Original: Exploiting Unlabeled Data to Improve Detection of Visual Anomalies in Soft Fruits
Revised: Exploiting Unlabeled Data to Improve Detection of Visual Anomalies in Soft Fruits

Original: Abstract
Revised: Abstract

Original: Recently, self-supervised learning methods have been pro- posed to learn a useful representation for visual detection of anomalous, unhealthy crops while a neural network classifies augmented images of normal instances, which are relatively easy-to-obtain.
Revised: Recently, self-supervised learning methods have been pro- posed to learn a useful representation for visual detection of anomalous, unhealthy crops while a neural network classifies augmented images of normal instances, which are relatively easy-to-obtain.

Original: Their pipelines are largely designed within the one-class classification paradigm, in which training samples are all of normal (negative) class, considering the severe scarcity of anomalous (positive) observations in realistic scenarios.
Revised: Their pipelines are largely designed within the one-class classification paradigm, in which training samples are all of normal (negative) class, considering the severe scarcity of anomalous (positive) observations in realistic scenarios.

Original: In this paper, we study whether this “homogeneity” of training set is necessary to boost up the performance of learned detector because otherwise “unlabeled” data newly gathered from the field could simply be utilized during training without the need of expensive human annotation.
Revised: In this paper, we study whether this “homogeneity” of training set is necessary to boost up the performance of learned detector because otherwise “unlabeled” data newly gathered from the field could simply be utilized during training without the need of expensive human annotation.

Original: To be specific, we first explore the scenarios treating every unlabeled instance as a normal one as the proportion of anomalous samples in the unlabeled set varies.
Revised: To be specific, we first explore the scenarios treating every unlabeled instance as a normal one as the proportion of anomalous samples in the unlabeled set varies.

Original: Also, we introduce an iterative training procedure for “negative-unlabeled” learn- ing, in which the unlabeled data are incrementally labeled based on predictions to train an one-class classifier with sam- ples regarded to potentially be normal.
Revised: Also, we introduce an iterative training procedure for “negative-unlabeled” learn- ing, in which the unlabeled data are incrementally labeled based on predictions to train an one-class classifier with sam- ples regarded to potentially be normal.

Original: Our experiments use
Revised: Our experiments use

Original: CH-Rand—a state-of-the-art method for learning useful rep- resentations for anomaly detection from fruit images—on the
Revised: CH-Rand—a state-of-the-art method for learning useful rep- resentations for anomaly detection from fruit images—on the

Original: Riseholme-2021 dataset, which includes a number of healthy and unhealthy strawberry images collected under realistic conditions.
Revised: Riseholme-2021 dataset, which includes a number of healthy and unhealthy strawberry images collected under realistic conditions.

Original: Specifically, the results show that using an un- labeled set as normal data can lead to the 8 .
Revised: Specifically, the results show that using an un- labeled set as normal data can lead to the 8 .

Original: 7% performance improvement without any effort for labeling, though 4% in the set are of anomalous strawberry.
Revised: 7% performance improvement without any effort for labeling, though 4% in the set are of anomalous strawberry.

Original: In addition, our iterative training can benefit trained anomaly detectors by automati- cally filtering out unlabeled anomalies to reduce the overall anomaly ratio in the unlabeled data from 6% to 4 .
Revised: In addition, our iterative training can benefit trained anomaly detectors by automati- cally filtering out unlabeled anomalies to reduce the overall anomaly ratio in the unlabeled data from 6% to 4 .

Original: 3% consequently leading to better detection performance.
Revised: 3% consequently leading to better detection performance.

Original: Introduction
Revised: Introduction

Original: Automated detection of unhealthy crops is crucial to realize precision agriculture, in which specialized treatments can be selectively applied to targeted individuals so as to not only increase overall productivity but also save available resources and protect the natural environments.
Revised: Automated detection of unhealthy crops is crucial to realize precision agriculture, in which specialized treatments can be selectively applied to targeted individuals so as to not only increase overall productivity but also save available resources and protect the natural environments.

