SlowGuess/NeurIPS_2023_OCR huggingface dataset:
- Has the dataset been used for any tasks already? No.
- Is there a repository that links to any or all papers or systems that use the dataset? No.
What (other) tasks could the dataset be used for? N/A.
- Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? N/A.
- Are there tasks for which the dataset should not be used? N/A.
- Will the dataset be distributed to third parties outside of the entity (e.g., company, institution organization) on behalf of which the dataset was created? No.
- How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? The dataset will be released upon acceptance.
- When will the dataset be distributed? After the whole process of reviewing
- Will the dataset be distributed under a copyright or other intellectual property (IP) license and/or under applicable terms of use (ToU)? The dataset will be released under MIT License.
- Have any third parties imposed IP-based or other restrictions on the data associated with the instances?
No.
- Do any export controls or other regulatory restrictions apply to the dataset or to individual instances?
No.
Contact the first author (jiyounglee0523@kaist.ac.kr) or other authors.
- Is there an erratum?
- Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)?
If any correction is needed, we plan to upload a new version.
- If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were the individuals in question told that their data would be retained for a fixed period of time and then deleted)?
- Will older versions of the dataset continue to be supported/hosted/maintained?
We plan to maintain the newest version only.
- If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?
Contact the authors of the paper.
For the experiments in Section 5, we use a batch size of 16 with a learning rate starting at  $1 \times 10^{-5}$ . The learning rate is decreased by a factor of 0.5 if there is no improvement for 10 epochs or until it reaches  $1 \times 10^{-6}$ . We approximately match the size of each model to 300M parameters. For ViT, we use the variant with 30 layers and 16 heads in each layer. For Swin Transformer, we use a hidden layer of size 256 with layer numbers  $\{2, 2, 15, 2\}$ . For DenseNet, we use a growth rate of 64 with the block configuration  $\{24, 48, 84, 64\}$ . For ConvNeXt, we use the large variant with block numbers  $\{3, 3, 50, 3\}$ . For MLP-Mixer, we use a hidden size of 2048 with 60 layers. We trained all models using either a single NVIDIA RTX A6000 or NVIDIA GeForce RTX 3090 graphics card.
C Class Selection
Table 4 shows the scientific names and sub-species for each class. The classes are selected based on the following four criteria.
- They should be grouped into one scientific name for clear definitions
- They should be visually distinguishable from other species to avoid multiple correct answers
- They should have typical visual features allowing them to be identified by a single image
- They should be familiar to humans so that any MTurk worker can participate in our survey
The final 10 classes are Tiger, Rhinoceros, Camel, Giraffe, Elephant, Zebra, Gorilla, Bear, and Human. These labels are revised and verified by two zoologists.
This section will describe the details of our dataset construction.
D.1 Cronbach's Alpha
Our dataset should contain sufficient test samples to serve as a universal benchmark. For instance, if the test set does not have enough test samples, it will fail to test the model's capacity appropriately. Cronbach's alpha [9] is an indicator that represents the validity of the number of questions in a test. To calculate this value, we first need responses from humans. Therefore, we can only calculate Cronbach's alpha for the Uncertain group, as it is the only group with human responses. However,
Table 4: The scientific names and subspecies of the each class.
Class	Scientific Name	Subspecies
Tiger	Panthera tigris	Amur tiger, Chinese tiger, North Indochinese tiger,
Malayan tiger, Sumatran tiger, Bengal tiger
Rhinoceros	Rhinoceros	White rhino, Black rhino, Indian rhino, Javan rhino, Sumatran rhino
Camel	Camelus	Bactrian camel, Arabian camel, Wild bactrian camel
Giraffe	Giraffa
Giraffa camelopardalis	Angolan giraffe, Kordofan giraffe, Transvaal giraffe,
Reticulated giraffe, Baringo giraffe, Masai giraffe
Elephant	Elephas maximus,
Loxodonta africana	Asiatic elephant, Malayan elephant, Indian elephant,
Sri Lankan elephant, Sumatran elephant, African elephant,
South African bush elephant, East African bush elephant
Zebra	Equus grevyi,
Equus quagga,
Equus zebra	Grevy's zebra, Plains zebra, Grant's zebra,
Half-maned zebra, Damara zebra, Chapman's zebra,
Hartmann's mountain zebra
Gorilla	Gorilla	Western lowland gorilla, Cross River gorilla,
Mountain gorilla, Eastern lowland gorilla
Bear	Ursus	Giant panda, Spectacled bear, Sun Bear, Sloth Bear,
American Black Bear, Brown Bear, Polar Bear, Asiatic black bear
Kangaroo / Wallaby	Macropus,
Notamacropus,
Onychogalea,
Osphranter	Western grey kangaroo, Eastern grey kangaroo, Agile wallaby,
Black-striped wallaby, Tammar wallaby, Western brush wallaby,
Parma wallaby, Pretty-faced wallaby, Red-necked wallaby, Genus Onychogalea,
Bridled nail-tail wallaby, Northern nail-tail wallaby, Genus Osphranter,
Antilopine kangaroo, Black wallaroo, Common wallaroo, Red kangaroo
Human	Homo sapiens sapiens	Homo sapiens sapiens
we believe that the Cronbach's alpha value for the Uncertain group can also be applied to other categories, given that samples in other categories are more straightforward than those in Uncertain (e.g., they have clear images and optimal actions are explicit). To calculate this value, we first treat the original label as a gold standard answer if more than  $50\%$  of MTurk workers correctly classify the image. Otherwise, we set Abstention as the gold standard answer. We then evaluate whether each response for each image is correct based on the gold standard answer and set it to a binary value (1 for a correct response and 0 for an incorrect response). We denote the binary response for the  $i$ -th image as  $x_{i}$ .
