Learn to Know Unknowns: A Bionic Memory Network for Unsupervised Anomaly Detection
Authors: Jiahao Li
Yiqiang Chen
Yunbing Xing
Venue: Submitted to ICLR 2023 (2023)
Keywords: Unsupervised learning
Anomaly detection
Memory bank

Abstract: Is generalization always beneficial? Over-strong generalization induces the model insensitive to anomalies. Unsupervised anomaly detection requires only unlabeled non-anomalous data to learn and generalize normal patterns, which results in a modest reconstruction error when reconstructing normal instances and a significant reconstruction error when reconstructing anomalies. However, over-strong generalization leads to the indistinguishable reconstruction error of normal instances and anomalies, which means that the model well reconstructs the unknown anomalies, resulting in unnoticeable reconstruction error. Inspired by the cascade structure of the hippocampus and cortex in human brain memory, we proposed a re-representation memory network called Random Forgetting Twin Memory (RFTM) to decompose the latent space and introduce a configurable reintegration mechanism to suppress overgeneralization. RFTM shows striking brain-like memory characteristics, which enables the model to know what it does not know. RFTM has the convenience of a single line of code boosting at the model level without adding any additional extra loss terms at the loss function level. RFTM-based models have achieved state-of-the-art experimental results on different public benchmarks.

Under review as a conference paper at ICLR 2023
LEARN TO KNOW UNKNOWNS: A BIONIC MEMORY NETWORK FOR UNSUPERVISED ANOMALY DETECTION
Anonymous authors Paper under double-blind review
ABSTRACT
Is generalization always beneficial? Over-strong generalization induces the model insensitive to anomalies. Unsupervised anomaly detection requires only unlabeled non-anomalous data to learn and generalize normal patterns, which results in a modest reconstruction error when reconstructing normal instances and a signifi- cant reconstruction error when reconstructing anomalies. However, over-strong generalization leads to the indistinguishable reconstruction error of normal in- stances and anomalies, which means that the model well reconstructs the unknown anomalies, resulting in unnoticeable reconstruction error. Inspired by the cascade structure of the hippocampus and cortex human brain memory, we proposed re-representation memory network called Random Forgetting Twin Memory (RFTM) to decompose the latent space and introduce a configurable reintegra- tion mechanism to suppress overgeneralization. RFTM shows striking brain-like memory characteristics, which enables the model to know what it does not know. RFTM has the convenience of single line of code boosting at the model level without adding any additional extra loss terms at the loss function level. RFTM- based models have achieved state-off titee-of-theaare art experimental results on different pub- lic benchmarks.
INTRODUCTION
Anomaly detection (AD) refers to the identification of deviant samples based on known rules, ex- pectations, or distributions. has been extensively studied in many fields requiring attention to rare events, such as hSalthcare al. 2021 Arabahmadi al. 2022), financial fraud(Hila)
al., 2021 Sanober et al. 2021). and defect detection(Fu et al. [022] Cui et al. 2022). The Machine learnin-bbssed approach gradually adopted to deal with AD tasks with more and more impres- sive results. Supervised and unsupervised methods are two branches of machine learning-based AD(Omar et al., 2013). The former requires labeled data for model training, while the latter does not. Due to the occurrence of anomalies being small probability event, the supervised approach is restricted by the extremely imbalanced samples, which means there are not sufficient labeled anomaly samples for the model to learn. In contrast, the unsupervised approach does require anomaly samples. Unsupervised anomaly detection (UAD) only needs to fit the unlabeled normal samples to learn the normal patterns, so it can identify the anomaly when the instance deviates from the normal patterns. Therefore, UAD has attracted extensive research interest due to its label-free characteristic. Reconstruction-basses frameworks have achieved excellent performance in image(Li) et al. 2021, Schneider et al. 2022), video(Deepak et al. 2021 Chang et al., 2022). and time se- ries( al. 2021] Kieu Kieu et al. 2022) AD tasks recent years classic UAD paradigm. But at the same time, some studies(Gong et al. 2019 Park et al. 2020) show that reconstruction-based AD has an overgeneralization problem (OGP).
Generalization is not always good thing. Over-strong generalization in UAD leads to the failure of the reconstruction model to detect anomalies. The OGP was first observed in Figure of Zong ct al. (2018). Gong al. (2019) first briefly demonstrated and reported this problem in a one-class classification (OC) case. We give the first formal description of OGP in a broad sense the fol- lowing and demonstrate it in scenario that is not limited to OC. The framework of reconstruction- based AD is shown in Figure [b] The model is first trained by minimizing the reconstruction error of normal samples in the training phase, and then the trained model measures the anomaly degree

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Smor
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Reconstruted Normal
Error
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Error
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(b) The framework of AD.
