only vary BN momentum.
In addition to the default value 0.9, we also set it to 0.0, 0.95 and 0.99.
The rational is that BN mommentum is a critical hyperparameter, we can expect signiﬁcant performance differences.
As shown in Figure 9, we can see m bn = 0 .
99 slightly improves the performance for some datasets, however, there is no signiﬁcant performance difference among values greater than 0.9.
One hypothesis is that similar domains will share similar BN parameters and statistics, BN momentum may affect the parameter adaptation.
More investigation is still needed to fully understand.
[Figure]
D E XPERIMENTAL S ETTINGS FOR C OMPARISON OF L 2 AND L 2 -SP
The experiments in this section is based the code 5 provided by (Li et al., 2018).
The base network is ImageNet pretrained ResNet-101-V1.
The model is ﬁne-tuned with batch size 64 foriterations, and the learning rate is decayed at iteration 6000.
Following the original setting, we use momentum 0.9.
We performed grid search on learning rate and weight decay, with the range of η : { 0 . 02 , 0 . 01 , 0 .
and λ 1 : { 0 . 1 , 0 . 01 , 0 .
001 , 0 . 0001 } , and report the best average error for both methods.
For L 2 -SP norm, we follow the authors’ setting to use constant λ 2 = 0 .
Different with the original setting for L 2 regularization, we set λ
=
λ
1 to simulate the normal L 2 -norm.
E D ATA A UGMENTATION
Data augmentation is an important way of increasing data quantity and diversity to make models more robust.
It is even critical for transfer learning with few instances.
The effect of data augmentation can be viewed as a regularization method and the choice of data augmentation method is also a hyperparameter.
Most current widely used data augmentation methods have veriﬁed their effectiveness on training ImageNet models, such as random mirror ﬂipping, random rescaled cropping 6 , color jittering and etc (Szegedy et al., 2015; Xie et al., 2018) and they are also widely used for ﬁne-tuning.
Do these methods transfer for ﬁne-tuning on other datasets?
Here we compare three settings for data augmentation: 1) random resized cropping: our default data augmentation; 2) random crop: the same as standard data augmentation except that we use random cropping with ﬁxed size; 3) random ﬂip: simply random horizontal ﬂipping.
The effect of data augmentation is dataset dependent and affect the hyperparameters The training and validation errors of ﬁne-tuning with different data augmentation strategies are illustrated in Figure 10.
We ﬁnd that advanced cropping works signiﬁcantly better on datasets like Cars, Aircrafts and Flowers but performs worse on Dogs.
The choice of data augmentation methods has dramatic inﬂuence to the convergence behaviour.
Simpler data augmentation usually converge very quickly (e.g., in 20 epochs), while the training error for random resized cropping converges much slower.
We see that default hyperparemter and data augmentation method lead to overﬁtting on Dogs dataset.
This can be solved by disabling momentum as we can see in Table 2, and result in better performance than random cropping.
We can expect that random resized cropping adds extra variance to the gradient direction and the effect of disabling momentum is more obvious for this case.
Here we compare data augmentation methods with different momentum settings.
As can be seen in
Table 6, random resized cropping consistently outperforms random cropping on datasets like Cars,
Aircrafts and Flowers.
Using momentum improves the performance signiﬁcantly for both methods.
[Figure]
We see that advanced data augmentation method with default hyperparameters ( m = 0 .
9 and η = 0 .
01 ) leads to overﬁtting on Dogs and Caltech dataset (Figure 11 (a) and (c)).
Random resized cropping with zero momentum solves this problem and results in better performance than random cropping.
When momentum is disabled for random cropping, the performance is still better for
Dogs, but decreases for other datasets.
This can be expected as random cropping produces images with less variation and noise than random resized cropping, the gradients variation is less random and momentum can still point to the right direction.
This can be further veriﬁed as we increase the learning rate for random cropping, which adds variation to the gradients, and disabling momentum shows better performance than nonzero momentum on datasets that are close.
[Table]
[Figure]
[Figure]
F S OURCE D OMAINS
Transfer learning from similar source domains helps but does not guarantee good performance
We consider two ImageNet subsets: 449 Natural objects and 551 Man-made objects, following the splits of (Yosinski et al., 2014) (supplementary materials).
From the bottom of Table 7, we can see that ﬁne-tuning from ImageNet-Natural pre-trained models performs better on Birds and Dogs dataset, whereas Caltech-256 and Indoor beneﬁt more from ImageNet-Manmade pretrained models.
The performance gap between ImageNet-Manmad and ImageNet-Natural on Cars and Flowers are not as signiﬁcant as for Birds and Dogs.
It is surprising to see that ﬁne-tuning from ImageNetManmade subset yields worse performance than ImageNet-Natural on the Cars and Indoor dataset.
The ﬁne-tuning results on both subsets do not exceed the pre-trained models with full ImageNet.
For most tasks, training from scratch with default hyperparameters is much worse than ﬁne-tuning from ImageNet.
However, after slight hyperparameter tuning on learning rates and weight decay, the performance of training from scratch gets close to the default ﬁne-tuning result (e.g., Cars and
Aircrafts).
Scratch training HPO on Cars and Aircrafts even surpasses the ﬁne-tuning result with default results.
Previous studies (Kornblith et al., 2018; Cui et al., 2018) also identiﬁed that datasets like Cars and Aircrafts do not beneﬁt too much from ﬁne-tuning.
[Table]
Pre-trained models on ImageNet-Natural and ImageNet-Manmade
We train ResNet-101 from scratch on each subset using standard hyperparameters, i [Table] e., initial learning rate 0.1, batch size 256, momentum 0.9.
We train 180 epochs, learning rate is decayed at epoch 60 and 120 by a factor of 10.
Table 8 illustrates the Top-1 errors of training ResNet-101 on each source datasets
[Table]
[Table] Scratch Training HPO Figure 12 shows the training/validation errors of training from scratch on each dataset with different learning rate and weight decay.
We use initial learning rate 0.1, batch size 256.
For most dataset, we train 600 epochs, and decay the learning rate at epoch 400 andby a factor of 10.
The parameters to search is η ∈ [0 . 1 , 0 . 2 , 0 . 5] and λ ∈ [0 . 0001 , 0 . 0005] with ﬁxed momentum 0.9 and batch size 256.
We observe weight decay 0.0005 consistently performs better than 0.0001.
[Figure]
[Figure]
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