SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Shin'ya Yamaguchi†,‡* Daiki Chijiwa† Sekitoshi Kanai† Atsutoshi Kumagai† Hisashi Kashima‡ †NTT ¥Kyoto University
This paper investigates methods for improving generative data augmentation for deep learning. Generative data augmentation leverages the synthetic samples produced by generative models as an additional dataset for classification with small dataset settings. A key challenge of generative data augmentation is that the synthetic data contain uninformative samples that degrade accuracy. This is because the synthetic samples do not perfectly represent class categories in real data and uniform sampling does not necessarily provide useful samples for tasks. In this paper, we present a novel strategy for generative data augmentation called meta generative regularization (MGR). To avoid the degradation of generative data augmentation, MGR utilizes synthetic samples in the regularization term for feature extractors instead of in the loss function, e.g., cross-entropy. These synthetic samples are dynamically determined to minimize the validation losses through meta-learning. We observed that MGR can avoid the performance degradation of naive generative data augmentation and boost the baselines. Experiments on six datasets showed that MGR is effective particularly when datasets are smaller and stably outperforms baselines.
While deep neural networks achieved impressive performance on various machine learning tasks, training them still requires a large amount of labeled training data in supervised learning. The labeled datasets are expensive when a few experts can annotate the data, e.g., medical imaging. In such scenarios, generative data augmentation is a promising option for improving the performance of models. Generative data augmentation basically adds pairs of synthetic samples from conditional generative models and their target labels into real training datasets. The expectations of generative data augmentation are that the synthetic samples interpolate missing data points and perform as oversampling for classes with less real training samples [1]. This simple method can improve the performance of several tasks with less diversity of inputs such as medical imaging tasks [2, 3, 4, 5].
However, in general cases that require the recognition of more diverse inputs (e.g., CIFAR datasets [6]), generative data augmentation degrades rather than improves the test accuracy [7]. Previous studies have indicated that this can be caused by the low quality of synthetic samples in terms of the diversity and fidelity [7, 8]. If this hypothesis is correct, we can expect high-quality generative models (e.g., StyleGAN2-ADA [9]) to resolve the problem; existing generative data augmentation methods adopt earlier generative models e.g., ACGAN [10] and SNGAN [11]. Contrary to the expectation, this is not the case. We observed that generative data augmentation fails to improve models even when using a high-quality StyleGAN2-ADA (Figure 1). Although the samples partially appear to be real to humans, they are not yet sufficient to train classifiers in
*Corresponding author. Email: shinya.yamaguchi@ntt.com
Figure 1: Accuracy gain using meta generative regularization on Cars [12] with ResNet-18 classifier [13] and StyleGAN2-ADA [9] (FID: 9.5)
Figure 2: Meta generative regularization
existing generative data augmentation methods. This paper investigates methodologies for effectively extracting useful information from generative models to improve model performance.
We address this problem based on the following hypotheses. First, synthetic samples are actually informative but do not perfectly represent class categories in real data. This is based on a finding by Brock et al. [14] called "class leakage," where a class conditional synthetic sample contains attributes of other classes. For example, they observed failure samples including an image of "tennis ball" containing attributes of "dogs" (Figure 4(d) of [14]). These class leaked samples do not perfectly represent the class categories in the real dataset. If we use such class leaked samples for updating classifiers, the samples can distort the decision boundaries, as shown in Figure 3. Second, regardless of the quality, the generative models originally contain uninformative samples to solve the tasks. This is simply because the generative models are not explicitly optimized to generate informative samples for learning the conditional distribution  $p(y|x)$ ; they are optimized only for learning the data distribution  $p(x)$ . Further, the generative models often fail to capture the entire data distribution precisely due to their focus on the high-density regions. These characteristics of generative models might disturb the synthesis of effective samples for generative data augmentation. To maximize the gain from synthetic samples, we should select appropriate samples for training tasks.