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138 256 512 lidden
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(a) The visual illustration of OGP in reconstruction.
(c) The collapse of IIA.
Figure 1: OGP is demonstrated in the inference stage by using reconstruction models with the differ- ent number of bottleneck neurons trained on MNIST. The increasing number of bottleneck neurons from 2 to 512 stands for the increasing complexity of the model. The images from MNIST are taken normal, and the input from MaSSI is taken as anomalous. It can be seen that the reconstruction error gently decreases with the increase of model complexity for the normal class handwritten digit 5. The reconstruction error decreases rapidly with the increase of model complex- ity for the trousers image from Messhon-MMIIST The model trained on MNIST could reconstruct the data from Fashion-MNIST well when the complexity increased to certain extent. The deviation decreases with the increase of model complexity, which leads to the anomalies undetectable.
of unseen instances by the reconstruction error in the inference phase. The reconstruction error di- rectly reflects the deviation between the unknown samples and the learned normal patterns. The larger the reconstruction егrог is, the higher the corresponding anomaly degree is. However, this framework has an implicit ideal assumption (IIA) that the model trained on the normal samples can- not reconstruct the anomaly samples well. The model generalization is enhanced with the increase of model complexity which leads to the input data indiscriminately being reconstructed too well as shown in Figure Ta The input data containing both normal and anomaly samples will output the reconstruction error with an indistinguishable difference in the inference phase due to the excessive generalization of the model, resulting in the collapse of IIA as shown in Figure Ic
The OGP is particularly prominent and challenging in unsupervised semantic anomaly detection (USAD). The example shown in Figure la is routine setting, that is, using dataset as normal and the other as anomalous. Different from the routine setting, the motivation proposed by/Ahmed & Courville (2020) emphasizes that anomalies should be the semantic level, which recommends that hold out one class as anomalous and the rest classes as normal within the same dataset. The main challenges of USAD are as follows. First, the labels of normal classes are not accessible, which makes it impossible to train the classifier with supervised labels to obtain tight bounds that can describe normal patterns. Second, the number of classes of the normal pattern is unknown, which means that the OGP is more likely to occur in this multimodal case because more unlabeled classes of data lead better generalization. Third, the anomalies are at the semantic level and invisible, which means that the OGP can easily occur if the bounds on the normal pattern are not tight enough. Finally, the OGP can be alleviated by reducing the complexity of the reconstruction model from the perspective of its origin. However, there may be model with optimal complexity that both well generalize normal samples and fail to overgeneralize anomaly samples to satisfy IIA. Besides, multiple training searches computationally intolerable even if the optimal solution exists.
In recent years, a part of the work has started to pay attention and try to solve the above prob- lems. MemAE(Gong al. 2019)) proposed a memory module that stores certain amount of prototype of latent information and re-represents the latent space with a sparse attention mecha- nism. MNAD(Park al. 2020) introduces transformation matrix to remap the latent space, using
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KMeaas-like approach to allow the memory module to be updated independently. (H eet al., 2021) embeds the memory module(Gong et al., 2019) in more than one location of the net- work. Unfortunately, all the above works introduce one more penalty terms to induce the model to be functional. The adjustment of the penalty term coefficients largely affects the effectiveness of the model. Besides, most of the experiments were not conducted based on the USAD setup.
We proposed a novel re-representation neural network called Random Forgetting Twin Memory (RFTM) and introduced configurable reintegration mechanism solve the OGP in USAD. Specif- ically, RFTM uses limited prototypes to record and re-represent the latent representation, which makes suppression of overgeneralization feasible. Beyond that, RFTM can be trained end-to-ent without introducing any additional penalty terms on top of the original task loss function. It is im- portant to note that the RFTM is a well-packaged module, which allows a single line of code in project deployment to achieve performance gains for OGP without changing the original model. We conducted experiments and achieved sate-of-the-aan performance on different kinds of public benchmarks. The main contributions of this paper are follows:
RFTM networks: A novel memory network for reconstruction basedd AD with a bionic cascade structure and a random forgetting mechanism is proposed to suppress overgeneral- ization.
Plug-and-Play: RFTM is plug-and-play network that requires only one line of code to be added and does not require any modification of the original loss in the original project to obtain improved results for OGP.