Next, we calculate the variance of responses for each image, denoted as  $Var(x_{i})$  for the  $i$ -th image, and the variance of the sum of responses from all images, denoted as  $Var(X)$ . Here,  $X$  is the sum of responses for all images, i.e.,  $X = \sum_{i=1}^{N} x_{i}$ , and  $N$  is the total number of images. We then employ Cronbach's Alpha formula as shown in Equation 2 below.
In our case,  $\sum_{i=1}^{N} Var(x_i) = 127.134$ ,  $Var(X) = 976.564$ , and  $N = 100$  which yields a Cronbach's Alpha of 0.88. A Cronbach's Alpha value between 0.75 and 0.9 is considered ideal. A value higher than 0.9 might indicate redundancy in the questions, as it suggests that there are more questions than necessary.
$$
\alpha = \frac {N}{N - 1} \left(1 - \frac {\sum_ {i = 1} ^ {N} \operatorname {V a r} \left(x _ {i}\right)}{\operatorname {V a r} (X)}\right), \quad \text {w h e r e} X = \sum_ {i = 1} ^ {N} x _ {i} \tag {2}
$$
D.2 Stable Diffusion Prompt
Since there is a limited amount of data for Category 2 and Category 5, we manually generated samples with using Stable Diffusion [59]. We filtered all images to ensure that there is only one object in an image and images look as realistic as Category 1.
D.2.1 Category 2 Prompts
The prompt used is "RAW photo of a {subspecies} {background_prompt}, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3," where {subspecies} is one of the subspecies listed in Table 4 and {background_prompt} is one of the following:
on the moon	surrounded by fire	underwater
in New York city	in a construction site	inside an office
near a swimming pool	on the playground	on a volcano
on the clouds	on an iceberg	in a rainforest
on a snowy mountain	on top of a roof	on a pile of garbage
at night taken using an infrared camera	on top of a tree	inside a tunnel
inside a large bathroom	on top of a bus	with a static background
with a rainbow background	in a dystopian world	in the middle ages
with a purple background	with a pink background	with a red background
with a bright orange background
The negative prompt used is "unrealistic, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation."
D.2.2 Category 5
To create a Category 5 image, we first create an image using the following prompts: {"subspecies"} that looks like {"other_animal"}, "a picture of {"subspecies"} with head of an {"other_animal)}. For {"other_animal", we choose species that in not in-class (e.g., eagle, bird, fish, alligator). Some samples were generated using a variant of Stable Diffusion called MagicMix [36], which performs semantic mixing by blending the semantics of an image and a text prompt to create a new image. To use MagicMix, we first create an image using a prompt similar to the one used for Category 2, except we also choose species that are not in-class. Then, we insert any other species as the target prompt into MagicMix to blend the semantics of another species into the image.
E AI-Human Visual Alignment for Uncertain Images
In this section, we explain why corrupted images should be evaluated based on human perception ratios obtained from MTurk workers. Some researchers might argue that since the corrupted images come from clean images, the models should be able to correctly classify the original label despite the existence of corruption severity regardless of human perception ability. However, when the images are gradually corrupted, the essential features of objects will eventually be lost and become images with complete noises (e.g., black images or images with pure Gaussian noise). In such cases, it is meaningless for AI models to make predictions because they would predict based on noise rather than using related features to classes. Therefore, we need new labels for corrupted images, indicating whether images are unrecognizable or contain essential features. However, setting a unified guideline is impossible since visibility varies by objects, corruption types, and images themselves. Therefore, we must newly obtain labels by asking qualified humans. Here, the qualified humans we refer to are people with commonsense knowledge (i.e., must know the 10 mammals) and have functioning visual perception (i.e., we test this via intra-annotator agreement and we also rejected responses from workers who chose other than 'Abstention' for distractor images that are corrupted images from Category 4). To obtain a gold human ratio, we asked 134 people from diverse age groups and backgrounds to achieve the error bound of  $5\%$ .