In this paper, we present a novel regularization method called meta generative regularization (MGR). Based on the above hypotheses, MGR is composed of two techniques for improving generative data augmentation: pseudo consistency regularization (PCR) and meta pseudo sampling (MPS). PCR is a regularization term using synthetic samples in training objectives for classifiers. Instead of supervised learning with negative log-likelihood  $-\log p(y|x)$ , i.e., cross-entropy, on synthetic samples, we regularize the feature extractor to avoid the distortions on decision boundaries. That is, PCR leverages synthetic samples only for learning feature spaces. PCR penalizes the feature extractors by minimizing the gap between variations of a synthetic sample, which is inspired by consistency regularization in semi-supervised learning [15, 16, 17]. MPS corresponds to the second hypothesis and its objective is to select useful samples for training tasks by dynamically searching optimal latent vectors of the generative models. Therefore, we formalize MPS as a bilevel optimization framework of a classifier and a finder that is a neural network for searching latent vectors. Specifically, this framework updates the finder through meta-learning to reduce the validation loss and then updates the classifier to reduce the PCR loss (Figure 2). By combining PCR and MPS, we can improve the performance even when the existing generative data augmentation degrades the performance (Figure 1).
We conducted experiments with multiple vision datasets and observed that MGR can stably improve baselines on various settings by up to 7 percentage points of test accuracy. Further, through the visualization studies, we confirmed that MGR utilizes the information in synthetic samples to learn feature representations through PCR and obtain meaningful samples through MPS.
We consider a classification problem in which we train a neural network model  $f_{\theta}:\mathcal{X}\to \mathcal{Y}$  on a labeled dataset  $\mathcal{D} = \{(x^i,y^i)\in \mathcal{X}\times \mathcal{Y}\}_{i = 1}^{N}$ , where  $\mathcal{X}$  and  $\mathcal{Y}$  are the input and output label spaces,
Figure 3: Distortion of decision boundary caused by generative data augmentation with conditional synthetic samples leaking another class attribute. If the synthetic sample  $x_{\mathrm{p}}$  is "tennis ball dog" (reprinted from [18]) with its conditional label  $y_{\mathrm{p}} =$  "ball", the supervised learner of a task head  $h_{\omega}$  distorts the decision boundary of "dog" and "ball" to classify  $x_{\mathrm{p}}$  as "ball".
Figure 4: Meta pseudo sampling framework. We meta-optimize the finder  $F_{\phi}$  to generate a useful latent vector  $F_{\phi}(z)$  for training a model parameter  $\theta$  through minimizing the validation loss with the once updated parameter  $\theta'$  by a real sample  $x$  and a synthetic sample  $x_{\mathrm{p}} = G_{\Phi}(F_{\phi}(z))$ .
respectively. Here, we can use a generative model  $G_{\Phi}: \mathcal{Z} \times \mathcal{Y} \to \mathcal{X}$ , which is trained on  $\mathcal{D}$ . We assume that  $G_{\Phi}$  generates samples from a latent vector  $z \in \mathcal{Z}$  and conditions on samples with a categorical label  $y \in \mathcal{Y}$ , where  $z$  is sampled from a standard Gaussian distribution  $p(z) = \mathcal{N}(0, I)$  and  $y$  is uniformly sampled from  $\mathcal{Y}^2$ . We refer to the classification task on  $\mathcal{D}$  as the main task, and  $f_{\theta}$  as the main model.  $f_{\theta}$  is defined by a composition of a feature extractor  $g_{\psi}$  and a classifier  $h_{\omega}$ , i.e.,  $f_{\theta} = h_{\omega} \circ g_{\psi}$  and  $\theta = [\psi, \omega]$ . To validate  $f_{\theta}$ , we can use a small validation dataset  $\mathcal{D}_{\mathrm{val}} = \{(x_{\mathrm{val}}^i, y_{\mathrm{val}}^i) \in \mathcal{X} \times \mathcal{Y}\}_{i=1}^{N_{\mathrm{val}}}$ , which has no intersection with  $\mathcal{D}$  (i.e.,  $\mathcal{D} \cap \mathcal{D}_{\mathrm{val}} = \emptyset$ ).