Bionic properties: We demonstrated the process of RFTM training, which is surprisingly similar to the process of memory formation in the human brain. We experimentally demon- structures.
strated the human brain's 's similarity between RFTM and hippocampus-corte
Effectiveness: We performed extensive experiments on various datasets and achieved state- of-the-at art performance to demonstrate the effectiveness of the RFTM.
RELATED WORK
Unsupervised Anomaly Detection. Reconstruction- based methods learn model that is opti- mized to well-reconstruct normal data instances, thereby aiming to detect anomalies by failing to accurately reconstruct them under the learned mouel et al., 2021). ). Autoencoders are naturally chosen as the model framework for reconstruction-bane based (D et al., [202]). The reconstruction
error is usually considered to be small on the normal training set and large on the anomaly test
set(Hasan et al. 2016 Zhao et al. 2017 Zong et al. 2018).
Memory Bank, The OGP has progressed some extent in the field of video surveillance since the memory module was proposed by Gong et al. (2019). At the model structure level, the memory module provides an additional trainable matrix between the encoder and decoder for storing proto- types and introduces an attention mechanism to reintegrate prototypes. At the loss function level, the memory module suppresses overgeneralization by adding regular terms that encourage attention weights sparsity. The success of the memory module drew the attention of scholars to improve it. MNAD(Park et al. reconstets latent information from the clustering perspective of memory read/write updates. At the model structure level, MNAD draws rithm. Each row of the trainable matrix is considered as a clustering center, which is momentum updated by the neighbor information. At the loss function level, separation loss and compact loss are introduced to control the inter-class and intra-class distance centered on the prototype. Trust- MAE[Tan al. 202I) based on MNAD further introduces the trust region suppress overgener- alization. Six regular terms are introduced to jointly constrain the loss function. DAAD(Hou et al.) 2021) embeds memory module(Gonn et al. 2019) in all concatenate layers of Unet. The adversarial and alignment loss regular terms are introduce to train. The main improvements of the memory module are as follows. First, the regular term is designed to work with the memory module to sup- press overgeneralization. Second, the memory module is updated autonomously in non-gradient style similar to clustering. Third, the memory module is embedded in different positions of the model. However, the solution new problems of adjusting the coefficients of the regular terms, the unknown prior capacity setup of the memory module, and the choice of the embedding position of the model remain explored.
ideas from the K-Menas algo-
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Random Forgetting
Twin Memory (RFTM)
Original
Re-representation Latent Vector
Latent Vector
Encoder
Decoder
Input
Output
(a) The RFTM-based reconstruction framework.
Hippocampus
Cortex
Latent Vector
Cosine Similarity
Forget Gates
Hippocampus Memory
Memory Association
Memory Connector
Cosine Forget Similarity Gales
Cortex Memory
Memory Association representation
Re-
DC checepdirt
checkpeint
[k] chadipclet 2
DNI
checspornt
KI checkpoint
KI checkpoist
[C] Checipoirt
Top
Thredi
SoftMax
Configurable Sparsification
Configurable Sparsification
(b) The details of the RFTM.
Figure Overview of RFTM-AE.
METHOD
3.1 OVERVIEW
The reconstruction framework equipped with RFTM is shown in Figure 2a The model consists of three parts: encoder, decoder, and RFTM. During the inference phase, the encoder fex RR" R firstly makes nonlinear transform to the input data R and represents them in the latent space R4 with parameter 0g. Hence, the latent representation can written by h Sog (a) E RR.. Then the RFTM Eæ,, R + R re-represent the latent representation to get h Ee (h). After that, the decoder see, g- R receives re- re-representation h from the RFTM and, opposed to the encoder, reconstructs to the original input data with parameter dD as closely as possible. In the end, the anomaly intensity of the unseen input sample is measured by reconstruction enror. Equation (T) gives the formal description the inference phase.
K || (Eo., (for (x))) xll
During the training phase, the model minimizes reconstruction error and uses BPmRulllhart al. 1986) algorithm to update all parameters including RFTM. At the same time, the RFTM learned representative patterns as prototype 0,. which is demonstrated in Section 3.2 The optimization problem is described by Equation (
min 0n.0 On
11sep (Epx (Jeg (x))) x11
Without loss of generality, it should be noted that the proposed RFTM network is suitable for all models that follow the autoencoder paradigm and works for the most shape of data. That is to say, and so mentioned above be neural network operators such as CNi(Krizyevsky et al. 2012). Zarebaa et al., 2014], (iif Welling 2016) and their variations, and the space R to which the input data belongs be multidimensional time series, images videos, etc.

