SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 10: Statistics of small-scale natural image datasets.
Datasets	Tasks	# Classes	# Samples	# Average samples per class
Caltech101	Coarse-grained object classification	102	3,060	30
CIFAR100-Subset	Coarse-grained object classification	100	10,000	100
Standard Cars	Fine-grained object classification	196	8,144	42
Oxford 102 Flowers	Fine-grained object classification	102	6,552	64
Oxford-IITT Pets	Fine-grained object classification	37	3,842	104
Describable Textures (DTD)	Texture classification	47	3,760	80
The statistics of medical image datasets. To evaluate the effect of dataset expansion on medical images, we conduct experiments on three small-scale medical image datasets. These datasets cover a wide range of medical image modalities, including breast ultrasound (i.e., BreastMNIST [1]), colon pathology (i.e., PathMNIST [36]), and Abdominal CT (i.e., OrganSMNIST [88]). We provide detailed statistics for these datasets in Table 11.
Table 11: Statistics of small-scale medical image datasets. To better simulate the scenario of small medical datasets, we use the validation sets of BreastMNIST and PathMNIST for experiments instead of training sets, whereas OrganSMNIST is based on its training set.
Datasets	Data Modality	# Classes	# Samples	# Average samples per class
BreastMNIST [1, 90]	Breast Ultrasound	2	78	39
PathMNIST [36, 90]	Colon Pathology	9	10,004	1,112
OrganSMNIST [88, 90]	Abdominal CT	11	13,940	1,267
F More Experimental Results and Discussion
F.1 More comparisons to expansion with augmentations
F.1.1 More results on expansion efficiency
In Figure 4, we have demonstrated the expansion efficiency of our proposed GIF over Cutout, GridMask and RandAugment on the Cars, DTD and Pets datasets. Here, we further report the results on Caltech101, Flowers, and CIFAR100-Subset datasets. As shown in Figure 18,  $5 \times$  expansion by GIF-SD and GIF-DALLE has already performed comparably to  $20 \times$  expansion of these augmentation methods, while  $10 \times$  expansion by GIF-SD and GIF-DALLE outperforms  $20 \times$  expansion by these data augmentation methods a lot. This result further demonstrates the effectiveness and efficiency of our GIF, and also reflects the importance of automatically creating informative synthetic samples for model training.
Figure 18: Accuracy of ResNet-50 trained from scratch on the expanded datasets with different expansion ratios based on Caltech101, Flowers, and CIFAR100-Subset datasets.
F.1.2 Comparison to Mixup and CutMix
We further compare our method to more advanced augmentation methods. Specifically, we apply Mixup-based methods, i.e., Mixup [99] and CutMix [96], to expand CIFAR100-Subset by  $5 \times$  and use the expanded dataset to train the model from scratch. As shown in Table 12, GIF-SD performs much better than Mixup and CutMix, further demonstrating the superiority of our method over augmentation-based expansion methods.
Table 12: Comparison between GIF and Mixup methods for expanding CIFAR100-Subset by  $5 \times$ .
CIFAR100-Subset	Accuracy
Original dataset	35.0±1.7
Expanded dataset
5×-expanded by Mixup [99]	45.6±1.2
5×-expanded by CutMix [96]	50.7±0.2
5×-expanded by GIF-SD	61.1±0.8
F.1.3 Comparison with an advanced generative method
We further compare our method with an advanced generative method [26] for dataset expansion. This method includes strategies like language enhancement (LE) [26] and CLIP Filter (CF) [26]. We use this method to expand the CIFAR100-S dataset based on Stable Diffusion (SD). As shown in the following table, SD combined with the method [26] is still noticeably inferior to our GIF-SD for both training from scratch and CLIP tuning. This further demonstrates the superiority of our method.
Table 13: Comparison between GIF and the method [26] for expanding CIFAR100-Subset by  $5 \times$ .
CIFAR100-S	Training from scratch	CLIP fine-tuning
Original dataset	35.0	75.2
5x-expanded dataset by SD+method [26]	55.1 (+20.1)	77.0 (+1.8)
5x-expanded dataset by GIF-SD (ours)	61.1 (+26.1)	79.4 (+4.2)
F.1.4 Comparison to infinite data augmentation
The training time varies based on the specific datasets. However, it is pivotal to note that all dataset expansion methods were compared based on the same expansion ratio, thus ensuring consistent training time/cost and fair comparisons. We acknowledge that training on an expanded dataset will inevitably take longer than training on the original dataset. However, as shown in Table 1 (cf. Section 5.1), the significant improvement in model performance (i.e., by  $36.9\%$  on average over six natural image datasets and by  $13.5\%$  on average over three medical datasets) makes the increased investment in training time worthwhile.