Nevertheless, some might still argue that AI should aim to identify the original class because we can set up a controlled experiment where we can test if its guess was correct. For example, we can put an elephant in a dark room, let the machine take a guess, then increase the brightness of the room. In such experiments, we may be able to identify if some AI possesses superhuman visual perception (e.g., only 1 out of 100 human participants were able to confidently tell the object in the dark room was an elephant, but the AI had a  $95\%$  confidence in the elephant class). However, making a decision based on a single image and setting a controlled experiment are completely different settings since it is infeasible to set a controlled experiment with static images. It is not correct to claim that AI must always try to identify the original class in the former (i.e., deciding based on a single image) because the latter (i.e., running a controlled trial) is also possible. The main objective of VisAlign is to test the model's safety (or potential harmfulness), as well-aligned models are less likely to cause harm. Potentially, our dataset can be used as a prerequisite such that, if models pass our dataset by some threshold, then the models are less likely to make harmful decisions. Then, the model's superhuman capability can be tested using a separate dataset under controlled experiments. This is somewhat
A clear image of a tiger. In this case, please select Tiger.
Only partial information. This image could be a tiger or a carpet. In this case, please select None of the above, uncertain, or recognizable. Only select a different label when you are very certain.
Only partial information. We cannot specify an animal with this image. In this case, please eliminate None of the above, uncertain, or unrecognizable. Only a single word is present in the image.
Figure 2: Labeling examples provided to MTurk Workers as part of the instructions.
This is a bag with tiger pattern. Since this is not a tiger, in this case, None of the above, uncertain, or unrecognizable. Only select a different label when you are very certain.
This is an animal that looks similar to tiger but is actually of a different species. In this case, please select None of the above, uncertain, or unrecognizable options. Please also specify whether you are in the presence of any other animals.
This is an animal that looks similar to tiger but is actually of a different species. In fact, the term "tiger" does not mean "tiger" or "tiger-like" or "unrecognizable." Only select a different label when you are very certain.
Dog does not belong to any of the 10 labels. In this case, please select None of the above, uncertain, or unrecognizable.
similar to the multi-phase drug development process, where the initial phases always test the basic safety of the drug (toxicity, side effects) before advancing to latter phase to test the clinical efficacy of the drug.
F Amazon Mechanical Turk Survey
This section will describe in detail of Amazon Mechanical Turk to obtain gold human ratio of Uncertain samples. We paid $0.05 for classifying 35 images per worker.
F.1 Instructions
This section contains the instructions given to the survey participants. We also present detailed labeling instruction examples in Figure 2 for ease of labeling for MTurk workers to understand the variety of cases in which to abstain. Specifically, we provide a clear images of Tiger and instruct the workers to choose label "Tiger". For other cases (e.g., only a part of a tiger, a bag with a tiger pattern, species similar to tiger, other animals), we instruct the workers to choose "None of the 10 mammals, uncertain, or unrecognizable". The following box contains the exact instructions given to the MTurk Workers:
There are 11 labels to choose from:
- Tiger
Zebra
Camel
Giraffe
- Elephant
Rhino
- Gorilla
Bear or Giant Panda
Kangaroo
Human
- None of the above, uncertain, or unrecognizable
Please choose one of the first 10 labels only if you are certain the image belongs to that label.
Please choose the 11th label (None of the above, uncertain, or unrecognizable) for any of the situations below.
- None of the 10 labels describe the object observed in the image
- The object observed in the image is unrecognizable
- You are not sure which label describes the object observed in the image
- Any other similar situation
F.2 Filtering Process
To ensure high data quality, we filtered noisy collected responses of the survey. We added distractors (Category 4 samples corrupted with a severity between 1 and 10) in our survey. Among them, the authors identified clear distractors that should always be chosen as "None of the above, uncertain, or unrecognizable" (e.g., a clear image of a cup or a truck). We reject all the responses from the survey participants who chose other than "None of the above, uncertain, or unrecognizable" for clear distractors.
F.3 Participant Statistics
This section provides the characteristics of the MTurk workers participated in our survey.
MTurk workers are equal in gender (44.9% of male, 53.8% of female, and 1.3% of others).