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3.2
RANDOM FORGETTING TWIN MEMORY
3.2. BASIC UNIT
Memory. Consider a trainable matrix M E RNNE', where N is the memory capacity and L' is the flattened shape of Let m, be the i-th row of the matrix M denoting the i-th prototype checkpoint of the memory.
Cosine Similarity. The similarity & hm, h [ I [-1, between the latent representation h and any of the prototype checkpoint m, can be calculated. Let vector = (81, 8N) R be the association weights. Let S(h, M), where is the operator of the vector s with respect to h and M.
Random Forgetting, The forget gates part is a binary vector Pu E {0, 1) controlled by the forgetting rate E [0, 1). where the vN elements randomly and non-repeatedly selected to be set to one and the rest to zero.
Configurable Sparsification. The configurable sparsification consists of three functions, the Top- K function, the threshold function, and the softmax function. The Top-K function (x sets the index of the first & largest component of each component of x to one and the rest zero. The threshold function Ta (x) sets the index of all components of æ greater than a to be one and the rest be zero. The (2r) is softmax function. Note that function is compression function that removes the component of the input vector or matrix that is zero or an integer row that is all zero and outputs vector or matrix of the original order consisting of the remaining components or rows. In particular, we always want to use similar memory checkpoints to re-represent, so let = 0.
Memory Association. The output of the basic unit is obtained by linear summing of the output vector of the above pipeline and the prototype memory.
howt (TT @ S(hm, (()))) @ M)
Where e = (1)2 is the broadcast vector whose components are one, which used to add a mask to the forgotten prototype memory.
3.2.2 TWIN MEMORY
Memory Connector.
The memory connector consists of BN and LeakyReLU. For convenience, the BN-LeakyReLU block is noted the operator P.
Cascade Structure. The output h E RR of the encoder will first perform a flattening operation shape L' and will be restored its original shape after RFTM processing. Without loss of generality, let L = 6 x h x w X for the example of latent space of image data, then L' = b x hwee. RFTM consists of a pair basic unit of SMx, R + R and MMc R + R where 0N = {M,, Mc). The former is the hippocampus mapping and the latter is the cortex mapping. They share an identical architecture in Section 3.2.1 but different hyperparameters. (N, k. v) connected by a memory connector as shown Figure
RFTM.
The formal description of RFTM is shown in the following Equation and Equation 5
h' (To(a (Puw S(h, Mz))) @ Ma)
(4)
h" o (To(TTTTe (Pvc @ S(h", (Pve
O Mc)
(5)
Where h² P(W). h P(h"') treated as the output of RFTM.
EXPERIMENTS
4.1 DATASETS AND EVALUATION
We selected three benchmarks as follows, (eng (II2..MINST(Xia et al.) 2017), ), and
CIFAR Kriihevsyk al. 2009.. All datasets follow the USAD settings. Each class in the dataset
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Under review as a conference paper at ICLR 2023
Table 1: The numerical results.
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79.58s0.33
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39.9663.15
25.5050.61
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44.55=1.35
36,6359.5
73.34
12.4540.90 94
12.16-2.91 21
1880-1445 4
40.445.33
6.3740.0
6.5240.16
6.5140.05
8.7061776
30. 00:904001
23:42-005
30 1.000001
21.461855
16.2089.22
53 03.80e1 25
9663250.4
97.0520.26
44.322952
07.996665
45 46.1621.21
$1.23e1.55
48.4720.78
65. 65.29=4226
49.34e1.17
74.734775
64.8:00.0 10
83. 22.30:511 11
65622.041
91.0:8.11
26
55.084456
17.31
48.921..5
224k:004
S6.66-3.51
25.01 14.01.0.11
5555:899
770065111 11
65 45
32.949-377
15:356925 25
25
16.4242.61
33.0249.9
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33.11=257
23.854100
71.3400
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25.0:344
17.0840.40
53.4711.99
25
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70.00:1-19
79
19 10.12:000
67.7243.20
22.75a0
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15
15.0011 16
433846.9
149640
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70.9741.0
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59 1906-1.M
58.541.42
44 44.382170
$3.5465
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10.319076
0.9820.00 49
17.5
17.55
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23.6860.30
26.32
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47.55
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38.56201
5000 2227
50
50
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11 135000
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21.32
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110131 25
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3000 15
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43.4400
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48.330
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51.1520:22
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63
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66.220.51
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Mcn-AE
MNAD
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65.6120
09:17:0.57
71.1488266
16.0080111
19.31
17.9750.33
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27.6920.35
20.180223
30
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20,4023105
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was selected in turn as the anomalous class and the remaining as the normal class, thus constructing multiple sub-experiments under one dataset. 10% of the training data is split into validation sets according to the random seed 2022. In the training and validation sets, the anomalies are removed and only normal data are left. The label information of all normal classes is also removed to satisfy the USAD setting. In the test set, all normal data are labeled as negative, and anomalies are labeled as positive. AUROC, AUPRC, and Fl were used evaluations for this experiment, where AUPRC can better reflect the performance of the model compared to AUROC in the unbalanced case.
