Original: An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters
Revised: An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters

Original: Anonymous Author(s)

Original: AfﬁliationAddress email

Original: Abstract
Revised: Abstract

Original: We present ﬁrst empirical results from our ongoing investigation of distributionshifts in image data used for various computer vision tasks.
Revised: We present ﬁrst empirical results from our ongoing investigation of distribution shifts in image data used for various computer vision tasks.

Original: Instead of analyzingthe original training and test data, we propose to study shifts in the learned weightsof trained models.
Revised: Instead of analyzing the original training and test data, we propose to study shifts in the learned weights of trained models.

Original: In this work, we focus on the properties of the distributionsof dominantly used 3 × 3 convolution ﬁlter kernels.
Revised: In this work, we focus on the properties of the distributions of dominantly used 3 × 3 convolution ﬁlter kernels.

Original: We collected and publiclyprovide a data set with over half a billion ﬁlters from hundreds of trained CNNs,using a wide range of data sets, architectures, and vision tasks.
Revised: We collected and publicly provide a data set with over half a billion ﬁlters from hundreds of trained CNNs, using a wide range of data sets, architectures, and vision tasks.

Original: Our analysisshows interesting distribution shifts (or the lack thereof) between trained ﬁltersalong different axes of meta-parameters, like data type, task, architecture, or layerdepth.
Revised: Our analysis shows interesting distribution shifts (or the lack thereof) between trained ﬁlters along different axes of meta-parameters, like data type, task, architecture, or layer depth.

Original: We argue, that the observed properties are a valuable source for furtherinvestigation into a better understanding of the impact of shifts in the input datato the generalization abilities of CNN models and novel methods for more robusttransfer-learning in this domain. 
Revised: We argue, that the observed properties are a valuable source for further investigation into a better understanding of the impact of shifts in the input data to the generalization abilities of CNN models and novel methods for more robust transfer-learning in this domain. Data available at: https://github.com/paulgavrikov/CNN-Filter-DB/ .

Original:  Introduction
Revised:  Introduction

Original: Despite their overwhelming success in the application to various vision tasks, the practical deploymentof convolutional neural networks (CNNs) is still suffering from several inherent drawbacks.
Revised: Despite their overwhelming success in the application to various vision tasks the practical deployment of convolutional neural networks (CNNs) is still suffering from several inherent drawbacks.

Original: Twoprominent examples are I) the dependence on very large amounts of annotated training data [1],which is not available for all target domains and is expensive to generate; and II) still widely unsolvedproblems with the robustness and generalization abilities of CNNs [2] towards shifts of the inputdata distributions.
Revised: Two prominent examples are I) the dependence on very large amounts of annotated training data [1] which is not available for all target domains and is expensive to generate; and II) still widely unsolved problems with the robustness and generalization abilities of CNNs [2] towards shifts of the input data distributions.

Original: One can argue that both problems are strongly related, since a common practicalsolution to I) is the ﬁne-tuning [3] of pre-trained models by small data sets from the actual targetdomain.
Revised: One can argue that both problems are strongly related, since a common practical solution to I) is the ﬁne-tuning [3] of pre-trained models by small data sets from the actual target domain.

Original: This results in the challenge to ﬁnd suitable pre-trained models based on data distributionsthat are "as close as possible" to the target distributions.
Revised: This results in the challenge to ﬁnd suitable pre-trained models based on data distributions that are "as close as possible" to the target distributions.

Original: Hence, both cases (I+II) imply the need tomodel and observe distribution shifts in the contexts of CNNs.
Revised: Hence, both cases (I+II) imply the need to model and observe distribution shifts in the contexts of CNNs.

Original: In this paper, we propose not to investigate these shifts in the input (image) domain, but rather inthe weight distributions of the CNNs themselves.
Revised: In this paper, we propose not to investigate these shifts in the input (image) domain, but rather in the weight distributions of the CNNs themselves.

Original: We argue that e.g. the distributions of trainedconvolutional ﬁlters in a CNN, which implicitly reﬂect the sub-distributions of the input image datawhich are actually utilized by a speciﬁc model, are more suitable and easier accessible representationsfor this task.
Revised: We argue that e.g. the distributions of trained convolutional ﬁlters in a CNN, which implicitly reﬂect the sub-distributions of the input image data which are actually utilized by a speciﬁc model, are more suitable and easier accessible representations for this task.

Original:  Methods
Revised:  Methods

Original: Data.
Revised: Data.

Original: We collected a total of 391 publicly available CNN models pre-trained for various visualtasks, recorded meta-data for each model, and manually categorized the training data into visuallydistinctive groups  like natural scenes, medical ct, seismic, or astronomy for example.
Revised: We collected a total of 391 publicly available CNN models pre-trained for various visual tasks, recorded meta-data for each model, and manually categorized the training data into visually distinctive groups (data type) like natural scenes, medical ct, seismic, or astronomy for example.

Original: All modelswere trained with full 32-bit precision but may have been trained with variously scaled inputs.
Revised: All models were trained with full 32-bit precision but may have been trained with variously scaled inputs.

Original: Thedominant subset is formed by image classiﬁcation models trained on ImageNet1k [4] (264 models).
Revised: The dominant subset is formed by image classiﬁcation models trained on ImageNet1k [4] ( models).

Original: We extracted all trained convolution ﬁlters to get a heterogeneous and diverse representation.
Revised: We extracted all trained convolution ﬁlters to get a heterogeneous and diverse representation.

Original: Hereby,only the widely used ﬁlters with a kernel size of 3 × 3 were taken into account.
Revised: Hereby, only the widely used ﬁlters with a kernel size of 3 × 3 were taken into account.

Original: Filters were only extracted from regular convolution layers; Transposed convolution layers were not included.
Revised: Filters were only extracted from regular convolution layers; Transposed convolution layers were not included.

Original: A totalof 524,563,289 ﬁlters from 13,015 layers is used for the following study 1 .
Revised: A total of 524,563,289 ﬁlters from 13,015 layers is used for the following study.

Original: Structure analysis.
Revised: Structure analysis.

Original: We apply a full-rank PCA transformation to understand the underlying structureof the ﬁlters.
Revised: We apply a full-rank PCA transformation to understand the underlying structure of the ﬁlters.

Original: A linear combination of principal components/eigenimages/basis vectors v i weightedby the coefﬁcients
Revised: A linear combination of principal components v i weighted by the coefﬁcients

Original: c i : f = (cid:80) i c i v i then describes each ﬁlter f .
Revised: c i and a bias b i then describes each ﬁlter: f = (cid:80) i c i v i + b i .

Original: Figure 1 shows the principalcomponents computed on the complete data set and various sub-sets.
Revised: Figure 1 shows the principal components computed on the complete data set and various sub-sets.

Original: Measuring distribution drifts.
Revised: Measuring distribution shifts.

Original: The divergence between two distributions is measured by thesymmetric, non-negative variant of Kullback-Leibler [5].
Revised: The divergence between two distributions is measured by the symmetric, non-negative variant of Kullback-Leibler [5].

Original: The drift D of two ﬁlter sets is then deﬁnedby the sum of the divergence of the coefﬁcient distributions
Revised: The shift D of two ﬁlter sets is then deﬁned by the sum of the divergence of the coefﬁcient distributions

Original: P i , Q i along every principal componentindex i .
Revised: P i , Q i along every principal component index i .

Original: The sum is weighted by the ratio of variance q i explained by the i -th principal component.
Revised: The sum is weighted by the ratio of variance q i explained by the i -th principal component.

Original: [Equation]
Revised: [Equation]

Original: To avoid undeﬁned expressions, all probability distributions F are set to hold ∀ x ∈ X : F ( x ) ≥ (cid:15) .
Revised: To avoid undeﬁned expressions, all probability distributions F are set to hold ∀ x ∈ X : F ( x ) ≥ (cid:15) .

Original:  Empirical evaluation3.1 Comparison of ﬁlter structures
Revised:  Empirical evaluation 3.1 Comparison of ﬁlter structures

Original: In a ﬁrst series of experiments, we analyze only the structure of 3 × 3 ﬁlters, neglecting their actualnumerical weight in the trained models.
Revised: In a ﬁrst series of experiments, we analyze only the structure of 3 × 3 ﬁlters, neglecting their actual numerical weight in the trained models.

Original: We scale all ﬁlters by their absolute maximum weight andperform a PCA.
Revised: We scale all ﬁlters by their absolute maximum weight and perform a PCA.

Original: Figure 1 shows some qualitative examples of principal components, split by severalmeta-data dimensions.
Revised: Figure 1 shows some qualitative examples of principal components, split by several meta-data dimensions.

Original: Figure 2 depicts the shifts between distributions of PCA coefﬁcients alongmodels trained on different data types.
Revised: Figure 2 depicts the shifts between distributions of PCA coefﬁcients along models trained on different data types.

Original: We hypothesize that the spiky kernel density estimates (KDEs)are caused by "degenerated" layers e.g. if a model was too complex/deep for a given dataset (e [Figure] g.
Revised: We hypothesize that the spiky kernel density estimates (KDEs) are caused by "degenerated" layers e.g. if a model was too complex/deep for a given dataset (e.g.

Original: ResNet-101 [6] on CIFAR-10 [7]).
Revised: ResNet-101 [6] on CIFAR-10 [7]).

Original: [Figure]
Revised: [Figure]

Original: More examples in subsection A.3.

Original: [Figure]
Revised: [Figure]

Original:  plants 22 c 0 textures 22 c22 c22 c22 c22 c22 c22 c22 c
Revised: natural plants 22 c 0 textures 22 c22 c22 c22 c22 c22 c22 c22 c

Original: 3.1.1 Observable shifts along ﬁlters sub-sets
Revised: 3.1.1 Observable shifts along ﬁlters sub-sets

Original: Between Models.
Revised: Between Models.

Original: It is not surprising, that the comparison of ﬁlters between individual neuralarchitectures results in the highest shift we observe for all investigated meta-groups (Figure 4).
Revised: The comparison of ﬁlters between individual neural architectures results in the highest shift we observe for all investigated meta-groups (Figure 4).

Original: Inside Model families.
Revised: Inside Model families.

Original: The shift between models of the same family trained for the same task (e.g.
Revised: The shift between models of the same family trained for the same task (e.g.

Original: ResNet -classiﬁers in Figure 4) is negligible, indicating that the common practice of pre-trainingmodels with ImageNet  even for visually distant application domains is indeed a valid approach.
Revised: ResNet -classiﬁers in Figure 4) is negligible and mostly independent of the training data, showing that the common practice of pre-training models with ImageNet is indeed a valid approach even for visually distant application domains.

Original: Between Tasks.
Revised: Between Tasks.

Original: Unsurprisingly, classiﬁcation, segmentation, object detection, and GAN-generatordistributions are quite similar (Figure 3a), since the non-classiﬁcation models typically include aclassiﬁcation backbone.
Revised: Unsurprisingly, classiﬁcation, segmentation, object detection, and GAN-generator distributions are quite similar (Figure 3a), since the non-classiﬁcation models typically include a classiﬁcation backbone.

Original: The smallest mean shift to other tasks is observed in object detection, GAN-generators, depth estimation models.
Revised: The smallest mean shift to other tasks is observed in object detection, GANgenerators, depth estimation models.

Original: Super Resolution models are strong outliers.
Revised: Super Resolution models appears to be strong outliers, but we only have one model for this task.

Original: We assumethat this is due to the usage of PixelShufﬂe layers [8].
Revised: Additionally, this model contains PixelShufﬂe layers [8] that may tamper with the ﬁlters.

Original: Less transferable task distributions also include GAN-discriminators and face detection.
Revised: Less transferable task distributions also include GAN-discriminators and face detection models.

Original: GAN-discriminators distributions do barely differ alongprincipal components and can be approximated by a gaussian distribution.
Revised: GAN-discriminators distributions do barely differ along principal components and can be approximated by a gaussian distribution.

Original: This indicates a ﬁlterdistribution close to random initialization in many cases, resulting in a "confused" discriminator thatcannot distinguish between real and fake samples towards the end of training.
Revised: This indicates a ﬁlter distribution close to random initialization, representing a "confused" discriminator that cannot distinguish between real and fake samples towards the end of training.

Original: C l a ss i f i ca ti o n D ep t h
Revised: O b j e c t D e t e c t i o n

Original: E s t i m a t i o n F a c e D e  t e c t i o nF a c e R e
Revised: S e g m e n t a t i o n

Original: c o g n i t i o n

Original: G A N - D i s c r i m i n  a t
Revised: G A N - G e n e r a t

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Original: S e g me n t a t i o n S t y le T r a n s f e r
Revised: i m a t i o n C l a ss i f i c a t i o n S t

Revised: y l e T r a n s f e r

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Revised: F a c e D e t e c t i o n F a c e R e c o g n i t i o n G A N - D i s c

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Original:  R e s o l u t
Revised: o r S u p e r R e s o l u t

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Original: [Figure]
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Original: Resolution
Revised: d e p t h m a p f a c e s n a t u r a l m e d i c a l x r a y t h e r m a l a s t r o n o m y a r t s e i s m i

Revised: c c a r s m e d i c a l c t f

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Original: Data types and training sets.
Revised: Data types and training sets.