Despite this, one may wonder how the explored dataset expansion would perform compared to training with infinite data augmentation. Therefore, in this appendix, we further evaluate the performance of infinite data augmentation on the CIFAR100-Subset. Specifically, based on RandAugment, we train ResNet-50 using infinite online augmentation for varying numbers of epochs from 100 to 700. As shown in Table 14, using RandAugment to train models for more epochs leads to better performance, but gradually converges (around  $51\%$  accuracy at 500 epochs) and keeps fluctuating afterward. By contrast, our proposed method proves advantageous with the same training consumption costs: training the model on the original CIFAR100-S dataset for  $5\mathrm{x}$  more epochs performs much worse than the model trained on our  $5\mathrm{x}$ -expanded dataset. This comparison further underscores the effectiveness of our method in achieving higher accuracy without inflating training costs.
Table 14: Comparison between GIF-SD and infinite data augmentation on CIFAR100-Subset. Here, consumption costs equal data number  $\times$  training epoch.
Methods	Epochs	Consumption	Accuracy
Original
Standard training	100	1 million	35.0±1.7
Training with RandAugment	100	1 million	39.6±2.5
Training with RandAugment	200	2 million	46.9±0.9
Training with RandAugment	300	3 million	48.1±0.6
Training with RandAugment	400	4 million	49.6±0.4
Training with RandAugment	500	5 million	51.3±0.3
Training with RandAugment	600	6 million	51.1±0.3
Training with RandAugment	700	7 million	50.6±1.1
Expanded
5×-expanded by GIF-SD	100	6 million	61.1±0.8
F.1.5 Discussion of picking related samples from larger datasets
Picking and labeling data from larger image datasets with CLIP is an interesting idea for dataset expansion. However, such a solution is limited in real applications, since a large-scale related dataset may be unavailable in many image domains (e.g., medical image domains). Moreover, selecting data from different image domains (e.g., from natural images to medical images) is unhelpful for dataset expansion. Despite the above limitations in real applications, we also evaluate this idea on CIFAR100-Subset and investigate whether it helps dataset expansion when there is a larger dataset of the same image nature, e.g., ImageNet. Here, we use CLIP to select and annotate related images from ImageNet to expand CIFAR100-Subset. Specifically, we scan over all ImageNet images and use CLIP to predict them to the class of CIFAR100-Subset. We select the samples with the highest prediction probability higher than 0.1 and expand each class by  $5 \times$ . As shown in Table 15, the idea of picking related images from ImageNet makes sense, but performs worse than our proposed method. This result further demonstrates the effectiveness and superiority of our method. In addition, how to better transfer large-scale datasets to expand small datasets is an interesting open question, and we expect to explore it in the future.
Table 15: Comparison between GIF and picking related data from ImageNet for expanding CIFAR100-Subset by  $5 \times$ .
CIFAR100-Subset	Accuracy
Original dataset	35.0±1.7
Expanded dataset
5×-expanded by picking data from ImageNet with CLIP	50.9±1.1
5×-expanded by GIF-SD	61.1±0.8
F.2 More results of benefits to model generalization
In CIFAR100-C [27], there are 15 types of OOD corruption (as shown in Table 16), i.e., Gaussian noise, shot noise, impulse noise, defocus blur, glass blue, motion blur, zoom blur, snow, frost, fog, brightness, contrast, elastic transformation, pixelation, and JPEG compression. Each corruption type has 5 different severity levels: the larger severity level means more severe distribution shifts between CIFAR100 and CIFAR100-C. In Table 2 of the main paper, we have shown the empirical benefit of our method to model out-of-distribution (OOD) generalization based on CIFAR100-C with the severity level 3. Here, we further report its performance on CIFAR100-C with other severity levels. As shown in Table 16, our method is able to achieve consistent performance gains across all severity levels, which further verifies the benefits of GIF to model OOD generalization.
Table 16: Corruption Accuracy of ResNet-50 trained from scratch on CIFAR100-S and our  $5 \times$  expanded dataset, under 15 types of corruption in CIFAR100-C with various severity levels.