4.2 IMPLANTATION DETAILS
To facilitate the description, we make the following notation convention. FC(a,b) denotes Linear- BN-LeakyReLU block, where a.b is number of input and output channels. Conv(a,b,k,s) de- notes Con--BN-LekyyeLLU block, where (a,b,k.s) denotes input channels, output channels, ker- nel size, and strides, respectively. DeConv(a.b.kss) denotes the ConnTraaspose-BB-LLekkkky block, where (a.b.k,s) has the same meaning above. We use the same autoencoder in MNIST and Fahion MNIIT experiments, i..., FC1(784,512). FC2(512,256). (C3(256,128)) (2,512,,)))), FC5(256,512), FC6(512,784). Because the CIFARIO dataset is more informative with RGB color images, we use a convolutional autoencoder, i.e., Convl (onv1(3,64,322) (6nv2(64,128,,32) Conv3(128,256,332) Conv4(256.512.332). DeConv5(512.256..22) Conv7(128.64,322) DcC,.)8664,333,22. Note that since MNAD requires a residual structure at the bottleneck, the first layer of his decoder is twice as large as the other models, namely: FC4(128+128,25) and DcConv5(512+5112256633
@CConv6(256.1283.22) De-
All parallel comparison models in Section 4 use the same encoder, decoder, random seed, training set, test set, and validation set. All experimental training procedures use the same batch size of 256, Adam optimizer with a le-3 learning rate, and early stopping with patience of 10. The hyperparame- ters (N, k2 //} for the twin memory is (50, 15, 0.1) } and (1000, 5, 0.5). We boosted the hippocampus and cortex memory capacity to 3000 in the CIFARIO experiment because color pictures are more informative. Vanilla AE, MemAE(Gong et al. 2019). MNAD( et al.) 2020 were used com- parison models. The random seeds for the training process, validation set splitting, and inference evaluation process is all the same to ensure absolute fairness and achieve targeted comparisons for RFTM effectiveness. All experiments were performed in three replicate runs with random seeds 1997, 2015, and 2022 respectively.
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(a) AE
(b) MemAR
(c) MNAD
(d) Ours
Figure 3: A demonstration of overgeneralization suppression.
1.0
1.4
0.5
0.-
Pr00
Montd
0.2
0.0
0.4
0.6
(a) ROC
(b) PRC
Figure 4: Curves of different models on two metrics.
4.3
MAIN RESULTS
We counted the results of the comparison tests in Table Each row in the table represents a different metric for different models on that test set with that class anomalous and the rest of the classes as normal. We use bold font to indicate the best results for each experiment under each metric. We achieve almost 20% improvement in the average AUROC metric the MNIST dataset. We also achieve state-of-the-art results on Fashion-MNIST and CIFARIO datasets, which suggests that RFTM has the ability to significantly inhibit OGP under USAD settings.
To better demonstrate the ability of RFTM to discriminate between normal and anomaly samples, we choose one of the experiments to normalize the reconstruction errors of all samples in the test set and draw the distribution according to the test labels as shown in Figure Bl Orange represents the reconstruction error distribution anomaly class and blue represents the reconstruction error distribution of normal class. Our method compared to others produces a significant distance between the reconstruction error distributions of normal and anomaly as shown in Figure 3d which shows that RFTM creates significant distinction over other models in the USAD setting. The previous results can verified by observing the ROC curve as shown Figure 4a
It is worth noting that the reconstruction enror distribution of normal samples is relatively short- tailed in the output of the RFTM-based model, indicating that the model has a lower false alarm rate. By plotting the rrecision-recal. curve (PRC) shown in Figure 4b it can be observed that RFTM always maintains a high precision for the same recall thus verifying the low false alarm rate due to the short-tailed distribution of normal reconstruction errors.
