Continual Learning of Neural Networks for Realtime Wireline Cable Position Inference

1 INTRODUCTION . With the emergence of AI-powered computer vision techniques in recent years , more and more vision-based applications are 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 ( Fig.1 ) . This inferred cable position will then be passed to a 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-distribution frames , i.e. , frames that look significantly different from those in the training set , we kept collecting new training videos to 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 et al . ( 2021a ) . This , however , is not a scalable solution . Disk space required to hold all the past videos increases without bound over time . Also , the time to re-train a 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 is called catastrophic forgetting in the literature Kirkpatrick et 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 . There exist three types of continual learning techniques Liu et al . ( 2020 ) : the regularization-based method that penalizes large weight changes during re-training Kirkpatrick et al . ( 2017 ) , the parameter isolation method that assigns different weights for different datasets Mallya & Lazebnik ( 2018 ) , and the replay-based method that replays some of the old information to the network together with the new data Hayes et al . ( 2020 ) Wang et al . ( 2021 ) Prabhu et al . ( 2020 ) . More descriptions of these methods will be provided in next section . One realization of the replay-based method is the REMIND network Hayes et al . ( 2020 ) . Instead of saving all the raw videos , the REMIND network 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 indices . 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 , we proposed MREMIND , which modifies the dictionary update policy of the REMIND network so the previously stored centroids won ’ t be replaced and deleted . To optimize memory allocation , 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 contributions 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 modified the REMIND network with an extendable dictionary update policy and an improved memory management . 3 . We compared the accuracy and the disk space usage of multiple state-of-the-art continual learning techniques on 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-based method . One example is the Elastic Weight Consolidation Method ( EWC ) Kirkpatrick et al . ( 2017 ) . This method adds a 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 to implement . However , setting and tuning the penalty stiffness can be highly problem-dependent . Also , the method was reported to be vulnerable to domain shift Wang et al . ( 2021 ) Aljundi et al . ( 2017 ) . • 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 indeed can solve the catastrophic forgetting problem because the previously learnt weights won ’ t be affected by subsequent training . However , the amount of weights needed increases without bounds as more data becomes available . Therefore , it is not a scalable solution . • The replay-based method . This method tackles the catastrophic forgetting problem by replaying the old data during retraining . The old data may not in its raw form . It could be compressed , transformed , or reduced to a subset of the original data . For example , the REMIND network Hayes et al . ( 2020 ) extracted and stored features from the old data and replayed these features for network re-training . The ACAE-REMIND work Wang et al . ( 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 . Interestingly , 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 can get similar performance compared to other existing methods . For these replay-based methods , it is important to balance the distribution of replay samples for each dataset to avoid data bias . 3 METHODS . In this section we propose a new continual learning method for wireline cable position inference task . In section 3.1 , we 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 in 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 under different environments including lightness , weather and camera position setting . In this paper , our objective is to train a neural network that has high prediction accuracy in 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 . 3.2.1 CURRENT REMIND NETWORK . Current REMIND method divides a neural network into two parts : ( 1 ) a feature extractor , also called the encoder or the frozen layer , and ( 2 ) a decoder , also called the plastic layer Hayes et al . ( 2020 ) Wang et al . ( 2021 ) . 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 . It could contain multiple 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 a new dataset arrives , the old features are decompressed from the dictionaries and replayed to the network along with the new frames for network re-training . In this way , the network learns from the new data and keeps its performance on 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 an input image x to the network produces a mid-level feature ( z = Enc ( x ) ) and a predicted cable position ( ŷ = Dec ( Enc ( x ) ) ) . Additionally , we use y to denote the ground truth cable position , so that the prediction error is ŷ− y . The optimal weights of the network are trained by minimizing the squared prediction error . The training procedure is shown in Fig . 2 and is summarized in the steps below : 1 . 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 . 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 a 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 error . 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 The extracted features from the old frames are compressed and stored using the Product Quantization ( PQ ) method Jegou et al . ( 2010 ) . 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× n pixels on each channel , can be flatten into a 2D matrix of size ( b×m× n , d ) . The PQ method divides the d channels into s sub-sections . K-means clustering is then applied on each sub-section , resulting in K numbers of d/s-dimension 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×m× n× d × 32 bits ( 32 bits type ) . After PQ compression , we will get ( 1 ) an integer ID dictionary of the size b×m×n× log2 K × s bits , and ( 2 ) a centroid dictionary of the size K × s× ( d/s ) × 32 ) bits ( Fig.3 ) . These two dictionaries are used to recover the features during decompression . It has been reported that the PQ method can store 50 times more features compared to raw images with the same disk space Wang et al . ( 2021 ) . We used the library developed by Faiss for implementing the PQ method Johnson et al . ( 2017 ) .

Summary: 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.