Original: Deep learning methods thus have been actively studied to adapt their great success in computer vision and field robotics to practical applications to natural outdoor environments in agricul- ture (Salazar-Gomez et al. 2022;
Revised: Deep learning methods thus have been actively studied to adapt their great success in computer vision and field robotics to practical applications to natural outdoor environments in agricul- ture (Salazar-Gomez et al. 2022;

Original: Gao et al
Revised: Gao et al

Original: In particular, one-class classification has been widely adopted as a useful approach for building anomaly detectors because of its realistic assumption that those can be generated only from observations of normal class (e.g., healthy fruits) while samples of anomalous class (e.g., unhealthy fruits) are too rare to be available during training (Choi et al. 2022).
Revised: In particular, one-class classification has been widely adopted as a useful approach for building anomaly detectors because of its realistic assumption that those can be generated only from observations of normal class (e.g., healthy fruits) while samples of anomalous class (e.g., unhealthy fruits) are too rare to be available during training (Choi et al. 2022).

Original: In this paper, we hypothesize that as illustrated in Fig.
Revised: In this paper, we hypothesize that as illustrated in Fig.

Original: 1, such anomaly detectors could be enhanced by involving an “unlabeled” yet easy-to-obtain dataset in training, although some anomalous (positive) samples may be contained along with normal (negative) instances in it.
Revised: 1, such anomaly detectors could be enhanced by involving an “unlabeled” yet easy-to-obtain dataset in training, although some anomalous (positive) samples may be contained along with normal (negative) instances in it.

Original: Inspired by (Lee et al.
Revised: Inspired by (Lee et al.

Original: 2022), we first evaluate detection performance under various levels of anomaly ratio in the unlabeled set to investigate if simply treating them as normal data would tend to deteriorate the models.
Revised: 2022), we first evaluate detection performance under various levels of anomaly ratio in the unlabeled set to investigate if simply treating them as normal data would tend to deteriorate the models.

Original: Also, we propose an iterative algorithm to systematically select unlabeled samples to utilize for subsequent training based on estimated anomaly scores.
Revised: Also, we propose an iterative algorithm to systematically select unlabeled samples to utilize for subsequent training based on estimated anomaly scores.

Original: A relevant approach is positive-unlabeled (PU) learning in (Mu et al. 2021;
Revised: A relevant approach is positive-unlabeled (PU) learning in (Mu et al. 2021;

Original: Zhang et al. 2017).
Revised: Zhang et al. 2017).

Original: However, we adopt an oneclass classifier instead of a binary model, and our task is called “negative-unlabeled” (NU) learning since labeled examples included in the training set are of negative class.
Revised: However, we adopt an oneclass classifier instead of a binary model, and our task is called “negative-unlabeled” (NU) learning since labeled examples included in the training set are of negative class.

Original: Specifically, we use Channel Randomisation (CHRand) (Choi et al.
Revised: Specifically, we use Channel Randomisation (CHRand) (Choi et al.

Original: 2022)—a state-of-the-art method for learning useful representations for anomaly detection from fruit images—on the Riseholme-2021 dataset, which includes a number of healthy and unhealthy strawberry images collected under realistic conditions.
Revised: 2022)—a state-of-the-art method for learning useful representations for anomaly detection from fruit images—on the Riseholme-2021 dataset, which includes a number of healthy and unhealthy strawberry images collected under realistic conditions.

Original: To the best of our knowledge, we are the first demonstrating NU learning for classification of anomalies with agricultural data.
Revised: To the best of our knowledge, we are the first demonstrating NU learning for classification of anomalies with agricultural data.

Original: In particular, our study offers crucial perspectives from empirical results in order to fully exploit unlabeled datasets:
Revised: In particular, our study offers crucial perspectives from empirical results in order to fully exploit unlabeled datasets:

Original: • Simple combination of unlabeled data into the training set of normal class can outperform the model not using it at all, even when the anomaly ratio is non-zero.
Revised: • Simple combination of unlabeled data into the training set of normal class can outperform the model not using it at all, even when the anomaly ratio is non-zero.

Original: • Negative impact from a higher anomaly ratio can be mitigated by providing a larger size of unlabeled set.
Revised: • Negative impact from a higher anomaly ratio can be mitigated by providing a larger size of unlabeled set.

Original: • Anomaly ratio can be reduced by the NU learning that repeats choosing samples predicted to be normal with which a better classifier can finally be obtained.
Revised: • Anomaly ratio can be reduced by the NU learning that repeats choosing samples predicted to be normal with which a better classifier can finally be obtained.

Original: Preliminary: Channel Randomisation
Revised: Preliminary: Channel Randomisation

Original: In this section, we briefly review the CH-Rand technique, which was proposed to learn useful representations of visual anomalies in fruits only from normal examples (Choi et al.
Revised: In this section, we briefly review the CH-Rand technique, which was proposed to learn useful representations of visual anomalies in fruits only from normal examples (Choi et al.

Original: Unlike other state-of-the-art methods such as CutPaste (Bergmann et al. 2021) designed to identify structural normality, CH-Rand is to focus more on patterns of color for agricultural applications since those generally change dramatically depending on the health status of crops.
Revised: Unlike other state-of-the-art methods such as CutPaste (Bergmann et al. 2021) designed to identify structural normality, CH-Rand is to focus more on patterns of color for agricultural applications since those generally change dramatically depending on the health status of crops.

Original: In particular, Choi et al.
Revised: In particular, Choi et al.

Original: (2022) have discovered that informative representations g θ can be obtained within a neural network f Θ during the pretext task shown in Fig.
Revised: (2022) have discovered that informative representations g θ can be obtained within a neural network f Θ during the pretext task shown in Fig.

Original: 2—i.e., it is to distinguish original images I ∈ R W × H × C from channelrandomized ones A ∈ R W × H × C , where W and H are the width and the height, respectively, and C denotes the number of channels, which is set to 3 in the case of RGB space.
Revised: 2—i.e., it is to distinguish original images I ∈ R W × H × C from channelrandomized ones A ∈ R W × H × C , where W and H are the width and the height, respectively, and C denotes the number of channels, which is set to 3 in the case of RGB space.

Original: An application of CH-Rand can be regarded as adopting a random permutation of colour channels with a possibility of repetition over the entire image I .
Revised: An application of CH-Rand can be regarded as adopting a random permutation of colour channels with a possibility of repetition over the entire image I .

Original: More formally, for a new input I , an arbitrary function π : χ → χ ′ is drawn for a random permutation, where χ = { 1 , 2 , ..., C } is the set of channel indices, and χ ′ ∈ P ( χ ) \ ∅ , as P denotes the powerset of input.
Revised: More formally, for a new input I , an arbitrary function π : χ → χ ′ is drawn for a random permutation, where χ = { 1 , 2 , ..., C } is the set of channel indices, and χ ′ ∈ P ( χ ) \ ∅ , as P denotes the powerset of input.

Original: The function π then determines each pixel a cw,h in A as follows:
Revised: The function π then determines each pixel a cw,h in A as follows:

Original: [Equation]
Revised: [Equation]

Original: Note that the same π is applied for every w, h, and c to keep the fixed channel arrangement over the image.
Revised: Note that the same π is applied for every w, h, and c to keep the fixed channel arrangement over the image.

Original: In fact, we avoid the result to be A
Revised: In fact, we avoid the result to be A

Original: =
Revised: =

Original: I by continuing drawing a new π until ∃ c ∈ χ, c ̸
Revised: I by continuing drawing a new π until ∃ c ∈ χ, c ̸

Original: = π ( c ) .
Revised: = π ( c ) .

Original: In other words, 26 possible channel sequences can be generated by this augmentation for a typical 3 -channel color space, such as RGB.
Revised: In other words, 26 possible channel sequences can be generated by this augmentation for a typical 3 -channel color space, such as RGB.

Original: Several examples of augmentation are visualized in Fig.
Revised: Several examples of augmentation are visualized in Fig.

Original: For representation learning, a classifier is employed to learn to infer if input images contain any artefacts in color generated by CH-Rand.
Revised: For representation learning, a classifier is employed to learn to infer if input images contain any artefacts in color generated by CH-Rand.

Original: As in (Li et al. 2021; Gidaris, Singh, and Komodakis 2018), the loss function is set up as below to train a deep neural network f Θ with images I from a training dataset D N :
Revised: As in (Li et al. 2021; Gidaris, Singh, and Komodakis 2018), the loss function is set up as below to train a deep neural network f Θ with images I from a training dataset D N :

Original: [Equation]
Revised: [Equation]

Original: where CHR returns the output of CH-Rand, and H is the binary cross entropy function to consider prediction errors.
Revised: where CHR returns the output of CH-Rand, and H is the binary cross entropy function to consider prediction errors.

Original: Choi et al.
Revised: Choi et al.

Original: (2022) suggested taking a random batch D ′ ⊆
Revised: (2022) suggested taking a random batch D ′ ⊆

Original: D N at each iteration applying CH-Rand only to a half of it.
Revised: D N at each iteration applying CH-Rand only to a half of it.

Original: Anomaly Score Prediction
Revised: Anomaly Score Prediction

Original: According to (Choi et al. 2022), once a neural network f Θ has been trained, the outputs of an intermediate layer are employed as feature representations g θ of input images.
Revised: According to (Choi et al. 2022), once a neural network f Θ has been trained, the outputs of an intermediate layer are employed as feature representations g θ of input images.

Original: Similarly to (Perera and Patel 2019), the anomaly score s for an input image I ′ can then be estimated using the average Euclidean distance to the k nearest neighbors M ⊆ D N on the space of g θ —i.e., s ( I ′ )
Revised: Similarly to (Perera and Patel 2019), the anomaly score s for an input image I ′ can then be estimated using the average Euclidean distance to the k nearest neighbors M ⊆ D N on the space of g θ —i.e., s ( I ′ )

Original: =
Revised: =

Original: (1 /k ) (cid:80) I∈M
Revised: (1 /k ) (cid:80) I∈M

Original: δ (cid:0) g
Revised: δ (cid:0) g

Original: θ ( I ) , g θ ( I ′ ) (cid:1) , where δ is the function of Euclidean distance.
Revised: θ ( I ) , g θ ( I ′ ) (cid:1) , where δ is the function of Euclidean distance.

Original: Hence, I ′ is predicted to be either anomalous if s is larger than a certain threshold τ or normal otherwise.
Revised: Hence, I ′ is predicted to be either anomalous if s is larger than a certain threshold τ or normal otherwise.

Original: Problem Description
Revised: Problem Description

Original: Instead of one-class classification, we here explore “NUlearning” (Ding et al. 2022) scenarios, in which an unlabeled dataset is available along with a labeled set of normal class (cf. Fig. 1).
Revised: Instead of one-class classification, we here explore “NUlearning” (Ding et al. 2022) scenarios, in which an unlabeled dataset is available along with a labeled set of normal class (cf. Fig. 1).

Original: To be specific, while the data set D N is known to be of normal class (e.g., healthy crop), we also assume that a disjoint set D U is available, but samples therein are not annotated at all.
Revised: To be specific, while the data set D N is known to be of normal class (e.g., healthy crop), we also assume that a disjoint set D U is available, but samples therein are not annotated at all.

Original: In other words, D U may contain some instances of anomalous class (e.g., diseased crop), and so, simply involving it in training of one-class classifier might cause an unexpected impact on the overall performance.
Revised: In other words, D U may contain some instances of anomalous class (e.g., diseased crop), and so, simply involving it in training of one-class classifier might cause an unexpected impact on the overall performance.

Original: In this work, we thus investigate the utility of such unlabeled datasets and the proposed NU learning method to discuss useful strategies to maximize the positive effect from using those.
Revised: In this work, we thus investigate the utility of such unlabeled datasets and the proposed NU learning method to discuss useful strategies to maximize the positive effect from using those.

Original: For empirical exploration, we mainly use CH-Rand to build detectors of anomalous images of soft fruit (i.e., strawberry).
Revised: For empirical exploration, we mainly use CH-Rand to build detectors of anomalous images of soft fruit (i.e., strawberry).

Original: More formally, our goal is to gain useful representations g θ from training data Λ , which is the union of D N and D U
Revised: More formally, our goal is to gain useful representations g θ from training data Λ , which is the union of D N and D U

Original: Therefore, our new loss function can be re- formulated from Equation 2 by replacing D N with Λ :
Revised: Therefore, our new loss function can be re- formulated from Equation 2 by replacing D N with Λ :

Original: [Equation]
Revised: [Equation]

Original: I∈
Revised: I∈

Original: [Equation]
Revised: [Equation]

Original: Input: D N , D U , m
Revised: Input: D N , D U , m

Original: Output: Classifier f Θ 1: D ′ N ← D N 2: while |D U | ≥ m do 3:
Revised: Output: Classifier f Θ 1: D ′ N ← D N 2: while |D U | ≥ m do 3:

Original: Initialize f Θ 4:
Revised: Initialize f Θ 4:

Original: Train f Θ on D ′ N using CH-Rand 5:
Revised: Train f Θ on D ′ N using CH-Rand 5:

Original: Compute anomaly score s j for each I j ∈ D U 6:
Revised: Compute anomaly score s j for each I j ∈ D U 6:

Original: D ′
Revised: D ′

Original: U ← {I t : s t ≤ m -th lowest socre s ∗ } 7:
Revised: U ← {I t : s t ≤ m -th lowest socre s ∗ } 7:

Original: D ′ N ← D ′ N ∪ D ′ U 8:
Revised: D ′ N ← D ′ N ∪ D ′ U 8:

Original: D U ← D U − D
Revised: D U ← D U − D

Original: U 9: end while 10: return f Θ
Revised: U 9: end while 10: return f Θ

Original: In experiments, various conditions on D U are considered in learning representations g θ while examining the reliability of anomaly scores computed based on them
Revised: In experiments, various conditions on D U are considered in learning representations g θ while examining the reliability of anomaly scores computed based on them

Original: . Negative-Unlabeled Learning
Revised: . Negative-Unlabeled Learning

Original: Inspired by (Mu et al. 2021), we build an iterative framework to gradually merge some of informative samples from the unlabeled set D U into the labeled D N .
Revised: Inspired by (Mu et al. 2021), we build an iterative framework to gradually merge some of informative samples from the unlabeled set D U into the labeled D N .

Original: In particular, our approach is to prioritize the ones that are predicted to be most likely to be of normal, negative class so that the final selection of data can be utilized for training a one-class classifier.
Revised: In particular, our approach is to prioritize the ones that are predicted to be most likely to be of normal, negative class so that the final selection of data can be utilized for training a one-class classifier.

Original: Algorithm 1 shows that initially only the labeled dataset D N is used for learning a representation in f Θ with
Revised: Algorithm 1 shows that initially only the labeled dataset D N is used for learning a representation in f Θ with

Original: CH-Rand, but at each iteration, m images from D U with the lowest anomaly scores are offered a pseudo-label as normal to move from D U to the training set D ′ N for the next iteration.
Revised: CH-Rand, but at each iteration, m images from D U with the lowest anomaly scores are offered a pseudo-label as normal to move from D U to the training set D ′ N for the next iteration.

Original: This process repeats until less than m images finally remain in the unlabeled set D U .
Revised: This process repeats until less than m images finally remain in the unlabeled set D U .

Original: An alternative approach could be to use such an iterative procedure to gather both positive and negative samples from the unlabeled dataset to learn a binary classifier (Zhang et al.
Revised: An alternative approach could be to use such an iterative procedure to gather both positive and negative samples from the unlabeled dataset to learn a binary classifier (Zhang et al.

Original: However, the selection size m ′ for the positive class needs to be carefully chosen because anomaly ratios are usually unknown.
Revised: However, the selection size m ′ for the positive class needs to be carefully chosen because anomaly ratios are usually unknown.

Original: Furthermore, the binary classifier might suffer from the severe class imbalance problem if the ratio is significantly small, which would motivate to use an oneclass classifier.
Revised: Furthermore, the binary classifier might suffer from the severe class imbalance problem if the ratio is significantly small, which would motivate to use an oneclass classifier.

Original: Experiments
Revised: Experiments

Original: Here, we first describe technical information of experimental configurations.
Revised: Here, we first describe technical information of experimental configurations.

Original: To be specific, the following sections offer the details of Riseholme-2021 dataset, which is used throughout the experiments, as well as the network structures and hyperparameters for training sessions.
Revised: To be specific, the following sections offer the details of Riseholme-2021 dataset, which is used throughout the experiments, as well as the network structures and hyperparameters for training sessions.

Original: Primary results of each experiment are then discussed.
Revised: Primary results of each experiment are then discussed.

Original: [Figure]
Revised: [Figure]

Original: Riseholme-2021 Dataset
Revised: Riseholme-2021 Dataset

Original: For experiments, we utilize the Riseholme-2021 datasetsince it contains several thousands of strawberry images under three normal categories—such as Ripe , Unripe , and
Revised: For experiments, we utilize the Riseholme-2021 datasetsince it contains several thousands of strawberry images under three normal categories—such as Ripe , Unripe , and

Original: Occluded —and Anomalous in natural environments.
Revised: Occluded —and Anomalous in natural environments.

Original: Similar to (Choi et al. 2022), we split the data into Train ,
Revised: Similar to (Choi et al. 2022), we split the data into Train ,

Original: Val , and Test sets, but the Train set consists of a labeled set D N of normal classes and an unlabeled set D U , where |D N | is fixed to 300 while |D U | can vary in each experiment.
Revised: Val , and Test sets, but the Train set consists of a labeled set D N of normal classes and an unlabeled set D U , where |D N | is fixed to 300 while |D U | can vary in each experiment.

Original: In addition, the Test set contains 674 images from all normal categories as well as 99 instances of anomalous class, and the Val set has 337 normal examples.
Revised: In addition, the Test set contains 674 images from all normal categories as well as 99 instances of anomalous class, and the Val set has 337 normal examples.

Original: Furthermore, general steps for image preprocessing follow, in which each image is resized to 64 × 64 pixels, and conventional augmentations 2 including heuristic color jitters and horizontal/vertical flips are performed before application of CH-Rand.
Revised: Furthermore, general steps for image preprocessing follow, in which each image is resized to 64 × 64 pixels, and conventional augmentations 2 including heuristic color jitters and horizontal/vertical flips are performed before application of CH-Rand.

Original: Also, each pixel value is normalized to be between − 1 and 1 .
Revised: Also, each pixel value is normalized to be between − 1 and 1 .

Original: Implementation Details
Revised: Implementation Details

Original: We adopt the same structure of a deep network as in (Choi et al. 2022) using the publicly available code 3 .
Revised: We adopt the same structure of a deep network as in (Choi et al. 2022) using the publicly available code 3 .

Original: To be specific, the network has 5 ConvLayers and 2 DenseLayers, where the number of 3 × 3 convolutional filters increments at each layer—i.e., 64 , 128 , 256 , 512 , and 512 —and the DenseLayers employ 256 and 1 output nodes, respectively.
Revised: To be specific, the network has 5 ConvLayers and 2 DenseLayers, where the number of 3 × 3 convolutional filters increments at each layer—i.e., 64 , 128 , 256 , 512 , and 512 —and the DenseLayers employ 256 and 1 output nodes, respectively.

Original: Also, the output from each ConvLayer is processed by a BatchNorm layer and a 2 × 2 MaxPool layer, and every activation is computed with the LeakyReLU function except the sigmoid function at the last DenseLayer.
Revised: Also, the output from each ConvLayer is processed by a BatchNorm layer and a 2 × 2 MaxPool layer, and every activation is computed with the LeakyReLU function except the sigmoid function at the last DenseLayer.

Original: In particular, the learned representation g is taken from the outputs at the first DenseLayer, on which anomaly scores can be estimated with k = 1 nearest neighbor in training data D N since k = 1 was found to be most effective as all normal categories are considered (Choi et al. 2022).
Revised: In particular, the learned representation g is taken from the outputs at the first DenseLayer, on which anomaly scores can be estimated with k = 1 nearest neighbor in training data D N since k = 1 was found to be most effective as all normal categories are considered (Choi et al. 2022).

Original: Note here that the unlabeled set D U is not considered for distance calculation to avoid the case in which the distance to its anomalies may lead to inaccurate prediction.
Revised: Note here that the unlabeled set D U is not considered for distance calculation to avoid the case in which the distance to its anomalies may lead to inaccurate prediction.

Original: Evaluation Metrics
Revised: Evaluation Metrics

Original: For evaluation, we compute the Area Under the
Revised: For evaluation, we compute the Area Under the

Original: Curve (AUC) of the Precision-Recall (PR) curve to take into account the highly skewed class distribution in
Revised: Curve (AUC) of the Precision-Recall (PR) curve to take into account the highly skewed class distribution in

Original: It.
Revised: It.

Original: It. It.
Revised: It. It.

Original: It.
Revised: It.

Original: Total
Revised: Total

Original: [Table]
Revised: [Table]

Original: N
Revised: N

Original: [Table]
Revised: [Table]

Original: the Test set ( 674 normal vs. 99 anomalous) (Davis and
Revised: the Test set ( 674 normal vs. 99 anomalous) (Davis and

Original: Goadrich 2006).
Revised: Goadrich 2006).

Original: Also, every resulting score here is the average of three separate runs to mitigate the stochastic effects from training neural networks.
Revised: Also, every resulting score here is the average of three separate runs to mitigate the stochastic effects from training neural networks.

Original: Moreover, each run saves the model achieving the highest validation accuracy until either training is performed for 1 . 2 K epochs or a validation accuracy of > .
Revised: Moreover, each run saves the model achieving the highest validation accuracy until either training is performed for 1 . 2 K epochs or a validation accuracy of > .

Original: 95 has been achieved in average for the past five checkpoints while each checkpoint is examined every 30 epochs.
Revised: 95 has been achieved in average for the past five checkpoints while each checkpoint is examined every 30 epochs.

Original: Results
Revised: Results

Original: This section discusses observed impacts on final performance of anomaly detector under different settings.
Revised: This section discusses observed impacts on final performance of anomaly detector under different settings.

Original: Various Compositions of D U : Here, we examine the impact on the overall performance depending on the configuration of the unlabeled data set D U .
Revised: Various Compositions of D U : Here, we examine the impact on the overall performance depending on the configuration of the unlabeled data set D U .

Original: To be specific, we here control two parameters:
Revised: To be specific, we here control two parameters:

Original: • Size of D U : ∆ ∈ { 0 , 450 , 900 } • Ratio of anomalies in D U : α = { 0% , 4% , 6% }
Revised: • Size of D U : ∆ ∈ { 0 , 450 , 900 } • Ratio of anomalies in D U : α = { 0% , 4% , 6% }

Original: In particular, the range of α is based on the anomaly ratio of 4 .
Revised: In particular, the range of α is based on the anomaly ratio of 4 .

Original: 3% in the whole Riseholme-2021 dataset
Revised: 3% in the whole Riseholme-2021 dataset

Original: Figure 4a provides average performance for each controlled setting.
Revised: Figure 4a provides average performance for each controlled setting.

Original: Specifically, for α = 0% , as expected, more data in D U can provide improved detection performance since the neural network can gain a better quality representation from diverse normal samples and their channel randomisation.
Revised: Specifically, for α = 0% , as expected, more data in D U can provide improved detection performance since the neural network can gain a better quality representation from diverse normal samples and their channel randomisation.

Original: In particular, the improvement can be enhanced due to no interference of anomalies in the set.
Revised: In particular, the improvement can be enhanced due to no interference of anomalies in the set.

Original: On the other hand, any case with some level of anomaly ratio offers poor performance particularly when ∆ = 450 , which is even worse than the case not using it ( ∆ = 0 ).
Revised: On the other hand, any case with some level of anomaly ratio offers poor performance particularly when ∆ = 450 , which is even worse than the case not using it ( ∆ = 0 ).

Original: This implies that even though D U fed more than 422 additional normal samples to the neural network, those small percentages of anomalies significantly hindered it from learning a useful representation of normal and anomalous images.
Revised: This implies that even though D U fed more than 422 additional normal samples to the neural network, those small percentages of anomalies significantly hindered it from learning a useful representation of normal and anomalous images.

Original: Consequently, the 6% ratio led to a worse quality representation than the case of 4% ratio.
Revised: Consequently, the 6% ratio led to a worse quality representation than the case of 4% ratio.

Original: However, a dramatic change has been observed in the performance for those non-zero-ratio models when more data (i.e., ∆ = 900 ) were involved.
Revised: However, a dramatic change has been observed in the performance for those non-zero-ratio models when more data (i.e., ∆ = 900 ) were involved.

Original: To be specific, 4% and 6% ratios lead to 8 .
Revised: To be specific, 4% and 6% ratios lead to 8 .

Original: 7% and 3 .
Revised: 7% and 3 .

Original: 9% performance improvements over the scenario only using D N (i.e., ∆ = 0 ).
Revised: 9% performance improvements over the scenario only using D N (i.e., ∆ = 0 ).

Original: Though those do not perform as well as the model of α = 0% using the same amount of data, the 4% ratio can show a 5 .
Revised: Though those do not perform as well as the model of α = 0% using the same amount of data, the 4% ratio can show a 5 .

Original: 5% better result than the 0% -ratio model with ∆ = 450 .
Revised: 5% better result than the 0% -ratio model with ∆ = 450 .

Original: This successful result can be surprising since the number of involved anomalies increases while ∆ becomes doubled because of the fixed ratio α .
Revised: This successful result can be surprising since the number of involved anomalies increases while ∆ becomes doubled because of the fixed ratio α .

Original: From this observation,
Revised: From this observation,

Original: we can
Revised: we can

Original: [Figure]
Revised: [Figure]

Original: discover that unlabeled datasets can be highly useful without any annotation, if the total size of data is sufficiently large 
Revised: discover that unlabeled datasets can be highly useful without any annotation, if the total size of data is sufficiently large .

Original: Although some anomalies can be present in proportion to interfere with representation learning, a much larger number of normal samples can compensate for the potential negative effect during training.
Revised: Although some anomalies can be present in proportion to interfere with representation learning, a much larger number of normal samples can compensate for the potential negative effect during training.

Original: NU Learning: We apply the iterative algorithm for NU learning to the case of 6% anomaly ratio.
Revised: NU Learning: We apply the iterative algorithm for NU learning to the case of 6% anomaly ratio.

Original: For ∆ = 450 , m = 100 image samples were selected from D U at each iteration whereas m = 160 instances were considered as ∆ = 900 .
Revised: For ∆ = 450 , m = 100 image samples were selected from D U at each iteration whereas m = 160 instances were considered as ∆ = 900 .

Original: That is, the former finally adds 400 images to the training set while the latter 800 instances.
Revised: That is, the former finally adds 400 images to the training set while the latter 800 instances.

Original: Table 1 implies that every case eventually reduced the number of anomalies in the training set to the anomaly ratio of around 4 .
Revised: As a result, every case eventually reduced the number of anomalies in the training set to the anomaly ratio of around 4 .

Original:  Also, Figure 4b shows that only with ∆ = 450, the model can perform 10% better than the case that does not utilize any unlabelled dataset.
Revised: 3% (Table 1). Also, in contrast to the case without NU learning, the model only with ∆ = 450 can perform 10% better than the case of not utilizing any unlabelled dataset (Figure 4b).

Original: Limitations: While NU learning can reduce anomaly ratios, our experiments have revealed that detection performance from CH-Rand can also depend on the selections in D ′ U .
Revised: Limitations: While NU learning can reduce anomaly ratios, our experiments have revealed that detection performance from CH-Rand can also depend on the selections in D ′ U .

Original: For example, under ∆ = 450 and α = 6% ,
Revised: For example, under ∆ = 450 and α = 6% ,

Original: NU learning achieved .
Revised: NU learning achieved .

Original: 36 by reducing a to 4 .
Revised: 36 by reducing a to 4 .

Original: 3% , which unexpectedly outperformed the α = 0% case.
Revised: 3% , which unexpectedly outperformed the α = 0% case.

Original: On the other hand, for ∆ = 900 and α = 6% , it still gained a reduced α of 4 .
Revised: On the other hand, for ∆ = 900 and α = 6% , it still gained a reduced α of 4 .

Original: but led to the worse performance of .
Revised: but led to the worse performance of .

Original: 36 than the α = 4% model.
Revised: 36 than the α = 4% model.

Original: Hence, further investigation on instancelevel effects may be needed.
Revised: Hence, further investigation on instancelevel effects may be needed.

Original: Conclusion
Revised: Conclusion

Original: With real strawberry images, we have shown that unlabeled datasets can be utilized to improve the performance of anomaly detectors.
Revised: With real strawberry images, we have shown that unlabeled datasets can be utilized to improve the performance of anomaly detectors.

Original: Our results imply that if the unlabeled set is sufficiently large, it can compensate for the negative impact from anomalous samples in it.
Revised: Our results imply that if the unlabeled set is sufficiently large, it can compensate for the negative impact from anomalous samples in it.

Original: Also, we have introduced an iterative algorithm of negative-unlabeled learning to reduce the ratio of anomaly with no supervision as well as achieve better detection performance. 
Revised: Also, we have introduced an iterative algorithm of negative-unlabeled learning to reduce the ratio of anomaly with no supervision as well as achieve better detection performance. Acknowledgement

Revised: This work was partially supported by USDA/NIFA (202067021-32855) and by NSF (IIS-1838207, CNS-1901218,

Revised: OIA-2134901).