4.4 ABLATION STUDY
In this section, we carry out ablation experiments focusing on the influence of different basic units in RFTM. We randomly selected the experiments Fashion-MNIST for Coat anomaly and performed the ablation experiments with fixed random seed 1997 shown in Table 2 The performance of the original RFTM in the last row is used as a comparison criterion. The three rightmost columns represent the reduction of the ablation experiment relative the performance of the original RFTM, where the largest, second largest, and third largest reductions are indicated in black, red, and blue, respectively. It can be seen that cortex memory has the largest effect model performance. The hippocampus memory has the second largest effect. In addition, the active forgetting mechanism was shown be effective.

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Table 2: Results of ablation experiments on RFTM.
Basic Unit Ablation
Evalmation
Reduction
Forget Gates
Sparsification
Connector
H-Memory
C-Memory
AUROC
AUPRC
AUROC.
AUPRCI
F11
59.60
11.66
22.24
2.01
0.60
0.72
61.10
12.21
22.34
0.51
0.05
0.62
55,09
10.31
21.20 11.20
6.52
1.95
1.76
61.61
12.26
22.96
0.00
0.00
0.00
(a) Input
(b) AE
(c) MemAE
(d) MNAD
(c) Ours
Figure 5: Comparison the input images in the test set with the reconstructed images of different models.
4.5
BIONIC FEATURES
4.5.1 WHAT DOES RFTM DO?
RFTM learned to know the unknown by suppressing overgeneralization. We selected the first 50 samples in the test as shown in Fi These samples were mixed with the semantic anomaly handwritten digit 2. We compare the reconstructed results of the four models for these 50 samples. The top of the image is marked with the test ground truth, where 0 means negative normal and means positive anomaly. The output of the parallel comparison model proves that it falls into the overgeneralization trap as shown in Figure (b]5c/ This means that AE, MemAE, and MNAD all learn how reconstruct well the handwritten digit 2 that does not exist in the training set. The reconstruction results in the overgeneralization case have imperceptible differences at the naked eye level, however, AD estimates the anomaly degree of the sample by evaluating the reconstruction error, which indicates that the high reconstruction error in the overgeneralization case results from the image noise that is visible the naked eye level. This case is not interpretable and risks the failure of AD as the model parameter capacity is further increased.
The reconstruction results of the RFTM-based model satisfy the IIA. For all anomalous samples, the reconstructed results of the RFTM-based model exhibit boundary tightness and interpretability. On the one hand, RFTM does not reconstruct semantics that does not exist outside the training set, and on the other hand, RFTM only reconstructs semantics that already exists in the training set and can be understood by the naked eye. Compared to other models that identify anomalies by noise in the overgeneralization case, RFTM has larger reconstruction error and better interpretation. RFTM reconstructs the anomalous samples as handwritten numbers 0, 8, 3, 4, and 5 as shown in Figure Se This shows that the model only knows what it knows. In other words, RFTM gives the model the ability to know what it does not know.
4.5.2 How DOES RFTM WORK?
In order interpret RFTM from the representation space, we have done the following visualization. The outputs of the encoder, hippocampus memory, and cortex memory were selected to perform the representation visualization in the test set as shown Figure 7 Purple color indicates normal samples and yellow color indicates anomalies. The encoder always tends to construct a space with cavities as shown in Figure 7a| which are used to accommodate anomaly representations. However, the presence of cavities in the representation space causes the model learn the anomalous rep- resentation spontaneously, providing the basis for the decoder to reconstruct the anomalous pattern well (OGP).
Based on the encoder output space, hippocampus memory tries to construct denser and cavity- free space as shown in Figure 7b To produce large reconstruction emors for anomalous samples,
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Under review as a conference paper at ICLR 2023
7
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Figure 6: memory and cortex memory, respectively. Above the arrow is hippocampus memory and below is cortex memory.