(a) CIFAR100-C with the severity level 1
Dataset	Noise	Blur	Weather	Digital
Gauss.	Shot	Impul.	Defoc.	Glass	Motion	Zoom	Snow	Frost	Fog	Brit.	Contr.	Elastic	Pixel	JPEG	Average
Original	25.6	29.3	25.0	34.2	32.2	31.7	30.9	32.3	28.3	31.8	33.7	29.2	31.7	34.1	30.9	30.7
5×-expanded by GIF-SD	50.3	54.6	50.8	59.2	29.4	53.7	51.9	53.1	54.0	58.7	59.5	57.1	52.5	57.9	54.7	53.2 (+22.5)
20×-expanded by GIF-SD	55.0	60.5	54.8	66.1	30.2	56.0	58.0	61.1	62.2	65.1	66.2	64.3	59.2	63.8	60.8	58.9 (+27.2)
(b) CIFAR100-C with the severity level 2
Dataset	Noise	Blur	Weather	Digital
Gauss.	Shot	Impul.	Defoc.	Glass	Motion	Zoom	Snow	Frost	Fog	Brit.	Contr.	Elastic	Pixel	JPEG	Average
Original	18.6	24.4	17.4	32.5	31.9	28.3	29.8	28.4	22.9	23.6	31.1	16.3	30.8	33.7	29.2	26.6
5×-expanded by GIF-SD	39.5	48.8	41.7	56.3	29.6	46.4	49.7	45.2	46.4	52.8	57.6	45.5	52.1	54.2	51.1	47.8(+21.2)
20×-expanded by GIF-SD	42.7	53.7	43.9	63.1	31.2	51.8	56.1	52.0	54.9	60.4	65.2	54.3	59.2	60.0	55.6	52.3(+25.7)
(c) CIFAR100-C with the severity level 3
Dataset	Noise	Blur	Weather	Digital
Gauss.	Shot	Impul.	Defoc.	Glass	Motion	Zoom	Snow	Frost	Fog	Brit.	Contr.	Elastic	Pixel	JPEG	Average
Original	12.8	17.0	12.5	30.5	31.7	25.2	28.6	26.5	19.0	18.6	28.3	11.5	29.5	33.6	28.8	23.6
5×-expanded by GIF-SD	29.7	36.4	32.7	51.9	32.4	39.2	46.0	45.3	38.1	47.1	55.7	37.3	48.6	53.2	49.4	43.3 (+19.3)
20×-expanded by GIF-SD	31.8	39.2	34.7	58.4	33.4	43.1	51.9	51.7	47.4	55.0	63.3	46.5	54.9	58.0	53.6	48.2 (+24.6)
(d) CIFAR100-C with the severity level 4
Dataset	Noise	Blur	Weather	Digital
Gauss.	Shot	Impul.	Defoc.	Glass	Motion	Zoom	Snow	Frost	Fog	Brit.	Contr.	Elastic	Pixel	JPEG	Average
Original	10.8	14.3	7.7	28.5	29.3	25.2	27.8	23.3	19.5	14.1	24.9	7.4	29.0	33.0	28.1	21.5
5×-expanded by GIF-SD	25.3	31.2	18.0	45.1	21.4	39.6	42.5	41.7	37.7	40.2	52.1	26.1	44.2	47.8	48.2	37.4 (+15.9)
20×-expanded by GIF-SD	27.4	33.7	20.2	50.7	21.7	43.9	47.8	48.8	46.7	47.6	60.7	35.3	47.9	49.3	51.2	42.2 (+20.7)
(e) CIFAR100-C with the severity level 5
Dataset	Noise	Blur	Weather	Digital
Gauss.	Shot	Impul.	Defoc.	Glass	Motion	Zoom	Snow	Frost	Fog	Brit.	Contr.	Elastic	Pixel	JPEG	Average
Original	9.4	10.7	5.5	24.9	28.9	22.3	25.9	19.4	16.6	8.2	18.3	2.7	29.0	31.8	27.3	18.7
5×-expanded by GIF-SD	21.4	23.8	10.8	31.8	22.8	33.1	37.6	38.1	31.1	24.7	43.7	8.6	38.6	36.0	45.6	29.8 (+11.1)
20×-expanded by GIF-SD	22.9	25.5	11.1	33.5	24.1	36.2	41.8	46.4	38.4	32.1	53.5	13.9	40.4	32.0	48.8	33.4 (+14.7)
F.3 More results of applicability to various model architectures
In Table 3, we have demonstrated the generalizability of our expanded Cars dataset to various model architectures. Here, we further apply the expanded Caltech101, Flowers, DTD, CIFAR100-S, and Pets datasets  $(5\times$  expansion ratio) by GIF-SD and GIF-DALLE to train ResNeXt-50 [85], WideResNet-50 [97] and MobileNet V2 [64] from scratch. Table 17 shows that our expanded datasets bring consistent performance gains for all the architectures on all datasets. This further affirms the versatility of our expanded datasets, which, once expanded, are readily suited for training various model architectures.
Table 17: Model performance of various model architectures trained on  $5 \times$  expanded natural image datasets by GIF.
Dataset	Caltech101 [18]
ResNet-50	ResNeXt-50	WideResNet-50	MobileNet-v2	Avg.
Original dataset	26.3±1.0	32.6±0.5	34.7±0.8	33.8±1.1	31.9
5×-expanded by GIF-DALLE	57.3±0.4	55.2±0.1	61.8±0.5	59.4±0.7	58.4 (+26.5)
5×-expanded by GIF-SD	54.4±0.7	52.8±1.1	60.7±0.3	55.6±0.5	55.9 (+24.0)
Dataset	Cars [40]
ResNet-50	ResNeXt-50	WideResNet-50	MobileNet-v2	Avg.
Original dataset	19.8±0.9	18.4±0.5	32.0±0.8	26.2±4.2	24.1
5×-expanded by GIF-DALLE	53.1±0.2	43.7±0.2	60.0±0.6	47.8±0.6	51.2 (+27.1)
5×-expanded by GIF-SD	60.6±1.9	64.1±1.3	75.1±0.4	60.2±1.6	65.0 (+40.9)
Dataset	Flowers [49]
ResNet-50	ResNeXt-50	WideResNet-50	MobileNet-v2	Avg.
Original dataset	74.1±0.1	75.8±1.2	79.3±1.6	85.5±1.0	78.7
5×-expanded by GIF-DALLE	82.8±0.5	81.6±0.4	84.6±0.2	88.8±0.5	84.4 (+5.7)
5×-expanded by GIF-SD	82.1±1.7	82.0±1.2	85.0±0.6	89.0±0.1	84.5 (+5.8)
Dataset	DTD [10]
ResNet-50	ResNeXt-50	WideResNet-50	MobileNet-v2	Avg.
Original dataset	23.1±0.2	25.4±0.6	26.1±0.6	28.1±0.9	25.7
5×-expanded by GIF-DALLE	31.2±0.9	30.6±0.1	35.3±0.9	37.4±0.8	33.6 (+7.9)
5×-expanded by GIF-SD	33.9±0.9	33.3±1.6	40.6±1.7	40.8±1.1	37.2 (+11.5)
Dataset	CIFAR100-S [41]
ResNet-50	ResNeXt-50	WideResNet-50	MobileNet-v2	Avg.
Original dataset	35.0±3.2	36.3±2.1	42.0±0.3	50.9±0.2	41.1
5×-expanded by GIF-DALLE	54.5±1.1	52.4±0.7	55.3±0.3	56.2±0.2	54.6 (+13.5)
5×-expanded by GIF-SD	61.1±0.8	59.0±0.7	64.4±0.2	62.4±0.1	61.4 (+20.3)
Dataset	Pets [50]
ResNet-50	ResNeXt-50	WideResNet-50	MobileNet-v2	Avg.
Original dataset	6.8±1.8	19.0±1.6	22.1±0.5	37.5±0.4	21.4
5×-expanded by GIF-DALLE	46.2±0.1	52.3±1.5	66.2±0.1	60.3±0.3	56.3 (+34.9)
5×-expanded by GIF-SD	65.8±0.6	56.5±0.6	70.9±0.4	60.6±0.5	63.5 (+42.1)
F.4 More discussions on CLIP
In the following subsections, we provide more discussions on the comparisons with CLIP.
F.4.1 Why not directly transfer CLIP models to target datasets?
In our proposed GIF framework, we leverage the pre-trained CLIP model to guide dataset expansion. An inevitable question might be: why not directly use or transfer the CLIP model to the target dataset, especially given its proven effectiveness on many natural image datasets? Before delving into that, it is important to note that we aim to tackle small-data scenarios, where only a limited-size dataset is available and there are no large-scale external datasets with a similar nature to the target dataset. Consequently, training a new CLIP model on the target dataset (e.g., in the medical image domains) is not feasible. Therefore, we rely on publicly available CLIP models for dataset expansion. Compared to directly using or transferring CLIP models, our dataset expansion introduces a necessary new paradigm for two primary reasons as follows.
First, our GIF method has better applicability to scenarios across various image domains. While CLIP demonstrates good transfer performance on certain natural image datasets, it struggles to achieve this performance on other domains, such as medical image datasets. To illustrate this, we test the linear-probing and fine-tuning performance of the CLIP-trained ResNet-50 model on three medical datasets. As shown in Table 18, directly employing or transferring the CLIP model yielded unsatisfactory results or only marginally improved performance—significantly underperforming compared to our dataset expansion approach. The limited transfer performance is attributed to the fact that, when the pre-trained datasets are highly different from the target datasets, the pre-training weights do not significantly bolster performance compared to training from scratch [55]. Such an issue cannot be resolved by conducting CLIP pre-training on these domains, since there is no large-scale dataset of similar data nature to the target dataset in real scenarios. In contrast, our GIF framework is capable of generating images of similar nature as the target data for dataset expansion, enhancing its applicability to real-world scenarios across diverse image domains.
Second, our dataset expansion can provide expanded datasets suitable for training various network architectures. In certain practical scenarios, such as mobile terminals, the permissible model size is severely limited due to hardware constraints. Nonetheless, the publicly available CLIP checkpoints are restricted to ResNet-50, ViT-B/32, or even larger models, which may not be viable in these constrained settings. In contrast, the expanded dataset by our method can be readily employed to train a various range of model architectures (cf. Table 3), making it more applicable to scenarios with hardware limitations. One might suggest using CLIP in these situations by conducting knowledge distillation from large CLIP models to facilitate the training of smaller model architectures. However, as indicated in Table 1 and Table 18, although knowledge distillation of CLIP does enhance model performance on most datasets, the gains are limited. This arises from two key limitations of CLIP knowledge distillation. First, distillation can only yield marginal improvements when the performance of CLIP on the target dataset (e.g., medical image domains) is not good. Second, distillation tends to be ineffective when there is a mismatch between the architectures of student and teacher models [9, 71]. This comparison further underscores the advantages of our method for training various network architectures, while the CLIP model architectures are fixed and not editable.
Table 18: Comparison between our methods and directly fine-tuning CLIP models on three medical image datasets. All results are averaged over three runs.
Dataset	PathMNIST	BreastMNIST	OrganSMNIST
Original dataset	72.4±0.7	55.8±1.3	76.3±0.4
CLIP linear probing	74.3±0.1	60.0±2.9	64.9±0.2
CLIP fine-tuning	78.4±0.9	67.2±2.4	78.9±0.1
CLIP knowledge distillation	77.3±1.7	60.2±1.3	77.4±0.8
5×-expanded by GIF-SD	86.9±0.3	77.4±1.8	80.7±0.2
F.4.2 Discussion on when to use GIF over zero-shot CLIP models
In Table 1, it is noted that while zero-shot CLIP performs well on datasets like Caltech 101 and Pets, it struggles with medical image datasets. This poses the question: when should we prefer GIF over pre-trained CLIP models? Although zero-shot CLIP outperforms our GIF-SD on the Caltech 101 and Pets datasets, our method demonstrates superior overall performance across six natural image datasets, as well as medical image datasets (see Table 1). Thus, we recommend using our method as the primary option.
Meanwhile, if the target dataset has a high distributional similarity with the CLIP training dataset, it may also be beneficial to consider CLIP as an alternative and see whether it can achieve better performance. Nevertheless, it is important to note that, as discussed in Section 5.1 and Appendix F.4.1, CLIP is less effective in some specific application scenarios. For instance, its performance on non-natural image domains like medical images is limited (as shown in Table 4). Additionally, publicly available CLIP checkpoints are restricted to larger models like ResNet-50 and ViT-B/32, making them unsuitable for scenarios with hardware constraints (e.g., mobile terminals) where smaller model sizes are necessary. In these scenarios, our proposed method exhibits promising performance (as shown in Tables 3 and 4), offering a more versatile solution.
F.5 More ablation studies
F.5.1 The effectiveness of guidance in GIF-DALLE
GIF optimizes data latent features for informative sample creation by maximizing the designed objective functions of guidance (i.e., class-maintained informativeness  $S_{inf}$  and sample diversity  $S_{div}$ ), which are essential for effective dataset expansion. With these essential guidance criteria, as shown in Table 19, our guided expansion framework obtains consistent performance gains compared to unguided expansion with SD, DALL-E2, or MAE, respectively. This verifies the effectiveness of our criteria in optimizing the informativeness and diversity of the created samples.
Table 19: Accuracy of ResNet-50 trained from scratch on small datasets and their expanded datasets by various methods. Here, CIFAR100-Subset is expanded by  $5 \times$ , Pets is expanded by  $30 \times$ , and all other natural image datasets are expanded by  $20 \times$ . All medical image datasets are expanded by  $5 \times$ . Moreover, MAE, DALL-E2 and SD (Stable Diffusion) are the baselines of directly using them to expand datasets without our GIF. All results are averaged over three runs.
Dataset	Natural image datasets	Medical image datasets
Caltech101	Cars	Flowers	DTD	CIFAR100-S	Pets	Average	PathMNIST	BreastMNIST	OrganSMNIST	Average
Original	26.3	19.8	74.1	23.1	35.0	6.8	30.9	72.4	55.8	76.3	68.2
Expanded by MAE	50.6	25.9	76.3	27.6	44.3	39.9	44.1 (+13.2)	81.7	63.4	78.6	74.6 (+6.4)
Expanded by GIF-MAE (ours)	58.4	44.5	84.4	34.2	52.7	52.4	54.4 (+23.5)	82.0	73.3	80.6	78.6 (+10.4)
Expanded by DALL-E2	61.3	48.3	84.1	34.5	52.1	61.7	57.0 (+26.1)	82.8	70.8	79.3	77.6 (+9.4)
Expanded by GIF-DALLE (ours)	63.0	53.1	88.2	39.5	54.5	66.4	60.8 (+29.9)	84.4	76.6	80.5	80.5 (+12.3)
Expanded by SD	51.1	51.7	78.8	33.2	52.9	57.9	54.3 (+23.4)	85.1	73.8	78.9	79.3 (+11.1)
Expanded by GIF-SD (ours)	65.1	75.7	88.3	43.4	61.1	73.4	67.8 (+36.9)	86.9	77.4	80.7	81.7 (+13.5)
In this appendix, we further explore the individual influence of these criteria on GIF-DALLE. Specifically, as mentioned in Appendix D.1, GIF-DALLE conducts guided imagination on the CLIP embedding space, which directly determines the content of the created samples. With the aforementioned essential criteria, as shown in Figure 2b, our GIF-DALLE is able to create motorbike images with more diverse angles of view and even a new driver compared to unguided DALLE expansion. Here, we further dig into how different criteria influence the expansion effectiveness of GIF-DALLE. As shown in Table 20, boosting the class-maintained informativeness  $S_{inf}$  is the foundation of effective expansion, since it makes sure that the created samples have correct labels and bring new information. Without it, only  $S_{div}$  cannot guarantee the created samples to be meaningful, although the sample diversity is improved, even leading to worse performance. In contrast, with  $S_{inf}$ , diversity promotion  $S_{div}$  can further bring more diverse information to boost data informativeness and thus achieve better performance (cf. Table 20). Note that contrastive entropy increment  $s\log(s) - s'\log(s')$  in class-maintained informativeness plays different roles from diversity promotion  $S_{div}$ . Contrastive entropy increment promotes the informativeness of each generated image by increasing the prediction difficulty over the corresponding seed image, but this guidance cannot diversify different latent features obtained from the same image. By contrast, the guidance of diversity promotion encourages the diversity of various latent features of the same seed image, but it cannot increase the informativeness of generated samples regarding prediction difficulty. Therefore, using the two guidance together leads the generated images to be more informative and diversified, thus bringing higher performance improvement (cf. Table 20). As a result, as shown in Table 19, with these two essential criteria as guidance, the model accuracy by GIF-DALLE is  $3.3\%$  accuracy higher than unguided data generation with DALL-E2.
Table 20: Ablation of guidance in GIF-DALLE for expanding CIFAR100-Subset by  $5 \times$ .
Method	Sinf	Sdiv	CIFAR100-Subset
GIF-DALLE	✓	52.1±0.9
53.1±0.3
✓	51.8±1.3
✓	✓	54.5±1.1
F.5.2 The effectiveness of guidance in GIF-SD