2.2 Generative Data Augmentation
A typical generative data augmentation trains a main model  $f_{\theta}$  with both real data and synthetic data from the generative models [19]. We first generate synthetic samples to be utilized as additional training data for the main task. Most previous studies on generative data augmentation [19, 20, 8] adopt conditional generative models for  $G_{\Phi}$ , and generate a pseudo dataset  $\mathcal{D}_{\mathrm{p}}$  as
$$
\mathcal {D} _ {\mathrm {p}} = \left\{\left(x _ {\mathrm {p}} ^ {i}, y _ {\mathrm {p}} ^ {i}\right) \mid x _ {\mathrm {p}} ^ {i} = G _ {\Phi} \left(z ^ {i}, y _ {\mathrm {p}} ^ {i}\right) \right\} _ {i = 1} ^ {N _ {\mathrm {p}}} \tag {1}
$$
where  $z^i$  is sampled from a prior distribution  $p(z)$ , and  $y_{\mathrm{p}}^{i}$  is uniformly sampled from  $\mathcal{V}$ . Subsequently,  $f_{\theta}$  is trained on both of  $\mathcal{D}$  and  $\mathcal{D}_{\mathrm{p}}$  using the following objective function.
$$
\min  _ {\theta} \quad \mathcal {L} (\theta) + \lambda \mathcal {L} _ {p} (\theta), \tag {2}
$$
$$
\mathcal {L} (\theta) = \mathbb {E} _ {(x, y) \in \mathcal {D}} \ell \left(f _ {\theta} (x), y\right), \tag {3}
$$
$$
\mathcal {L} _ {\mathrm {p}} (\theta) = \mathbb {E} _ {\left(x _ {\mathrm {p}}, y _ {\mathrm {p}}\right) \in \mathcal {D} _ {\mathrm {p}}} \ell_ {\mathrm {p}} \left(f _ {\theta} \left(x _ {\mathrm {p}}\right), y _ {\mathrm {p}}\right), \tag {4}
$$
where  $\ell$  is a loss function of the main task (e.g., cross-entropy),  $\ell_{\mathrm{p}}$  is a loss function for the synthetic samples, and  $\lambda$  is a hyperparameter for balancing  $\mathcal{L}$  and  $\mathcal{L}_{\mathrm{p}}$ . In previous works,  $\ell_{\mathrm{p}}$  is often set the same as  $\ell$ . Although optimizing Eq. (2) with respect to  $\theta$  is expected to boost the test performance by interpolating or oversampling conditional samples [1], its naive application degrades the performance of  $f_{\theta}$  on general settings [7]. In this paper, we explore methods to resolve the degradation of generative data augmentation and maximize the performance gain from  $\mathcal{D}_{\mathrm{p}}$ .
In this section, we describe our proposed method, MGR. The training using MGR is formalized as alternating optimization of a main model and finder network for searching latent vectors of  $G_{\Phi}$ , as shown in Figure 2. To maximize the gain from synthetic samples, MGR regularizes a feature extractor  $g_{\psi}$  of  $f_{\theta}$  using PCR by effectively sampling useful samples for the generalization from  $G_{\Phi}$  using MPS. We show the overall algorithm of MGR in Appendix A.
2 Although we basically use conditional  $G_{\Phi}$  for comparing our method and generative data augmentation, our method can be used with unconditional  $G_{\Phi}$  (see Sec. 4.6).
3.1 Pseudo Consistency Regularization
As discussed in Section 1, we hypothesize that the synthetic samples do not perfectly represent class categories, and training classifiers using them can distort the decision boundaries. This is because  $y_{\mathrm{p}}$  is not reliable due to  $\mathcal{D}_{\mathrm{p}}$  can contain class leaked samples [14]. To avoid the degradation caused by the distortion, we propose utilizing  $x_{\mathrm{p}}$  to regularize only the feature extractor  $g_{\psi}$  of  $f_{\theta}$  by discarding a conditional label  $y_{\mathrm{p}}$ . For the regularization, we borrow the concept of consistency regularization, which was originally proposed for semi-supervised learning (SSL) [15, 16, 17]. These SSL methods were designed to minimize the dissimilarity between the two logits (i.e., the output of  $h_{\omega}$ ) of strongly and weakly transformed unlabeled samples to obtain robust representations. By following this concept, the PCR loss is formalized as
$$
\ell_ {\mathrm {P C R}} \left(x _ {\mathrm {p}}; \psi\right) = \left\| g _ {\psi} \left(T \left(x _ {\mathrm {p}}\right)\right) - g _ {\psi} \left(x _ {\mathrm {p}}\right) \right\| _ {2} ^ {2}, \tag {5}
$$
where  $T$  is a strong transformation such as RandAugment [21], which is similar to the one used in UDA [16] for SSL. The difference between PCR and UDA is that PCR penalizes only  $g_{\psi}$ , whereas UDA trains the entire  $f_{\theta} = h_{\omega} \circ g_{\psi}$ .  $\ell_{\mathrm{PCR}}$  can be expected to help  $g_{\psi}$  learns features of inter-cluster interpolated by  $x_{\mathrm{p}}$  without distorting the decision boundaries. Using  $\ell_{\mathrm{PCR}}$ , we rewrite Eq. (4) as
$$
\mathcal {L} _ {\mathrm {P C R}} (\theta) = \mathbb {E} _ {x _ {\mathrm {p}} \in \mathcal {D} _ {\mathrm {p}}} \ell_ {\mathrm {P C R}} (x _ {\mathrm {p}}; \psi). \tag {6}
$$
3.2 Meta Pseudo Sampling
Most generative data augmentation methods generate a synthetic sample  $x_{\mathrm{p}}$  from  $G_{\Phi}$  with a randomly sampled latent vector  $z$ . This is not the best option as  $G_{\Phi}$  is not optimized for generating useful samples to train  $f_{\theta}$  in predicting the conditional distribution  $p(y|x)$ ; it is originally optimized to replicate  $p(x|y)$  or  $p(x)$ . The main concept of MPS is to directly determine useful samples for training  $f_{\theta}$  by optimizing an additional neural network called a finder  $F_{\phi}: \mathcal{Z} \to \mathcal{Z}$ .  $F_{\phi}$  takes a latent vector  $z \sim p(z)$  as input and outputs a vector of the same dimension as  $z$ . By using  $F_{\phi}$ , we generate a synthetic sample as  $x_{\mathrm{p}} = G_{\Phi}(F_{\phi}(z), y)$ . The role of  $F_{\phi}$  is to find the optimal latent vectors that improve the training of  $f_{\theta}$  through  $x_{\mathrm{p}}$ . Although we can consider optimizing  $G_{\Phi}$  instead of  $F_{\phi}$ , we optimize  $F_{\phi}$  because the previous work showed that transforming latent variables according to loss functions efficiently reaches the desired outputs [22] and we observed that optimizing  $G_{\Phi}$  is unstable and causes the performance degradations of  $f_{\theta}$  (Section 4.5).
The useful samples for generalization should reduce the validation loss of  $f_{\theta}$  by using them for optimization. Based on this simple suggestion, we formalize the following bilevel optimization problem for  $F_{\phi}$ .
$$
\min  _ {\phi} \mathcal {L} _ {\text {v a l}} \left(\theta^ {*}\right) = \mathbb {E} _ {\left(x _ {\text {v a l}}, y _ {\text {v a l}}\right) \in \mathcal {D} _ {\text {v a l}}} \ell \left(f _ {\theta^ {*}} \left(x _ {\text {v a l}}\right), y _ {\text {v a l}}\right)
$$
$$
\text {s u b j e c t} \quad \theta^ {*} = \underset {\theta} {\operatorname {a r g m i n}} \mathcal {L} (\theta) + \lambda \mathcal {L} _ {\mathrm {P C R}} (\theta , \phi). \tag {7}
$$
Note that the finder parameter  $\phi$  is added to the arguments of  $\mathcal{L}_{\mathrm{PCR}}$ . We can optimize  $F_{\phi}$  with a standard gradient descent algorithm because  $F_{\phi}$ ,  $G_{\Phi}$ , and  $f_{\theta}$  are all composed of differentiable functions as well as existing meta-learning methods such as MAML [23] and DARTS [24]. We approximate  $\theta^{*}$  because the exact computation of the gradient  $\nabla_{\phi} \mathcal{L}_{\mathrm{val}}(\theta^{*})$  is expensive [23, 24]:
$$
\theta^ {*} \approx \theta^ {\prime} = \theta - \eta \nabla_ {\theta} (\mathcal {L} (\theta) + \lambda \mathcal {L} _ {\mathrm {P C R}} (\theta , \phi)), \tag {8}
$$
where  $\eta$  is an inner step size. Thus, we update  $F_{\phi}$  by using  $\theta^{\prime}$  that is updated for a single step and then alternately update  $\theta$  by applying Eq. (6) with  $x_{\mathrm{p}}$  generated from the updated  $F_{\phi}$ . The overall optimization flow is shown in Figure 4.
Approximating gradients with respect to  $\phi$ . Computing  $\nabla_{\phi} \mathcal{L}_{\mathrm{val}}(\theta')$  requires the product computation including second-order gradients:  $\nabla_{\phi, \theta}^{2} \mathcal{L}_{\mathrm{PCR}}(\theta, \phi) \nabla_{\theta'} \mathcal{L}_{\mathrm{val}}(\theta')$ . This causes a computation complexity of  $\mathcal{O}((|\Phi| + |\phi|)|\theta|)$ . To avoid this computation, we approximate the term using the finite difference method [24] as
$$
\nabla_ {\phi , \theta} ^ {2} \mathcal {L} _ {\mathrm {P C R}} (\theta , \phi) \nabla_ {\theta^ {\prime}} \mathcal {L} _ {\text {v a l}} \left(\theta^ {\prime}\right) \approx \frac {\nabla_ {\phi} \mathcal {L} _ {\mathrm {P C R}} \left(\theta^ {+} , \phi\right) - \nabla_ {\phi} \mathcal {L} _ {\mathrm {P C R}} \left(\theta^ {-} , \phi\right)}{2 \varepsilon}, \tag {9}
$$
where  $\theta^{\pm}$  is  $\theta$  updated by  $\theta \pm \varepsilon \eta \nabla_{\theta} \mathcal{L}_{\mathrm{val}}(\theta)$ .  $\varepsilon$  is defined by  $\frac{\text{const.}}{\|\nabla_{\theta} \mathcal{L}_{\mathrm{val}}(\theta)\|_2}$ . We used 0.01 of the constant for  $\varepsilon$  based on [24]. This approximation reduces the computation complexity to  $\mathcal{O}(|\Phi| + |\phi| + |\theta|)$ . We confirm the speedup by Eq. (9) in Appendix B.1.
Techniques for improving finder We introduce two techniques for improving the training of  $F_{\phi}$  in terms of the architectures and a penalty term for the outputs.
While arbitrary neural architectures can be used for the implementation of  $F_{\phi}$ , we observed that the following residual architecture produces better results.
$$
F _ {\phi} (z) := z + \tanh  (\mathrm {M L P} _ {\phi} (z)), \tag {10}
$$
where MLP is multi layer perceptron.
To ensure that  $F_{\phi}(z)$  does not diverge too far from the distribution  $p(z)$ , we add a Kullback-Leibler divergence term  $D_{\mathrm{KL}}(p_{\phi}(z)\| p(z))$ , where  $p_{\phi}(z)$  is the distribution of  $F_{\phi}(z)$  into Eq. (7). When  $p(z)$  follows the standard Gaussian  $\mathcal{N}(0,I)$ ,  $D_{\mathrm{KL}}(p_{\phi}(z)\| p(z))$  can be computed by
$$
D _ {\mathrm {K L}} \left(p _ {\phi} (z) \| p (z)\right) = - \frac {1}{2} \left(1 + \log \sigma_ {\phi} - \mu_ {\phi} ^ {2} - \sigma_ {\phi}\right), \tag {11}
$$
where  $\mu_{\phi}$  and  $\sigma_{\phi}$  is the mean and variance of  $\{F_{\phi}(z^i)\}_{i = 1}^{N_{\mathrm{P}}}$ . In Appendix B.2, we discuss the effects of design choice based on the ablation study.
In this section, we evaluate our MGR (the combination of PCR and MPS with the experiments on multiple image classification datasets. We mainly aim to answer three research questions with the experiments: (1) Can PCR avoid the negative effects of  $x_{\mathrm{p}}$  in existing methods and improve the performance of  $f_{\theta}$ ? (2) Can MPS find better samples for training than those by uniform sampling? (3) How practical is the performance of MGR? We compare MGR with baselines including conventional generative data augmentation and its variants in terms of test performance (Sec. 4.2 and 4.3). Furthermore, we conduct a comprehensive analysis of PCR and MPS such as the visualization of trained feature spaces (Sec. 4.4), quantitative/qualitative evaluations of the synthetic samples (Sec. 4.5), performance studies when changing generative models (Sec. 4.6), and comparison to data augmentation methods such as TrivialAugment [25] (Sec. 4.7).
Baselines. We compare our method with the following baselines. Base Model: training  $f_{\theta}$  with only  $\mathcal{D}$ . Generative Data Augmentation (GDA): training  $f_{\theta}$  with  $\mathcal{D}$  and  $G_{\Phi}$  using Eq. (2). GDA+MH: training  $f_{\theta}$  with  $\mathcal{D}$  and  $G_{\Phi}$  by decoupling the heads into  $h_{\omega}$  for  $\mathcal{D}$  and  $h_{\omega_{\mathrm{p}}}$  for  $\mathcal{D}_{\mathrm{p}}$ . MH denotes multi-head. This is a naive approach to avoid the negative effect of  $x_{\mathrm{p}}$  on  $h_{\omega}$  by not passing  $x_{\mathrm{p}}$  through  $h_{\omega}$ . GDA+MH optimizes the parameters as  $\underset{\theta}{\operatorname{argmin}} \mathcal{L}(f_{\theta}(x), y) + \lambda \mathcal{L}(h_{\omega_{\mathrm{p}}} (g_{\psi}(x_{\mathrm{p}})), y_{\mathrm{p}})$ .
$\mathbf{GDA} + \mathbf{SSL}$ : training  $f_{\theta}$  with  $\mathcal{D}$  and  $G_{\Phi}$  by applying an SSL loss for  $\mathcal{D}_{\mathrm{p}}$  that utilizes the output of  $h_{\omega}$  unlike PCR. This method was originally proposed by Yamaguchi et al. [26] for transfer learning, but we note that  $G_{\Phi}$  was trained on the main task dataset  $\mathcal{D}$  in contrast to the original paper. By following [26], we used UDA [16] as the SSL loss and the same strong transformation  $T$  as of MGR for the consistency regularization.
Datasets. We used six image datasets for classification tasks in various domains: Cars [12], Aircraft [27], Birds [28], DTD [29], Flowers [30], and Pets [31]. Furthermore, to evaluate smaller dataset cases, we used subsets of Cars that were reduced by  $\{10, 25, 50, 75\}\%$  in volume; we reduced them by random sampling on a fixed random seed. We randomly split a dataset into  $9:1$  and used the former as  $\mathcal{D}$  and the latter as  $\mathcal{D}_{\mathrm{val}}$ .
Architectures. We used ResNet-18 [13] as  $f_{\theta}$  and generators of conditional StyleGAN2-ADA for  $256 \times 256$  images [18] as  $G_{\Phi}$ .  $F_{\phi}$  was composed of a three-layer perceptron with a leaky-ReLU activation function. We used the ImageNet pre-trained weights of ResNet-18 distributed by PyTorch. For StyleGAN2-ADA, we did not use pre-trained weights. We trained  $G_{\Phi}$  on each  $\mathcal{D}$  from scratch according to the default setting of the implementation of StyleGAN2-ADA. Note that we used the same  $G_{\Phi}$  in the baselines and our proposed method.
3 https://github.com/pytorch/vision 4 https://github.com/NVlabs/stylegan2-ada
Table 1: Top-1 accuracy (\%) of ResNet18. Underlined scores outperform that of Base Model, and Bolded scores are the best among the methods.
(a) Multiple Datasets
Method / Dataset	Cars	Aircraft	Birds	DTD	Flower	Pets
Base Model	85.80±10	62.61±79	72.24±32	68.16±35	94.18±08	87.21±13
GDA	84.50±25	61.29±05	67.55±11	67.68±37	93.46±15	87.37±21
GDA+MH	85.77±10	63.16±15	71.95±09	68.49±21	93.83±11	87.77±10
GDA+SSL	85.75±21	62.87±54	71.28±42	68.49±59	93.66±08	87.75±21
GDA+MPS	85.28±05	62.11±34	72.25±13	68.87±22	94.39±14	87.92±11
PCR	86.36±08	64.43±21	73.69±10	69.17±18	94.66±22	88.59±44
MGR (PCR+MPS)	87.22±15	65.11±57	74.24±34	69.53±28	95.42±20	88.98±01
(b) Small Datasets (Cars)
Method / Dataset size	10%	25%	50%	75%
Base Model	20.11±.03	49.33±.54	72.91±.38	81.68±.18
GDA	18.91±.54	46.56±.07	68.81±.52	80.67±.10
GDA+MH	18.40±.93	46.63±.32	71.51±.10	81.25±.32
GDA+SSL	19.71±.41	47.80±.30	71.99±.40	81.55±.38
MGR	23.49±.53	53.16±.32	75.24±.21	83.13±.27