We tracked the update of 49 randomly selected memory checkpoints in hippocampus
-5
-50
-08 Ae
(a) Encoder
(b) Hippocampus Memory
(c) Cortex Memory
Figure 7: 2D visualization of different stage representation output by tSNE.
the model needs to have a representation space that is dense enough that the anomalies are always forced to be embedded in the distribution of normal representations. The re-representation space of the cortex memory achieves enough denseness mentioned before. Figure 7 shows that the lack of cavities forces the anomalies to be distributed within the boundaries of the normal pattern. We illustrate the memory checkpoint generation in the RFTM training process in Figure intuitively verifies the above explanation. It be observed that the prototype memory checkpoints used for memory association gradually learn representative normal representations in the training set. The new space constructed based on these representative checkpoints will be denser and cavity-free, forcing the anomaly representation to be re-represented to known normal patterns as shown in Figure 5e
CONCLUSION AND FUTURE WORK
We proposed the RFTM for UAD inspired by the bionics of the hippocampus-cortex cascade struc- ture. which has yielded state-of-the-arr results in experiments. The RFTM has the following advan- tages. First, it can effectively inhibit overgeneralization to know the unknown anomalies. Second, it is network designed purely from structure perspective without adding additional loss terms, which makes it plug-and-play with a single line of code. Third, RFTM has bionic properties similar those of the human brain such active forgetting, memory association, hippocampus memory, and cortex memory, which provides potential new perspectives on the interpretability of unsuper- vised data reconstruction. RFTM has values that deserve to be studied and explored in more depth, such as the relationship between RFTM and generative networks, other variants of RFTM, the ap- plication of RFTM in multivariate time series analysis, etc.
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Reviews:
[{"review_id": "w1osoP5FMK-", "reviewer": "Reviewer_8c7P", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "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": "", "summary_of_the_paper": "The paper mainly explores the issue of “overgeneralization” in unsupervised anomaly detection and proposed a bio-inspired solution, termed “Random Forgetting Twin Memory (RFTM).” The new model did not change the fundamental structure of existing learning models, e.g., autoencoder, and work in a plug-and-play fashion. It achieves state-of-the-art performance on several public benchmarks.", "summary_of_the_review": "In brief, the new method developed an interesting bio-inspired RFTM model under the USAD setting to solve the issue of “overgeneralization.” The model details are well-elaborated. However, the experiments are inadequate as only the USAD setting is considered. Other routine settings and non-reconstruction methods should be considered and compared in a more comprehensive way.", "strength_and_weaknesses": "Strength:\n* The new method is promising compared to other memory mechanism-based anomaly detection methods under the unsupervised semantic anomaly detection (USAD) setting.\n\nWeakness:\n* The major issue is about the experiment design that only USAD setting is considered. While this setting is challenging, other routine settings would be considered as well. This allows the new methods to be evaluated on a variety of data such as image/video datasets.\n* How about the performance of the new method compared to non-reconstruction methods, e.g., [1], which also provides appealing performance under the USAD setting? \n* While it is fair to use the same encoder/decoder in the experiments, readers may be more interested in other methods, e.g., DAAD mentioned in the “introduction.”\n\n[1] Jihoon ,et al; CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances\n", "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": "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": "Upd0T27ZIg", "reviewer": "Reviewer_c8kV", "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": "", "summary_of_the_paper": "The work presents a memory network-based autoencoder method for unsupervised anomaly detection, in which random forgetting gates and top-k prototype selection functions are used to regularize the autoencoder networks to avoid overfitting. ", "summary_of_the_review": "The paper has major issues in main claims, empirical justification and clarity, and its technical novelty is marginal.", "strength_and_weaknesses": "Strengths.\n- The studied problem - having small reconstruction errors in reconstructing anomalous samples - is a common problem in autoencoder-based AD approaches\n- In the presented method, the key idea of enforcing less prototypes in the memory to alleviate the problem is plausible\n- A set of empirical results on three commonly used datasets is used to justify the effectiveness of the proposed method. \n\nWeaknesses.\n- Although the key idea is plausible, it shares similar insights to the previous memory network-based autoencoder approaches for anomaly detection, such as MNAD. The main objective there is to learn less yet compact prototypes to avoid the overfitting of the data. Although the specific way to achieve this objective is different from each other, the new prototype learning in this work does not make clear major contributions. The proposed method seems to simplify the memory learning, but its effectiveness is not clear (see comments below).\n- The presented results are not convincing. 1) The reported results of the competing methods in the paper are significantly worse than the ones in the their original papers and some recent relevant papers [A,B]. 2) The performance of the presented method is far below that of the current SOTA models (e.g., see the results on the three datasets in [A,B]).