People from diverse age groups (from 10s to 70s) participated (2.1% of 10s, 19.7% of 20s, 35.1% of 30s, 24.8% of 40s, 14.3% of 50s, 3.5% of 60s, and 0.4% of 70s).
The participant locations were focused on largely five countries, namely USA (71.1%), India (13.1%), Italy (5%), UK (3.1%), and Canada (2.3%). Other responses are from other countries including Philippines, Brazil, Nigeria, Mexcio, Pakistan, UAE, and Malaysia.
F.4 Sampling Theory
Given an image  $x$  and its corresponding label  $y$ , we can assume  $y \sim \mathrm{Bernoulli}(p)$ , where  $p$  is the probability of the true class.
Let  $N$  denote the number of individuals in the population and  $n$  denote the number of samples, then the approximated variance of  $\hat{p}$ , assuming sampling without replacement and a  $95\%$  confidence level, can be expressed as in Eq. 3. In this equation,  $z_{0.975}$  represents the z-score under the normal distribution corresponding to a probability of 0.975, and  $q = 1 - p$ .
$$
\begin{array}{l} z _ {0. 9 7 5} \sqrt {\widehat {V} (\hat {p})} = z _ {0. 9 7 5} \sqrt {\left(1 - \frac {n}{N}\right) \times \left(\frac {\hat {p} \hat {q}}{n - 1}\right)} \tag {3} \\ \approx z _ {0. 9 7 5} \sqrt {\left(\frac {\hat {p} \hat {q}}{n - 1}\right)} \quad (\because N = \infty) \\ \end{array}
$$
Given an error bound  $\xi$ , we can derive the required minimum number of samples to achieve the error bound by setting the  $95\%$  confidence interval of the approximated variance to be lower than  $\xi$ . For ease of calculation, we round  $z_{0.975} = 1.96$  to 2.
$$
\begin{array}{l} 2 \sqrt {\left(\frac {\hat {p} \hat {q}}{n - 1}\right)} \leq \xi \tag {4} \\ n \geq \frac {4 \hat {p} \hat {q}}{\xi^ {2}} + 1 \\ \end{array}
$$
Since we do not have prior knowledge of  $\hat{p}$ , we set  $\hat{p}$  to  $\frac{1}{11}$ , which represents a uniform distribution over the 11 classes (10 mammals + abstention). We drop the constant for simplicity.
$$
n \geq \frac {4 \times \frac {1}{1 1} \times \frac {1 0}{1 1}}{\xi^ {2}} = \frac {4 0}{1 1 ^ {2} \times \xi^ {2}} \tag {5}
$$
For  $\xi = 0.05, 0.1, 0.15$ , the minimum required number of participants are as follows:
Therefore, to achieve an error bound lower than  $5\%$ , we surveyed 134 people per image.
ξ	40/112×ξ2
0.05 (5%)	132.23
0.1 (10%)	33.05
0.15 (15%)	14.69
Table 5: Percentage of each action type (the different action types are organized in Table 2). The Label Pred. column shows the original label prediction for Uncertain samples treated as Must-Abstain. Otherwise if the model does not abstain (nor predict the original label) for Must-Abstain, then the action is considered Other Prediction.
Must-Act	Must-Abstain
Correct	Incorrect	Abstain	Label Pred.	Other Pred.	Abstain
ViT [11]
SP	0.62	0.07	0.31	0.02	0.84	0.13
ASP	0.63	0.00	0.37	0.03	0.97	0.00
MD [35]	0.63	0.01	0.35	0.02	0.94	0.03
KNN [67]	0.62	0.04	0.34	0.02	0.91	0.07
TAPUDD [13]	0.63	0.00	0.37	0.80	0.97	0.00
OpenMax [3]	0.61	0.11	0.28	0.02	0.79	0.18
MC-Dropout [16]	0.63	0.00	0.37	0.03	0.97	0.00
Deep Ensemble [32]	0.62	0.14	0.24	0.03	0.72	0.25
Swin Transformer [38]
SP	0.71	0.07	0.22	0.02	0.83	0.15
ASP	0.73	0.00	0.27	0.02	0.98	0.00
MD [35]	0.73	0.03	0.24	0.02	0.91	0.07
KNN [67]	0.63	0.28	0.10	0.01	0.44	0.55
TAPUDD [13]	0.73	0.00	0.27	0.02	0.98	0.00
OpenMax [3]	0.70	0.07	0.23	0.02	0.75	0.24
MC-Dropout [16]	0.73	0.00	0.27	0.02	0.98	0.00
Deep Ensemble [32]	0.74	0.11	0.15	0.01	0.72	0.27
DenseNet [27]
SP	0.76	0.07	0.17	0.02	0.80	0.18
ASP	0.78	0.00	0.22	0.02	0.98	0.00
MD [35]	0.78	0.00	0.22	0.02	0.93	0.06
KNN [67]	0.73	0.15	0.11	0.01	0.61	0.38
TAPUDD [13]	0.74	0.04	0.22	0.02	0.94	0.05
OpenMax [3]	0.71	0.16	0.12	0.01	0.64	0.35
MC-Dropout [16]	0.78	0.00	0.22	0.02	0.98	0.00
Deep Ensemble [32]	0.79	0.07	0.14	0.02	0.82	0.16
ConvNeXt [39]
SP	0.66	0.14	0.20	0.02	0.65	0.33
ASP	0.71	0.00	0.29	0.04	0.96	0.00
MD [35]	0.63	0.15	0.22	0.03	0.78	0.19
KNN [67]	0.68	0.14	0.18	0.03	0.61	0.36
TAPUDD [13]	0.67	0.04	0.29	0.04	0.94	0.02