Original: We ﬁnd that the distribution shift is well balanced across most datatypes and training sets (Figure 3b).
Revised: We ﬁnd that the distribution shift is well balanced across most data types and training sets (Figure 3b).

Original: Most coefﬁcient distributions tend to shrink towards the leastsigniﬁcant principal components, with a remarkably wide distribution of the ﬁrst principal component.
Revised: Most coefﬁcient distributions tend to shrink towards the least signiﬁcant principal components, with a remarkably wide distribution of the ﬁrst principal component.

Original: Notable outliers include medical CT & MRI, formula, texture, and plants data types.
Revised: Notable outliers include medical CT & MRI, formula, texture, and plants data types.

Original: Medical typeshave visible spikes in the KDEs, indicating that many structurally similar ﬁlters exist.
Revised: Medical types have visible spikes in the KDEs, indicating that many structurally similar ﬁlters exist.

Revised: The outlier

Original: Formulaincludes models trained on Fractal-DB , which was proposed as a synthetic pre-training alternative to ImageNet1k [9].
Revised: Formula includes models trained on Fractal-DB , which was proposed as a synthetic pre-training alternative to ImageNet1k [9].

Original: Layer depth.
Revised: Layer depth.

Original: The shift between layers of various depth deciles increases with the difference in depth(Figure 3c), yet it is marginal compared to the shift across tasks or data types.
Revised: The shift between layers of various depth deciles increases with the difference in depth (Figure 3c), yet it is marginal compared to the shift across tasks or data types.

Original: Distributions in the lastdecile of depth form the most distinct interval, signiﬁcantly outdistancing the ﬁrst and second-to-lastdecile that follow next.
Revised: Distributions in the last decile of depth form the most distinct interval, signiﬁcantly outdistancing the ﬁrst and second-to-last decile that follow next.

Original: However, splitting the coefﬁcients by absolute depth introduces many extremeoutliers (Figure 4).
Revised: However, splitting the coefﬁcients by absolute depth introduces many extreme outliers (Figure 4).

Original: That may again be a result of degenerated layers.
Revised: That may again be a result of degenerated layers or an effect of under-sampling.

Original: [Figure]
Revised: [Figure]

Original: 3.1.2 Observable ﬁlter distribution phenotypes
Revised: 3.1.2 Observable ﬁlter distribution phenotypes

Original: We categorize the distributions into three phenotypes depending on their distribution characteristic in the PCA space: I) distributions where all dimensions are gaussian-like; II) distributions containing one or more layer that shows a fairly small variety of feature patterns and therefore takes on discrete stages in bi-variate scatter plots; III) distributions where one or more distribution is multi-modal, not centered, highly sparse or otherwise non-normal forming scatter plots that look like symbols.
Revised: We categorize the distributions into three phenotypes depending on their distribution characteristic in the PCA space: I) distributions where all dimensions are gaussian-like; II) distributions containing one or more layer that shows a fairly small variety of feature patterns and therefore takes on discrete stages in bi-variate scatter plots; III) distributions where one or more distribution is multi-modal, not centered, highly sparse or otherwise non-normal forming scatter plots that look like symbols.

Original: Figure 5 shows typical examples of these phenotypes.
Revised: Figure 5 shows typical examples of these phenotypes.

Original: [Figure]

Original: [Figure]
Revised: [Figure]

Original: 3.2 Filter Scales
Revised: 3.2 Filter Scales

Original: [Figure]

Original: So far we have only studied the structural similarity, independent of the actual scale of the learnedﬁlter weights (difference between minimal and maximal weight).
Revised: So far we have only studied the structural similarity, independent of the actual scale of the learned ﬁlter weights (difference between minimal and maximal weight).

Original: In Figure 6 we compute the meanscale per layer depth decile.
Revised: In Figure 6 we compute the mean scale per layer depth decile.

Original: The distributions show an expected decrease with depth but also a highvariance and many outliers across models, especially in the ﬁrst two deciles.
Revised: The distributions show an expected decrease with depth but also a high variance and many outliers across models, especially in the ﬁrst two deciles.

Original: [Figure]
Revised: [Figure]

Original: L a y e r D e p t
Revised: L a y e r D e p t

Original: h D e c il e
Revised: h D e c il e

Original:  Related Work
Revised:  Related Work

Original: An extensive analysis of features, connections, and their organization extracted from trained InceptionV1 [10] was presented in [11, 12, 13, 14, 15, 16, 17, 18, 19].
Revised: An extensive analysis of features, connections, and their organization extracted from trained InceptionV1 [10] was presented in [11, 12, 13, 14, 15, 16, 17, 18, 19].

Original: The authors of [20] studied learnedﬁlter representations in ImageNet classiﬁcation models and presented the ﬁrst moves towards transferlearning.
Revised: The authors of [20] studied learned ﬁlter representations in ImageNet classiﬁcation models and presented the ﬁrst moves towards transfer learning.

Original: A summary of transfer learning for image classiﬁcation CNN can be found in [21] andgeneral surveys for other tasks and domains are available in [22, 23].
Revised: A summary of transfer learning for image classiﬁcation CNN can be found in [21] and general surveys for other tasks and domains are available in [22, 23].

Original: [24] captured convolutionﬁlter patterns with Gaussian Mixture Models to achieve cross-architecture transfer learning and [25]demonstrated that convolutions ﬁlters can be replaced by a ﬁxed ﬁlter basis that 1 × 1 convolutionlayers blend.
Revised: [24] captured convolution ﬁlter patterns with Gaussian Mixture Models to achieve cross-architecture transfer learning and [25] demonstrated that convolutions ﬁlters can be replaced by a ﬁxed ﬁlter basis that 1 × 1 convolution layers blend.

Original: A benchmark for distribution shifts that arise in real-world applications is provided in [26] and [27]measured robustness to natural distribution shifts.
Revised: A benchmark for distribution shifts that arise in real-world applications is provided in [26] and [27] measured robustness to natural distribution shifts.

Original: Lastly, [28] studied the correlation between transferperformance and distribution shifts of image classiﬁcation models.
Revised: Lastly, [28] studied the correlation between transfer performance and distribution shifts of image classiﬁcation models.

Original:  Discussion and Outlook
Revised:  Discussion and Outlook

Original: Our ﬁrst results support our initial hypothesis that the distributions of trained convolutional ﬁltersare a suitable and easy-to-access proxy for the investigation of image distributions and the similaritybetween the same.
Revised: Our ﬁrst results support our initial hypothesis that the distributions of trained convolutional ﬁlters are a suitable and easy-to-access proxy for the investigation of image distributions and the similarity between the same.

Original: While the presented results are still in the early stages of a thorough study, wereport several interesting ﬁndings that could be explored to obtain better model generalizationsand assist in ﬁnding suitable pre-trained models.
Revised: While the presented results are still in the early stages of a thorough study, we report several interesting ﬁndings that could be explored to obtain better model generalizations and assist in ﬁnding suitable pre-trained models.

Original: One ﬁnding is the presence of large amounts ofdegenerated (or untrained) ﬁlters in large, well-performing networks - resulting in the phenotypesspikes and symbols .
Revised: One ﬁnding is the presence of large amounts of degenerated (or untrained) ﬁlters in large, well-performing networks - resulting in the phenotypes spikes and symbols .

Original: We assume that their existence is a symptom in line with the Lottery Ticket
Revised: We assume that their existence is a symptom in line with the Lottery Ticket

Original: Hypothesis [29].
Revised: Hypothesis [29].

Original: Another striking ﬁnding is the observation of very low shifts between different meta-groups: I) shiftsinside a family of architectures are very low, independent of the target image distribution; II) also weobserve rather small shifts between convolution layers of different depths; while III) shifts betweendifferent tasks aremuch higher, even when related architectures and the same data is used.
Revised: Another striking ﬁnding is the observation of very low shifts between different meta-groups: I) shifts inside a family of architectures are very low, independent of the target image distribution; II) also we observe rather small shifts between convolution layers of different depths; while III) shifts between different tasks are higher, even when related architectures and the same data is used.

Original: A Appendix
Revised: Acknowledgments and Disclosure of Funding

Revised: This work was supported in part by the German Ministry for Science, Research and Arts BadenWuerttemberg (MWK) under Grant 32-7545.20/45/1 Quality Assurance of Machine Learning Applications (Q-AMeLiA). https://q-amelia.in.hs-furtwangen.de/ .

Original:  A.1 Detailed divergence computation
Revised: A Appendix A.1 Detailed divergence computation

Original: [Figure]

Original: All probability distributions are represented by histograms.
Revised: All probability distributions are represented by histograms.

Original: The histogram range is deﬁned by theminimum and maximum value found across all distributions to compare.
Revised: The histogram range is deﬁned by the minimum and maximum value found across all distributions to compare.

Original: The histogram widths areuniform and deﬁned by the maximum bin width needed to represent each distribution. We use themaximum of the Sturges-[30] and Freedman-Diaconis-Estimator [31] as an estimate for the optimalbin width. [Figure]
Revised: 70 uniform bins are used. A.2 D-matrix for non-scaled ﬁlters

Original: C l a ss i f
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Original: c u m u l a t i v e e x p l a i n e d v a r i a n c e r a t
Revised: c u m u l a t i v e e x p l a i n e d v a r i a n c e r a t

Original: i o allnaturalfacesplantsastronomyartmapdepthtexturesformulamedical ct
Revised: i o allnaturalfacesplantsastronomyartmapdepthtexturesformulamedical ct

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Revised: A.4 Data set statistics

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Original: B S

Original: Dw i d e r f a c e - ( c l e a n e d ) w i
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Original: F E R P l u s M
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Original: I M I C - C
Revised: I M I C - C

Original: X
Revised: X

Original: R
Revised: R

Original: U K B i
Revised: U K B i

Original: o
Revised: o

Original: b a n k A d
Revised: b a n k A d

Original: i e n c e l gg - m r i - s e g m e n t a t i o n c u s t o m
Revised: i e n c e l gg - m r i - s e g m e n t a t i o n c u s t o m

Original: F r a c t a l D B10 k
Revised: F r a c t a l D B10 k

Original: F r a c t a l D B1 k
Revised: F r a c t a l D B1 k

Original: C h a L e a r n L
Revised: C h a L e a r n L

Original: A P
Revised: A P

Original: P A S C A L 2007 ; P
Revised: P A S C A L 2007 ; P

Original: A S C A L 2012 C a r v a n a
Revised: A S C A L 2012 C a r v a n a

Original: G oo g l e M a C M P
Revised: G oo g l e M a C M P

Original: f a c a d C U[-B 
-]
Revised: f a c a d C U[-B 
-]

Original:  R 231 ; C o v i
Revised: [Figure] R [Figure]

Original:  d W e b L T R C R
Revised: ; C o v i d W e b L T R C R

Original: I M D B - W
Revised: I M D B - W

Original: I K
Revised: I K

Original: I
Revised: I

Original: P l a c e s- S t a n d a r d D T D
Revised: P l a c e s- S t a n d a r d D T D

Original: c
Revised: c

Original: e l e b A - c r o pp e d c e l e b A H Qc
Revised: e l e b A - c r o pp e d c e l e b A H Qc

Original: e
Revised: e

Original: l e b A H QF li c k e r H QR e D W e b +o t
Revised: l e b A H QF li c k e r H QR e D W e b +o t

Original: h e r s N
Revised: h e r s N

Original: I T R C II T R e f
Revised: I T R C II T R e f

Original: i n e d M K A
Revised: i n e d M K A

Original: I S T
Revised: I S T

Original: i m a g e n e tk - ( s u b s e t )
Revised: i m a g e n e tk - ( s u b s e t )

Original: C O C O
Revised: C O C O

Original: P A S C A L
Revised: P A S C A L

Original: P e n o b s c
Revised: P e n o b s c

Original: o t c
Revised: o t c

Original: i t y s c a p e s c u s t o m F l D u t c h F 3 p r o p r i e t a r y
Revised: i t y s c a p e s c u s t o m F l D u t c h F 3 p r o p r i e t a r y

Original: P A S C A L - C o n t e x t
Revised: P A S C A L - C o n t e x t

Original: C O C O 

Revised: C O C O

Original: A D E 20 k
Revised: A D E 20 k

Original: i m a g e n e tk
Revised: i m a g e n e tk

Original: [Table]
Revised: [Table]

Original: p l a n t s a s t r o n o m y m e d i c a l x r a y c a r s m a p f o r m u l a t e x t u r e s d e p
Revised: p l a n t s a s t r o n o m y m e d i c a l x r a y c a r s m a p f o r m u l a t e x t u r e s d e p

Original: t
Revised: t

Original: h m e d i c a l m r i a r t t h e r m a l m e d i c a l c t s e i s m i c
Revised: h m e d i c a l m r i a r t t h e r m a l m e d i c a l c t s e i s m i c

Original: f a c e s n a t
Revised: f a c e s n a t

Original: u r a l
Revised: u r a l

Original: Datatype
Revised: Datatype

Original: S u p e
Revised: S u p e

Original: r
Revised: r

Original: R e s o l u t
Revised: R e s o l u t

Original: i o n
Revised: i o n

Original: S t y l e T r a n s f e r
Revised: S t y l e T r a n s f e r

Original: F a c e D e t e c t i o n
Revised: F a c e D e t e c t i o n

Original: D e p t
Revised: D e p t

Original: h
Revised: h

Original: E s t
Revised: E s t

Original: i m a t i o n
Revised: i m a t i o n

Original: F a c e R e
Revised: F a c e R e

Original: c o g n i t i o n
Revised: c o g n i t i o n

Original: G A N - D i s c
Revised: G A N - D i s c

Original: r
Revised: r

Original: i m i n a t
Revised: i m i n a t

Original: o r
Revised: o r

Original: G A N - G e n e r a t
Revised: G A N - G e n e r a t

Original: o r
Revised: o r

Original: O b j e c t D e t e c t i o n
Revised: O b j e c t D e t e c t i o n

Original: S e g m e n t a t i o n
Revised: S e g m e n t a t i o n

Original: C l a ss i f
Revised: C l a ss i f

Original: i c a t i o n
Revised: i c a t i o n

Original: Task
Revised: Task

Original: N
Revised: N

Original: I H c
Revised: I H c

Original: h e s t l gg - m r i - s e g m e n t a t i o n c u s t
Revised: h e s t l gg - m r i - s e g m e n t a t i o n c u s t

Original: o m R e f i n e d M
Revised: o m R e f i n e d M

Original: R 231 ; C o v i
Revised: R 231 ; C o v i

Original: d W e b R
Revised: d W e b R

Original: P e n o b s c o t P a d C h e s t
Revised: P e n o b s c o t P a d C h e s t

Original: P A S C A L
Revised: P A S C A L

Original: P A S C A L 2007 ; P
Revised: P A S C A L 2007 ; P

Original: A S C A L w i d e r f a c e L T R C
Revised: A S C A L w i d e r f a c e L T R C

Original: K a gg l e R S N
Revised: K a gg l e R S N

Original: A + 4 o t h e r s
Revised: A + 4 o t h e r s

Original: K a gg l e R S N
Revised: K a gg l e R S N

Original: A K A I S T w i
Revised: A K A I S T w i

Original: d
Revised: d

Original: e
Revised: e

Original: r
Revised: r

Original: f a c e - ( c l e a n e d )
Revised: f a c e - ( c l e a n e d )

Original: C U B 

Revised: C U B

Original: B S D
Revised: B S D

Original: F r a c t a l D B10 k F E R P l u s C
Revised: F r a c t a l D B10 k F E R P l u s C

Original: h
Revised: h

Original: e X p e r t
Revised: e X p e r t

Original: C h a L e a r n L
Revised: C h a L e a r n L

Original: A P
Revised: A P

Original: C a r v a n a
Revised: C a r v a n a

Original: F r a c t a l D B1 k c e l e b A H Qc e
Revised: F r a c t a l D B1 k c e l e b A H Qc e

Original: l e b A H Qc e
Revised: l e b A H Qc e

Original: l e b A - c r o pp e d
Revised: l e b A - c r o pp e d

Original: U K B i
Revised: U K B i

Original: o
Revised: o

Original: b a n k C M P
Revised: b a n k C M P

Original: f a c a d
Revised: f a c a d

Original: R e D W e b +o t
Revised: R e D W e b +o t

Original: h e r s
Revised: h e r s

Original: G oo g l e M a A d i e n c e D T D
Revised: G oo g l e M a A d i e n c e D T D

Original: M I M I C - C
Revised: M I M I C - C

Original: X
Revised: X

Original: R
Revised: R

Original: F li c k e r H QI M D B - W
Revised: F li c k e r H QI M D B - W

Original: I K
Revised: I K

Original: I N I T R C II T D u t c h F 3 c i t
Revised: I N I T R C II T D u t c h F 3 c i t

Original: y s c a p e s
Revised: y s c a p e s

Original: P l a c e s- S t a n d a r d
Revised: P l a c e s- S t a n d a r d

Original: P A S C A L - C o n t e x t
Revised: P A S C A L - C o n t e x t

Original: i m a g e n e tk - ( s u b s e t )
Revised: i m a g e n e tk - ( s u b s e t )

Original: C O C O 

Revised: C O C O

Original: A D E 

Revised: A D E

Original: k c u s t o m F l C O C O 

Revised: k c u s t o m F l C O C O

Original: p r o p r i e t a r y
Revised: p r o p r i e t a r y

Original: i m a g e n e tk
Revised: i m a g e n e tk

Revised: [Figure]

Original: [Figure]
Revised: [Figure]

Original: c a r s p l a n t s t
Revised: c a r s p l a n t s t

Original: h e r m a l a s t r o n o m y d e p t
Revised: h e r m a l a s t r o n o m y d e p t

Original: h f
Revised: h f

Original: o r m u l a m a p t
Revised: o r m u l a m a p t

Original: e x t u r e
Revised: e x t u r e

Original: s m e d i c a l c t s e i s m i c a r t m e d i c a l m r
Revised: s m e d i c a l c t s e i s m i c a r t m e d i c a l m r

Original: i m e d i c a l x r a y f a c e s n a t u r a l
Revised: i m e d i c a l x r a y f a c e s n a t u r a l

Original: [Figure]
Revised: [Figure]

Original: F a c e R e
Revised: F a c e R e

Original: c o g n i t i o n
Revised: c o g n i t i o n

Original: S u p e
Revised: S u p e

Original: r
Revised: r

Original: R e s o l u t
Revised: R e s o l u t

Original: i o n
Revised: i o n

Original: D e p t
Revised: D e p t

Original: h
Revised: h

Original: E s t
Revised: E s t

Original: i m a t i o n
Revised: i m a t i o n

Original: F a c e D e t e c t i o n
Revised: F a c e D e t e c t i o n

Original: S t y l e T r a n s f e r
Revised: S t y l e T r a n s f e r

Original: G A N - D i s c
Revised: G A N - D i s c

Original: r
Revised: r

Original: i m i n a t
Revised: i m i n a t

Original: o r
Revised: o r

Original: O b j e c t D e t e c t i o n
Revised: O b j e c t D e t e c t i o n

Original: G A N - G e n e r a t
Revised: G A N - G e n e r a t

Original: o r
Revised: o r

Original: S e g m e n t a t i o n
Revised: S e g m e n t a t i o n

Original: C l a ss i f
Revised: C l a ss i f

Original: i c a t i o n
Revised: i c a t i o n

Revised: Task

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: 2 0 2 c

Original: [Figure]
Revised: [Figure]

Original: 2 0 2 c2 0 2 c2 0 2 c2 0 2 c2 0 2 c2 0 2 c2 0 2 c

Original: [Figure]
Revised: [Figure]

Original: 22 c22 c22 c22 c22 c22 c22 c22 c

Original: [Figure]
Revised: [Figure]

Revised: c

Original: [Figure]
Revised: [Figure]

Original: 2 0 2 c
Revised: 2 0 2 c

Original: [Figure]
Revised: [Figure]

Original: 2 0 2 c2 0 2 c2 0 2 c2 0 2 c2 0 2 c2 0 2c2 0 2 c
Revised: 2 0 2 c2 0 2 c2 0 2 c2 0 2 c2 0 2 c2 0 2 c

Original: [Figure]
Revised: [Figure]

Original: [-22 c
-]
Revised: [-22 c
-]

Original: [Figure]
Revised: [Figure]

Original: 22 c22 c

Original: [Figure]
Revised: [Figure]

Original: 22 c22 c22 c22 c

Original: [Figure]
Revised: [Figure]

Original: A full overview over all used models will be made available on the data website after publication.
Revised: [Figure]