\n- There are a number of false/misleading claims, such as what does it mean by unlabeled normal samples in \"Unsupervised anomaly detection (UAD) only needs to fit the unlabeled normal samples to learn the normal patterns\" (why is it unlabeled if the samples are known to be normal), and \"most of the experiments were not conducted based on the USAD setup\" when reviewing memory network-based AD methods.\n- It is unclear why the learned memory can be named as Hippocampus and Cortex memory.\n- It claims that the method can work as a plug-and-play component, but not empirical results are given to support this claim.\n\nReferences.\n- A. \"Anomaly Detection via Reverse Distillation from One-Class Embedding.\" In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9737-9746. 2022.\n\n- B.  \"Deep one-class classification via interpolated gaussian descriptor.\" In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 1, pp. 383-392. 2022.", "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": "1: The contributions are neither significant nor novel.", "empirical_novelty_and_significance": "Not applicable"}, "extra_text": {}}, {"review_id": "KAXOgLWZGUg", "reviewer": "Reviewer_P1F7", "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": "", "summary_of_the_paper": "This paper tackles anomaly detection using a memory-based autoencoder approach. The main problem with reconstruction-based AD is that both normal and anomalous examples are well reconstructed making the anomaly score weak. Memory-based AE approaches have not addressed this problem sufficiently. This paper proposed a fairly complex memory mechanism which claims to overcome those issues. It draws inspiration from human cognition (although the precise analogy is not made very clear). The method is compared to a handful of memory-based AE approaches on simple dataset, and claims to obtain better results. ", "summary_of_the_review": "The paper tackles an important problems but the method is not well motivated, unclear. The experimental results are also unsatisfactory.  I recommend rejection.\n\n#######################################################\n\nI have read the the two responses by the authors but would like to keep my rating. ", "strength_and_weaknesses": "The task of anomaly detection is obviously important. Reconstruction-based methods have been around for a long time, but getting them to SoTA performance would be very interesting. \n\nI am not positive towards this paper. It makes very strong claims but does not deliver. \n* It claims \"We experimentally demonstrated the human brain’s similarity between RFTM and hippocampus-cortex structures\". This is a really strong claims and I do not think there is sufficient evidence to support it.\n* It claims \"state-of-the-art experimental results\" but in fact the experiments show it's ROCAUC on Cifar10 is around 50% (random performance). The comparison was only made against memory-based AE methods which are far from SoTA. Comparison against CSI (Tack et al.) or PANDA (Reiss et al.) would not support the above claims.\n\nFurthermore, the approach is probably too complex for its level of performance. The design choice is not justified by a sufficient ablation study making it unclear which aspects are in fact helpful.\nFinally, the complexity of the method made it unclear to me. With difficulty I think I managed to understand it, but it still seems arbitrary to me.", "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": "D8DCBzoMR7g", "reviewer": "Reviewer_ZPFH", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "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": "", "summary_of_the_paper": "The model consists of three parts: encoder, decod er, and RFTM, The encoder \nmakes a nonlinear transform to the input data x in the latent space. The latent data goes through a re-representation (in the same dimensionality) by the RFTM. Then the data is reconstructed by the decoder. The anomaly of the test sample is measured by reconstruction error. In particular, the RFTM  uses limited prototypes to record and re-represent the latent representation, which makes suppression of overgeneralization feasible, and can be trained end-to-end without introducing any additional penalty terms on top of the original task loss function. The rest of the paper is used to show the functioning of the RFTM module with some comparative results and a lot of ablation studies.\n", "summary_of_the_review": "- state of the art is insufficien, and I have provided some of the SOTA approaches for UAD\n- qualitative results in FIg.7 are not clear", "strength_and_weaknesses": "Strenghts: \nUnsupervised anomaly detection is a very interesting topic, more insidious than outlier detection.\n\n\nWeaknesses:\nThe comparative results are obviously insufficient, since only Vanilla AE, MemAE and MNAD were used as comparison models. Instead, Trust-MAE, DAAD, [Ruff et al., 2021] should be considered (and already mentioned by the authors in the state of the art). Other approaches are\n\n-- Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework 2020\n-- FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows\n-- Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection\n--Robust Subspace Recovery Layer for Unsupervised Anomaly Detection  2019\nThese approaches should also be present and discussed in the state of the art\n\nSecond, the study of figure 7 is not clear at all. The space constructed by the autoencoder and the two types of memory of the RFTM should have cavities (autoencoder) and less cavities (the second type of memory of the RFTM in particular). This is somewhat visible in the figures b and c. What is not clear is why the model needs to have a representation space that is dense enough that the anomalies are always forced to be embedded in the distribution of normal representations. Isn’t this a way to hide anomalies instead of highlighting them?\n", "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": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject