Continual Learning of Neural Networks for Realtime Wireline Cable Position Inference
Authors: Jun Wang
Tianxiang Su
Venue: ICLR 2022 Submitted (2022)
Keywords: Continual Learning
Wireline Automation

Abstract: In the oil fields, Wireline cable is spooled onto a drum where computer vision techniques based on convolutional neural networks (CNNs) are applied to estimate the cable position in real time for automated spooling control. However, as new training data keeps arriving to continuously improve the network, the re-training procedure faces challenges. Online learning fashion with no memory to historical data leads to catastrophic forgetting. Meanwhile, saving all data will cause the disk space and training time to increase without bounds. In this paper, we proposed a method called the modified-REMIND (mREMIND) network. It is a replay-based continual learning method with longer memory to historical data and no memory overflow issues. Information of old data are kept for multiple iterations using a new dictionary update rule. Additionally, by dynamically partitioning the dataset, the method can be applied on devices with limited memory. In our experiments, we compared the proposed method with multiple state-of-the-art continual learning methods and the mREMIND network outperformed others both in accuracy and in disk space usage.

Under review as a conference paper at ICLR 2022
CONTINUAL LEARNING OF NEURAL NETWORKS FOR REALTIME WIRELINE CABLE POSITION INFERENCE
Anonymous authors Paper under double-blind review
ABSTRACT
In the oil fields, Wireline cable is spooled onto a drum where computer vision techniques based on convolutional neural networks (CNNs) are applied to esti- mate the cable position in real time for automated spooling control. However, as new training data keeps arriving to continuously improve the network, the re- training procedure faces challenges. Online learning fashion with no memory to historical data leads to catastrophic forgetting. Meanwhile, saving all data will cause the disk space and training time to increase without bounds. In this paper, we proposed a method called the modified-REMIN a ceplay-based continual learning method with longer memory to historical data and memory overflow issues. Information of old data kept for multiple iterations using a new dictionary update rule. Additionally, by dynamically par- titioning the dataset, the method can be applied on devices with limited memory. In our experiments, we compared the proposed method with multiple state-of ofre--of-ii of-the- art continual learning methods and the mREMIND network outperformed others both in accuracy and in disk space usage.
(MREMIND) network. It is
INTRODUCTION
With the emergence of Al-powered computer vision techniques in recent years, more and more vision-based applications being introduced into the oil fields Wan (2019) Ant (2019). As an example, to automate the Wireline spooling process, one can mount a surveillance camera inside the cabin behind the window facing the drum, feed the live spooling video into a trained neural network and infer the so-called cable position in real time (F. This inferred cable position will then be passed to spooling controller, which moves the spooling arm accordingly so that the cable can be laid onto the drum without gaps or crossovers [Su et al. (2021b). To train the neural network, example video frames with ground truth cable position were first collected offline. The weights of the network were then learnt from these training frames to minimize the prediction error. After testing and optimization, the network was then deployed to the Wireline units. Training did not stop after the deployment, however, because the example frames in the first training process may not cover all the operational conditions (different Wireline units, drum sizes, cable types. lighting and weather conditions, operations in daytime vs. nighttime, etc). To continuously improve the network performance on the out-of-distributiin frames, i.e., frames that look significantly different from those in the training set, we kept collecting new training videos help generalize the performance of the network to various operational environments. This process is called continual, lifelong or incremental neural network training.
The most straightforward setup for continual learning is to combine the new videos with all the old ones and re-train the network using the ever-increasing number of training frames [Su al. (2021a). This, however, is not scalable solution. Disk space required to hold all the past videos increases without bound over time. Also, the time to re-train new network increases rapidly with more frames to extract, process and learn. A naive scalable solution could be to re-train the network only on the new videos and remove all the old ones. This solution, however, does not work well. During re-training, a neural network tends to forget what it learned previously, causing large prediction error when applying the updated network on the old videos. This phenomenon called catastrophic forgetting in the literature Kirkpatrick al. (2017). To help the network learn the characteristics of the new videos without forgetting the information of the old ones, continual learning techniques are usually applied.

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spooling
cable
cable position
Figure 1: Wireline cable position is the distance between the contact point of the cable and the right flange the drum. It is a dynamic variable needed by a spooling controller to control the spooling arm to automate the spooling process. We used computer vision to infer the cable position from live spooling video in real time.
There exist three types of continual learning techniques Liu et al. (2020); the regularization-base method that penalizes large weight changes during re-training Kirkpatrick et al. (2017), the parame-
ter isolation method that assigns different weights for different datasets Mallya & Lazebnik (2018),
and the replay- paaed method that replays some of the old information to the network together with
the new data Hayes et al.] (0200) Wang et al.] (2021) Prabbu et al. (020). More descriptions of these methods will be provided in next section. One realization of the replay-based method the REMIND network Hayes et al. (2020). Instead of saving all the raw videos, the REMIND net- work compresses and stores the features of the old videos and then replays them to the network for re-training. Feature compression is done by Product Quantization (PQ) Jegou et al. (2010), which clusters the features into K-means centroids and compactly represents them using the centroid in- dices. Although efficient in disk space usage, the original REMIND network replaces the previously stored centroids with those generated by the new data at each re-train iteration, which is not suitable for multiple re-training process. Besides, we found that the original REMIND network has memory overflow issue when the size of dataset is large.
In this paper, proposed MREMIND, which modifies the dictionary update policy of the REMIND network so the previously stored centroids won't 't be replaced and deleted. To optimize memory al- location, we also partitioned the dataset into subsets, which enables the method to work on devices with limited RAM. Additionally, we investigated other state-of-the-art continual learning methods and compared their performance on the cable position inference problem. In summary, our con- tributions in this work are: 1. We applied continual learning techniques to train neural networks for real-time vision-based cable position inference to enable Wireline auto-spooling. 2. We modi- fied the REMIND network with extendable dictionary update policy and an improved memory management. 3. We compared the accuracy and the disk space usage of multiple state-of-the-an continual learning techniques the cable position inference problem.
2 RELATED WORKS
Continual learning solutions aim to achieve the following goals: (1) Continuously train and re-train a network to learn from new data; (2) Keep the performance of the network on the previously seen old data; and (3) Avoid saving all the old data in its raw form to make the solution scalable for disk space usage. Related works in this area include:
The regularization-oon based method. One example is the Elastic Weight Consolidation Method (EWC) Kirkpatrick al. (2017). This method adds regularization term to the loss function to penalize the change of the weights that are sensitive for the old datasets. Here "sensitivity" is quantified using the Fisher information. This method is simple imple- ment. However, setting and tuning the penalty stiffness can be highly problem-dependeee Also, the method was reported be vulnerable to domain shift Wang al. 2021 Aljundi
ct al. (217)).
The the parameter isolation method. This method isolates the parameters and train different datasets on different parameters in the network Mallya & Lazebnik (2018). This method
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indeed can solve the catastrophic forgetting problem because the previously learnt weights won't 't be affected by subsequent training. However, the amount of weights needed increases without bounds as more data becomes available. Therefore, it is not scalable solution.
The replay-based method. This method tackles the catastrophic forgetting problem by playing the old data during retraining. The old data may not in its raw form. It could be compressed, transformed, or reduced REMIND network Hayes et al. (2020) extracted and stored features from the old data and replayed these features for network re-training. The (2021) increased the number of trainable parameters in plastic layer and introduced an auto-encoder to further enhance the inference accuracy. Wu et al. (2019) used generative adversarial network(GAN) to generate images of the old datasets for future replay. Inter- estingly, a recent paper, GDumb Prabhu et al. (2020). shows that by greedily storing the old images and then retraining the network from scratch at each iteration, one get similar performance compared to other existing methods. For these replay-basee based methods, it is important balance the distribution of replay samples for each dataset avoid data bias.
subset of the original data. For example, the
work Wang et al.
METHODS
In this section we propose a new continual learning method for wireline cable position inference task. In section 3.1, will define the objective of the paper. In 3.2 we first give an introduction to current replay-based continual learning method and point out the drawbacks actual deployment. Then we introduce our proposed MREMIND method.
3.1 CONTINUAL. LEARNING FOR WIRELINE AUTOMATION
Upon actual deployment, wireline cable position inference might occur huge performance drop der different environments including lightness, weather and camera position setting. In this paper, our objective is train a neural network that has high prediction accuracy different configurations. Data under different settings will arrive sequentially. In order to avoid catastrophic forgetting issue as well as balance inference accuracy with training time and disk space usage, we apply continual learning technique to tackle this problem.
3.2
MREMIND: MODIFED REMIND NETWORK
3.2. CURRENT REMIND NETWORK
Current REMIND method divides neural network into two parts: (I) feature extractor, also called the encoder or the frozen layer, and (2) decoder, also called the plastic layer Hayes et al. (2000 Wang al. (02T). The weights in the extractor are frozen after the first training, while those in the decoder are continuously trained on the new data iteration after iteration. In this paper, one "iteration" refers to one re-training of the network when a new dataset arrives. could contain mul- tiple training epochs. To avoid saving all the old raw videos, the REMIND network uses the frozen layer to extract the mid-level features from the old frames, then compresses and stores them into dictionaries. When new dataset arrives, the old features are decompressed from the dictionaries and replayed to the network along with the new frames for network re- re-training. In this way, the network learns from the new data and keeps its performance the old ones.
Training Procedure
To explain the training procedure of the REMIND network, denote Enc and Dec as the encoder and decoder of the network, respectively, Feeding input image the network produces mid-level feature (z = Enc(x)) and predicted cable position (f = Dec(Enc(())) Additionally, we use y to denote the ground truth cable position, so that the prediction error is --y. The optimal weights of the network are trained by minimizing the squared prediction error. The training procedure is shown Fig. and is summarized in the steps below:
With all the weights in the encoder and decoder set to be tunable, train the network on all images in the first dataset 1. The trained network is the backbone pre-trained model.
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STEP
Encoder
Decoder
Ourput
Datase11
STEP
Product Quenticaiin
PQ Dictionary
Cuntroich Laden Ercund luth-
Batase2
STEP 3+4
Output
Nanden
PQ
Produc: Quantication
Secermen
Faaltures
Detee::
STEP 5
Product
Juppaace
PO Dictionary
Quantization
Figure 2: Illustration the training procedure old features are decompressed and replayed to the network along with the new features. The weights in the decoder is re- re-trained using the combined features by minimizing the prediction error. The new features then compressed replace the old ones.
original REMIND network. At each iteration,
2. Apply the PQ method to compress all the mid-level features from dataset 1, In this step, we obtain two base dictionaries representing dataset 1.
3. When new dataset arrives, feed the new images into the encoder to extract features.
4. Uniformly and randomly select the compressed old features from the previous dataset, decompress and feed them together with the new features to the plastic layer. Re-train the decoder using these combined features by minimizing the prediction enror.
5. Compress the new features, and replace the stored old features. In this step, we obtain two updated dictionaries representing the new features.
Product Quantization
d/s
d/s
K-maan
Featture(h,, mi,
sub-sactions of clastering cantroids
Cluste can centroid
Feature
Index (inogge))
Index matrix-
Figure 3: Illustration of the Product Quantization (PQ) method. A batch of features are first flattened into a 2D shape. Then the features are divided into sub-sections. K-means clustering is applied on each sub-section, resulting in K centroids and IDs of the centroids that represent the sub-features. 'Tee centroids and the centroid IDs will be used to recover the features during decompression.
The extracted features from the old frames are compressed and stored using the Product Quantization (PQ) method Jegou et al. (010)). The PQ method clusters the features into K-means centroids, and compactly represents the original features with the centroids indices. Each batch of features, with batch size b, number of channels d, and m x n pixels on each channel, can be flatten into 2D matrix of size (b n.d). The PQ method divides the d channels into sub-sections. K-means
Jegou
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clustering is then applied each sub-section, resulting in K numbers of d ddimmennoooon centroids for each sub-section. The features can then be represented using the IDs of the closest centroids (of integer type). After compression, the original features are represented by the clustered centroids and the centroid IDs that tell us which centroid each sub-feature corresponds to. For example, image a batch of original features with the size of b compression, we will get (1) an integer ID dictionary of the size X M X n x log, K x bits, and (2) used (d/s) 32) bits (Fig 3 These two dictionaries
d 32 bits (32 bits type). After PQ
a centroid dictionary of the size to recover the features during decompression. It has been reported that the PQ method can store 50 times more features compared raw images with the same disk space Wang et (202I). We used the library developed by Faiss for implementing the PQ method Johnson
methodJJhnson et al. (2017).
3.2.2 CHALLENGES
The two main drawbacks of current REMIND network Hayes et al. (0200) are: (1) The latent code dictionary update rule only allows one dataset being kept at the same time as old features will be replaced by new ones upon each arrival. So latent code only exist in the dictionary for 1 iteration. For example, when the third dataset arrives, features from the second dataset are gone and cannot be replayed to upate the parameters in the network. Thus, catastrophic forgetting can still occur in this case. (2) Memory overflow issue might occur upon the arrival of new dataset. Current REMIND model fits the complete dataset into the Product Quantization method for feature compression gardless of the size of the dataset. This will not work for computers with limited RAM and can lead memory overflow.
3.2.3 DICTIONARY UPDATE RULE
Our proposed MREMIND method is implemented to tackle the issues that the original REMIND network is only capable of storing two datasets as well as memory overflow. For the dictionary update rule (Step 5) described in previous section, instead of replacing the dictionaries after each iteration, we compress the new features into new dictionaries and store them together with the old ones. An illustration of this modification is shown in Fig
Danazet
Controld Dictonary
ky
Centeratd,
Produc:
Xr
Quantization
Latent Dictinnary
Latent;
yis
ie [1A]
Datasel
Centrold Dictionary
kyt
Conteroid,
Product Quantization
kg lig!
Centeroids
Latents
yt
Latent D'ciorary"
Laterut;
j (Kg 1,4,
Figure 4: Illustration of the modified dictionary update rule. Instead of replacing the old dictionaries with the new ones, we keep both the old and new dictionaries in our modified REMIND network.
Each dataset is compressed its own K-means centroids and indices. During the feature decom- pression process (the process of recovering the features from the centroids and the indices), one needs know where locate the centroids for the different datasets. To do this, we modify the data structure that stores the centroids, such that they are stored in key-value pair structure: For each new dataset, we use the cumulative summation of the number of frames all existing datasets as the key, and the centroids of the new features as the value. As an example, let's say we have 500 images dataset 1, the key-value pair will be {500 centroid, ). Now imagine a second dataset arrives with 1000 images. We compress and obtain new set of centroids for these 1000 images. At this point, we update the centroid dictionary to (500: centroid, 1500 centroid2). } Here, the second key '1500' represents the cumulative summation of all the images (500 + 1000) from the two currently available datasets. On the other hand, the centroid indices and the ground truth cable positions are the
stored as: (i: (M.. :: (Mj.yy),, [1,500), j [501, 1500). During decompression,
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above data structure reconstruct the features. For example, feature with index 800 can be found to fall into the second centroid dictionary by comparing its index (800) to the keys (500 and 1500): 500 < 800 < 1500. We note that the modified dictionary update rule indeed requires larger storage space because we save all the compressed features of the old datasets. However, because the floating-type features are converted into integer-type indices, the storage space increases at much lower rate compared to other methods that store the raw images directly.
3.2.4 MEMORY OVERFLOW
For the memory overflow issue, current REMIND network applies the PQ method on the entire dataset regardless of the number of images in that dataset. For devices with limited RAM, this could lead to memory overflow issue. To tackle this issue, optimized the memory allocation policy by applying dataset partition. Denoting D and as the size of the dataset and the designed maximum capacity of one subset, respectively. We divide dataset into D/p subsets and applied the PQ method on each of them individually. For example, if D = 600, p 256, then the centroids will be stored as {256: centroid,,512 centroidy, 600 centroid3 }. The first key 256 represents the first partition with size p 256, the second key 512 represents the sum of + p. The last key 600 represents the final centroid matrix with - p - p = 88 images. One can adjust the value p based on the hardware limits. It enables the REMIND network to work on devices with limited RAM.
Dataset
k
A/p
mod
Figure 5: Illustration of dataset partition for enabling the REMIND network on RAM limited de- vices.
4 IMPLEMENTATION
4.1 DEVICES, DATASETS & DATA PREPROCESSING
We implemented all codes in Python 3.7 and PyTorch 1.5.1. All models are trained on virtual ma- chine Google Cloud Platform using one Tesla K80 GPU (12 GB) with 16 GB RAM. The datasets in this paper were collected from Wireline trucks in Texas, USA. We used 3 datasets collected under different conditions to compare the continual learning solutions. Dataset contained videos that were captured in daytime under sunny weather. Dataset 2 contained videos taken in nighttime in rains. Dataset contained videos captured by different camera with a few different camera set- tings: Dataset & 2 had reflection protection that the reflection from the window of the operators inside the cabin did not appear on the frames. For dataset 3, however, no reflection protection was up. Besides, dataset 3 was taken under special condition where the spooling arm never moved to near the left flange of the drum, causing different distribution of the cable position compared the other datasets. For dataset & the cable position values ranged between [0, I]. but those in dataset were in between [0, 0.6). The ground truth cable position was calculated by another service during the Wireline operation. Fig 6hows example frames from each of the datasets. For training the REMIND network, we applied dataset as our initial dataset to train the weights in both the frozen layer and the plastic layer. Then datasets 2 and 3 arrived and were used subsequently to re-train the plastic layer. All 3 videos were taken with 4K resolutions (3840 2180) and frame rate 30.
We cropped the frames to show only the cable the drum without the irrelevant background. During the training process, we resized the images to (1200,350 before feeding them into the network. After frame sampling, datasets 1, 2 and contained 4838, 2007 and 3102 images, spectively. In terms of disk space, they used 4 GB, 1.5 GB and 5.6 GB, respectively. For all the methods being tested in this paper, applied the ratio of 80%: 10% 10% as the percentage of the

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wainiing/alidatioonnestnnng sets. The evaluation metrics applied to quantify the performance of network was the mean- recan-s error (L = (9 y)?).. The learning rate was le - 4.
Figure 6: Example frames from datasets (left), 2 (middle) and 3 (right). The environment (lighting conditions, weather, camera settings, frame distributions) under which the 3 datasets were taken were different. We used these 3 datasets to compare the performances of different continual learning methods.
4.2
THE BACKBONE NETWORK
We applied VGG19 Simonyan & Zisserman (2015) as the backbone network with the fully con- nected layers customized to output the Wireline cable position (a scalar).
4.3 BRUTE FORCE SOLUTION
In order to prove the existence of catastrophic forgetting in our datasets, we implemented the brute force solution (abbreviated as BF Sol) first, which followed simple rule of 'train one drop one', For this method, every time a new dataset arrived, the network was re-trained only on the new dataset without access any old data.
4.4 STORE-ALL- STORE-ALL-TRAIN-AA TRAIN-ALL ALL
For comparison purpose, we also implemented another naïve solution where all the old datasets were saved its raw form on the disk -Slore-AllTrrain-All. abbreviated as SATA). The combined training dataset increased without bounds in this method. At each iteration, the model was re-trained on this ever-increasing dataset, with the previously trained model as its initial condition.
4.5
THE GDUMB GDUMB PLUS METHOD
For the GDumb method Prabhu et al. 2020), each iteration, the images from all datasets were stored 'greedily' in the disk and the network was re- re-trained from scratch using the selected images. The GDumb method was shown reach similar level of performance with the simplest configu- ration compared to all other methods. In addition to the original GDumb method, we also created an improved version and named it the GDumb Plus method. This new method greedily stored equal amount of images from each datasets (regardless of how many images the original dataset contained), and re-trained the network using the previously trained model as the initial condition (instead of training it from scratch).
4.6 THE ELASTIC WEIGHT CONSOLIDATION METHOО
For comparison purpose, we also implemented the Elastic Weight Consolidation (EWC) method Kirkpatrick et al. (2017). During re- re-training, small subset of the previous dataset was forwarded into the model for a second time to update the Fisher Information matrix F. A Fisher information dependent regularization term was added to the lost function for each weight. The regularization penalized the changes of those weights that were important for predicting the old data:
Laquared emrrr Σ Σ (0:- Oprev.))
(1)
Here Laquared enror is the original squared prediction enror loss term, 2. is the penalty stiffness, F are the diagonal elements the Fisher information matrix, and 6, are the tunable weights in the network.

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4.7 MREMIND NETWORK
Inspired by the work of ACAE-REMIND Wang et al. 2022)) we tested separating the encoder and the decoder at different convolution layers when designing the modified REMIND network (abbreviated as MREMIND). For the first test, we used layer to 29 of the VGG19 net as the frozen layer (encoder). This generated features of shape 721,75,512) We named this model MREMIND- 29. In the second test, we used layers 31 as the encoder. The features at layer 31 have the same shape as those at layer 29, We named this setting MREMIND-31.
EXPERIMENT RESULTS
We first trained the VGG19 network on dataset with a batch size of 8 for 10 epochs to get the initial model. In this first training, the weights in both the encoder and the decoder were be tainable. Table[]ssowss the prediction error when we applied this initial model (trained only on dataset 1) on all the 3 datasets. The table shows that model trained on only dataset does perform well on the others. Therefore, continual learning solutions need to be applied.
Method
Dataset
Dataset 2
Dataset 3
Base Model
1.02e-05
1.01e-03
3.02e-03
Table 1: Testing error on all the 3 datasets using the initial model that was trained only on dataset
1. This initial model did not perform well on datasets 2 and 3. Therefore, continual learning tech- niques need to be applied. Otherwise, the cable position inference wouldn't be accurate when operational conditions change.
Next, we introduced dataset 2 into the continual training pipeline. We tested all the methods de- scribed in previous section. The number of data subsets used for MREMIND-29 and MREMIND- 31 were 256 and 320, respectively. Size of code-book (ie., the number of sub-sections we divided the feature channels into for the PQ method) for MREMIND-22 29 and mREMIND-33 were 32 and 16, respectively. Comparisons the testing errors and disk space usage were shown in Table and
7
Method
Dataset
Datase12
DUS
BF Sol
1.68e-033
3.34e-06
1500
GDumb Plus
4.58e-06
2.47e-06
3600
GDumb
1.19c405
2.12c-05
3600"
SATA
3.58e-06
3.79e-06
5400
Indio
EWC-10000
9.70c-04
2.78c-06
1808
9.19e-05
3.47e-06
1808
MREMIND-3I
7.89c-07
2.21e-06
1663
MREMIND-22
8.09e-07
2.10e-06
1809
Disk space usage (DUS) in Megabyte (MB) does not include trained models Approximation usage
Ddhvd3
B-50X
GOuYE
FREMILD
Table 2:
Mean-squared prediction error, in units of squared meter, datasets I & 2 after the network was trained for iterations before dataset 3 was introduced.
Figure 7: the network was trained for 2 iterations.
log error plot datasets & after
was shown in Table that the testing error on dataset 1 using the initial model was 1.02e-05 m At the end of the 2nd iteration, however, one used the brute force method, the testing error on dataset 1 increased 168 times to 1.68e-03 m² This confirms that catastrophic forgetting occurred our datasets and continual learning solutions are required solve the problem. For the SATA Sitore-All-Train-Al
method, the accuracy was good, but it required much longer training time and a significantly larger disk space usage. This is because all old frames were stored their raw form. For the GDumb method, the original setting experienced no catastrophic forgetting on dataset 1. However, its performance on dataset 2 was not satisfying. The reason was that dataset contained less images than dataset 1. When uniformly sampled among both datasets, the network trained on more images from dataset than from dataset 2. The GDumb Plus method solved this problem because it sampled equal amount of images from each dataset. For the EWC method,
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because of the large domain shift from dataset to (see Fig(6), the method did not perform well and suffered large accuracy drop on dataset This is in agreement with other studies Aljundi et al.) (2117). Our modified REMIND network (MREMIND-29) MREMIND-31) achieved the overall best performance compared to the other methods in terms of accuracy and disk space usage.
Finally, introduced dataset 3 into the re-training process. The number of data subsets and the code-book configurations for mREMIND-229 and mREMIND-31 were kept the same. The testing error and disk usage are shown in Table 3 and Fig
Method
Dataset
Dataset
Dataset 3
DUS
BBF Snl
4.93e-03
8.23e-03
1.82e-06
5600
GDumb Plus
5.79e-06
3.70e-06
1.92--66
5550
GDumb
05
2.622
1.76e
SATA
6.41e-06
3,82c-06
2.57c-06
11000
EWC- 10000
9.80e-04
2.00e-04
2.05e-06
6062
10CC-0000000
04
3.10c-04
192c-00
0062
MREMIND-31
L44c-06
6.21e-06
6.37c-07
5823
MREMIND-29
9,49c-07
2.55c-06
2.32c-06
6024
Disk space MB does not include trained models
Approximation usage
RP:M BUNG BIND 500000 1000mk
Table 3: Meun-squarde prediction error, in units of squared meter, on datasets & after the network was trained 3 iterations. Disk space usage for each method is shown in the last column.
Figure 8: llog error plot on datasets 1, 2 & 3 after the network was trained for 3 itera- tions.
For brutal force solution, catastrophic forgetting became even worse. The testing error on dataset increased from lc-5 m² in Table. [1] to 1.68e-03 m² in Table then to 4.93e-03 m² in Table 3 (~500 times larger compared to iteration method that trained the model from scratch at each iteration had larger testing error compared to the GDumb Plus method. As for the EWC method, tuning of the penalty stiffness was time consuming, yet the final performance was not satisfying. Our modified mREMIND-29 still had the lowest error in datasets 1 & 2, and best overall performance among all the 8 methods. terms of disk space usage (DUS), as more datasets arrive, the GDumb based and the brute force methods had relatively small space usages compared to SATA. Both the EWC and the modified REMIND methods stored higher-level representation of datasets. They were efficient in disk space usage.
and <33 times larger compared iteration 2). The GDumb
6 CONCLUSIONS
Computer vision has the potential to help automate the Wireline spooling process. However, to develop robust solution and to utilize the increasing and incrementally available field data, continual learning techniques are required. In this paper, we demonstrated that re-training network only the newly available videos training on all historical videos is not a scalable nor a practical solution. We modified the replay- based REMIND network, and successfully applied it on the cable position inference problem. To compare its performance, we implemented a few other continual learning solutions, including the GDumb method, the GDumb Plus method, and the EWC method. The modified REMIND network gave the best performance among all these tested solutions. However, the GDumb Plus method is still an attractive solution because easy to implement with an acceptable performance.
lead to catastrophic forgetting. On the other hand, saving and
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Reviews:
[{"review_id": "dkhgg0iD32O", "reviewer": "Reviewer_kEWr", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strength\n- The problem itself seems to be a good testbed for continual learning.\n\nWeakness\n1. The composition and details of the paper are quite unusual. For example, \n- Most portion of the introduction section is dealing with the basic concept of continual learning instead of the issues that must be raised by the authors.\n- A sentence or paragraph indented with bullet points should include important factors or summarization in a simplified manner. If the whole paragraph is to be indented in the 'related works' section, using bullet points would be inappropriate.\n- I am pretty sure that the separation of subsections in section 4 is significantly weird. Backbone network can be mentioned in a single line in the implementation subsection and the following subsections are explaining too much about the methods that are not proposed by the authors of this paper.\n- As MREMIND is built upon the work of REMIND, I understand that explanation of this method is necessary. But spending almost 2 pages out of 9 pages for the base method means that we cannot expect something new within those pages.\n- The key points of Table 2 & 3 seem to be identical. Leaving only Table 3 would have been sufficient.\n\nAfter all these factors, it has been hard to 'not' recognize that the authors had struggled to fill the paper with somewhat redundant information. I understand that offering prior information is as important as novel methods but honestly, this is too much.\n\n2. In 3.2.2, when the third dataset arrives, shouldn't it be the first dataset that is forgotten? This sentence confuses my understanding. By the way, as a matter of fact, If I have understood right, REMIND does not abandon the old 'dataset' but the old 'sample' when the memory has reached its maximum capacity.  I want to hear a more specific explanation of this point from the authors.\n\n3. The authors are saying that the memory overflow issue can be 'tackled' with the proposed method, but reducing the size of the data by dividing it into smaller subsets seems to simply 'delay' the overflow. If this is right, the contribution of this part (which is one of the two main contributions of this paper) is significantly minor. \n\n4. If the authors are to claim that their method works better than any other previous methods, it is usually recommended to conduct comparison experiments on datasets and settings from them. It is hard to accept the experimental results performed on the author's custom datasets. Besides, putting aside that the evaluation metric is not well explained, prediction error seems to be seriously minor in all cases and the results must have fluctuated a lot. More justification of these results seems to be necessary.\n\n5. If MREMIND is a method improving REMIND network, I wonder why the result of REMIND is not included in those two experiments.", "summary_of_the_paper": "Original REMIND network compresses and stores features from old videos and replays them on the network at the time of retraining. Previously stored centroids are replaced with new ones and this is not suitable for a multiple re-training process. The authors proposed a new memory update rule to overcome catastrophic forgetting more efficiently and a strategy of resolving limited resource issues", "summary_of_the_review": "The paper is quite below the standard of ICLR in all aspects of novelty, experiment, and writing. ", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "1: The main claims of the paper are incorrect or not at all supported by theory or empirical results.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "WRsXV0jf-Oy", "reviewer": "Reviewer_cXMu", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "* The paper is well written and easy to follow. The technical details are all clearly presented and technically sound. \n* The contribution of this paper is very limited. The proposed method is an incremental work of REMIND network. Although the proposed two modifications may be effective, they are mainly ad-hoc tricks and lack enough insights and novelty. \n* The proposed method is specifically designed for wireline cable position inference. Its impact to a larger scope may be limited. I suggest the authors to evaluate their approach to more diverse applications and compare against state-of-the-art continual learning methods on public benchmark datasets.", "summary_of_the_paper": "This paper proposes a modified version of REMIND network for the specific problem of wireline cable position inference. It improves the original REMIND network by using an extendable dictionary update policy and optimizing the memory allocation policy through dataset partition, which addresses the catastrophic forgetting and memory overflow issue.", "summary_of_the_review": "I think the contribution and novelty of the proposed method is somewhat limited and fails match the standard of ICLR.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "MZ5iW0qB52N", "reviewer": "Reviewer_jrD1", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Strength:\n- Well-written and easy-to-read paper.\n- The motivation is clear where it has motivated the problem of forgetting and scalability.\n- Experiment section contains figures and tables that show a proper measure of forgetting and memory disk usage.\nWeakness:\n- The challenge introduced in this paper is to overcome the current drawbacks of the REMIND network (Hayes et al. 2020). The latent code dictionary update and the memory overflow. However, there is no table or figure that shows how the proposed method performs against the REMIND network.\n- The dataset used in this paper were collected from Wireline trucks in Texas, where the authors used 3 datasets collected under different conditions to compare the continual learning solutions. However, what happens with other datasets is unknown. So, I think the proposed approach should be evaluated with multiple datasets from different sources.\n- Why MREMIND-29 performs better than MERIND-31 even though MERIND-31 has more samples (256 vs 320). Table 2, Table 3.\n- How do you balance the distribution of replay samples to avoid data bias.\n", "summary_of_the_paper": "In this paper, the authors proposed a method called the modified-REMIND (MREMIND) network. It is a replay-based continual learning method with a longer memory to historical data and no memory overflow issues. Information of old data is kept for multiple iterations using a new dictionary update rule. The modified-REMIND is applied to the cable position inference problem. experiment section shows that the modified-REMIND network gave the best performance among all other tested solutions.", "summary_of_the_review": "Overall interesting paper. It explained the proposed idea very well. However, the experiment section is incomplete. I think the authors need to show how the proposed method perform using multiple datasets (i.e different than the Wireline trucks) such as (ImageNet, CORe50). Moreover, they need to compare against the REMIND network (Hayes et al. 2020), since the main contribution of this paper is to overcome the REMIND drawbacks.", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "IcaFnPsX56n", "reviewer": "Reviewer_MCzH", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "### Weaknesses\n\n#### Major\n- This application is too narrow. The modifications are not coupled with this wireline auto-spooling task and can be applied on many other continual learning tasks (e.g. ImageNet ILSVRC-2012, CORe50, CLEVR, TDIUC tested in REMIND paper). But I am not sure if the proposed method works on those tasks. Commonly used benchmarks used to evaluate continual learning methods should be tested.\n\n#### Minor\n- Fig.2: Why do you use concat in \"STEP 3+4\" and how it works?\n- Sec. 4.3: The network is re-trained from scratch? Better state more clear as in Sec. 4.4 and 4.5.\n- Tab. 2: no comparison with REMIND?", "summary_of_the_paper": "This submission modified REMIND with different dictionary update policy to keep track of previously stored centroids and partition the datasets to subsets to optimize memory usage. The proposed method has been verified on wireline auto-spooling (predict cable position). Several other state-of-the-art continual learning techniques have been tested on this task too.", "summary_of_the_review": "Please refer to the Weaknesses", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "2: Several of the paper’s claims are incorrect or not well-supported.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This submission receives four negative reviews. The raised issues include paper organizations, presentation clarity, more experimental evaluations, the trade-off between technical contribution and application configuration, and the potential impact on more general visual recognition scenarios. In the rebuttal and discussion phases, the authors do not make any response to these reviews. Overall, the AC agrees with four reviewers that the current submission does not reach the publication bar. The authors are suggested to improve the current submission based on the reviews to make further improvements.
Decision: Reject