OpenMax [3]	0.69	0.04	0.28	0.04	0.94	0.02
MC-Dropout [16]	0.71	0.00	0.29	0.04	0.96	0.00
Deep Ensemble [32]	0.66	0.17	0.18	0.02	0.60	0.39
MLP-Mixer [68]
SP	0.62	0.09	0.29	0.01	0.80	0.19
ASP	0.65	0.00	0.35	0.01	0.99	0.00
MD [35]	0.61	0.14	0.26	0.01	0.73	0.26
KNN [67]	0.59	0.16	0.25	0.00	0.67	0.33
TAPUDD [13]	0.48	0.21	0.31	0.01	0.78	0.21
OpenMax [3]	0.60	0.12	0.27	0.01	0.76	0.23
MC-Dropout [16]	0.65	0.00	0.35	0.01	0.99	0.00
Deep Ensemble [32]	0.62	0.14	0.24	0.01	0.73	0.26
G.1 Experimental Results Shown as Percentages
Section 4.2 describes the possible action types for each group and how they are used to obtain the reliability score  $RS_{c}$ . While the reliability score allows us to assess the reliability of a given model with a single value, we also provide the ratios of each action type by their respective groups in Table 5.
Figure 3: Correlation between Visual Alignment Distance and Reliability Score ( $RS_{900}$ ). There exists a strong correlation between visual alignment distance and reliability score. This proves that visual alignment can be used as a proxy method for reliability.
G.2 Correlation between Visual Alignment and Reliability Score
Figure 3 shows the correlation between visual alignment distance and reliability score measured in Table 3. There exists a strong correlation between visual alignment distance and reliability score – the shorter the distance the higher the reliability score. This indicates that visual alignment score can be used as a proxy method for reliability, underscoring the importance of visual alignment.
H Experiment Results from Pre-training and Self-supervised Learning
Previous studies [1, 75, 23, 44] suggest that training on larger data and pre-training by self-supervised learning (SSL) methods help improve robustness and Out-of-Distribution (OOD) detection. To validate if the same findings can also be applied in our task, we additionally measure the visual alignment and reliability score on models that are pre-trained on ImageNet [60] and pre-trained by two popular SSL methods, which are SimCLR [6] and BYOL [21]. For models that are pre-trained on ImageNet, after pre-training, we initialize the top classification layer and train on our train set while freezing the pre-trained parameters during fine-tuning. For models that are pre-trained by SSL methods, we do not freeze any layers after pre-training.
The results are shown in Table 6 and Table 7. The results in Table 6 can be compared to the results in Table 3. For ImageNet pre-trained models, Transformer-based models show improved performance, whereas MLP-based and CNN-based models show similar or decreased visual alignment scores, especially when evaluated with SP. This indicates that the effect of pre-training on larger datasets is dependent on model architecture. Interestingly, distance-based abstention functions display higher visual alignment scores. We suspect that the improved output embeddings from pre-training enable distance-based abstention functions to capture more precise features. Deep Ensemble has better visual alignment when met with Transformer-based and MLP-based. Notably, Transformer-based models combined with KNN have the best visual alignment score. We conjecture the reason comes from both the model architecture and the abstention function. Contrary to CNN-based models, Transformer-based models are able to capture global features of images instead of only local features. Also, KNN calculates abstention probability based on the distance between samples instead of clusters, as done in MD or TAPUDD, which uses more fine-grained features for deciding abstention. Therefore, deciding abstention using fine-grained details on global features gets boosted when trained on a larger set,
which leads to the best visual alignment. The overall reliability score increases when pre-trained with ImageNet, and this represents that the models that are pre-trained on ImageNet are more likely to abstain.