Unsupervised Class-Incremental Learning through Confusion
Authors: Shivam Khare
Kun Cao
James Matthew Rehg
Venue:  (2021)
Keywords: Incremental Learning
Unsupervised Learning
Continual Learning
Novelty Detection
Out-of-Distribution Detection

Abstract: While many works on Continual Learning have shown promising results for mitigating catastrophic forgetting, they have relied on supervised training. To successfully learn in a label-agnostic incremental setting, a model must distinguish between learned and novel classes to properly include samples for training. We introduce a novelty detection method that leverages network confusion caused by training incoming data as a new class. We found that incorporating a class-imbalance during this detection method substantially enhances performance. The effectiveness of our approach is demonstrated across a set of common image classification benchmarks: MNIST, SVHN, CIFAR-10, and CIFAR-100.

Under review as a conference paper at ICLR 2021
UNSUPERVISED CLASS-INCREMENTAL THROUGH CONFUSION
LEARNING
Anonymous authors Paper under double-blind review
ABSTRACT
While many works on Continual Learning have shown promising results for mitigat- ing catastrophic forgetting, they have relied on supervised training. To successfully learn in label-agnostic incremental setting, model must distinguish between learned and novel classes to properly include samples for training. We introduce a novelty detection method that leverages network confusion caused by training incoming data as new class. We found that incorporating class iaaalaim during this detection method substantially enhances performance. The effectiveness of our approach is demonstrated across set of image classification benchmarks: MNIST, SVHN, CIFAR-10, 10, CIFAR-100, and CRIB.
INTRODUCTION
The development of continually learning systems remains to be major obstacle in the field of artificial intelligence. The primary challenge is to mitigate catastrophic forgetting: learning new tasks while maintaining the ability perform old ones. This domain of research is often referred Learning, Lifelong Learning, Sequential Learning, or Incremental Learning: each with subtleties in the learning environment and training process, but most with the use of supervision (De Lange et al.
Continual
(020)).
Recently, Stojanov al. 2019) introduced novel unsupervised class-incremental learning problem motivated by the desire to simulate how children's 's play behaviors support their ability to learn object models (IOLfCV). Here, sequential tasks take the form of exposures. Each exposure is comprised of set of images that pertains to a single class that is hidden from the learner. Exposure boundaries, the transition from one exposure to the next, are known. The model trained this setting, analogous to young child that has been placed in a playpen with set of new toys. The child steadily gathers information over time by picking up, examining, and putting down new/old objects continuously. Similar to how the child does not have any guidance the toys they will examine, the agent does not have access to the exposure's 's identity during training.
To learn in the unsupervised class- innsemnncremttall setting, agent must conduct two procedures successfully. Given a new learning exposure, the key step is to perform novelty detection: to identify whether exposure corresponds that an exposure is familiar, the second step is to identify its label such that the exposure can be leveraged to update the model. Both procedures must be performed reliably. Otherwise, the novelty detection mistakes will result in label noise that distorts the learned model, increasing the likelihood of subsequent mistakes.
class that has been learned. If the agent determines
Deep neural networks are known to make overconfident decisions for anomalous data distributions that were not seen during training (Hendrycks Gimpel (2016)). To address this problem, research (OOD) detection have utilized supervised methods (Liang et al. (2017);
related to
Alemi et al. (018)) ) and unsupervised methods (Choi & Jang (018): (Hedrrykk al. (2018) 8): Serra et al. (2019) ). Works related to open set recognition have also addressed the OOD problem by applying distance-based thresholds computed from known class scores (Scheirer et al. (001 2014)
The work by Stojanov et al. (2019) applies similar method to the unsupervised incremental setting by computing class features produced from a set of supervised samples. In contrast, we propose a model, Incremental Learning by Accuracy Performance (iLAP), that determines class novelty and identity by considering performance changes of previously learned tasks when an incoming set of exposure images trained under new label.

---

Under review as a conference paper at ICLR 2021
Instead of using distance- metric, novelty detection threshold relies the percentage of accuracy that was maintained by performing a model update using the incoming exposure. This poses several practical advantages: First, the threshold value does not rely supervised samples and is more intuitive (Section (3.3). ). Second, the performance of our method is independent of the sequence of the incoming exposure classes (Section 5.2). Finally, the model is able to distinguish between similar classes more reliably (Section 5.3p.
From our experiments, we demonstrate that the confusion resulting from training with label am- biguity results in more reliable signal for novelty detection in comparison to previous methods. We demonstrate that our technique is more robust and results in substantial performance gains in comparison to various baselines. Furthermore, despite the absence of labels, our model was able to perform similarly to supervised models under several benchmarks.
In summary, this work provides three contributions:
We present a novel framework, iLAP, that achieves learning in the unsupervised class- incremental environment where the exposure identities are unknown.
We demonstrate that by including class-imbalance technique, our unsupervised method is able to closely match supervised performance for several image classification benchmarks trained in the incremental setting.
We identify failure cases that are overlooked by traditional OOD methods that leverage distance-based thresholds.
RELATED WORKS
Introduced by Stojanov al. 2019), the unsupervised cass-incrrmental setting contains set of sequential tasks that are single-class exposures; classes pertaining to the exposures may repeat and are unknown. This not to be mistaken with unsupervised continual learning (UCL) where task boundaries and task identities are unavailable (Lee et al.) (2020); Smith & Dovrolis (2019); Rao et al. (2019)) ). Our work presents an agent that able leverage the boundary information from the unsupervised class-incremental environment to achieve performances that are close to models trained under supervision.
2.1 CONTINUAL LEARNING//NCCEMENNN LEARNING
Prior works this field primarily aim to improve model's s ability to retain information while incorporating new tasks (Goodfellow et al. (2013): Parisi et al. (2019): Rebuffi et al. (2017); [017):Lopezz Paz & Ranzato| Alijudii et al. 20 Castro et al. (2018). Typically, these models reside learning settings where both task labels and task boundaries
available. Methods include replay techniques, the usage artifacts and generated samples to refresh a model's 's memory (Kamra et al. (201)); Wu et al. (2018); [Ronicck et al.] (2019): [Shin et al. (2017); Wu et al. (2019)), and practices, the identification and preservation of weights that are crucial for the performance of specific tasks (Kirkpatrick et al. (2017) Zenke al. (2017); Yoon al. (2017)) In contrast to prior works, our method addresses incremental learning in setting where exposure labels are unavailable.
regularization-bate
2.2 UNSUPERVISED CONTINUAL LEARNING
Recently, series of works tackle the UCL problem where task boundaries and task identities are unknown. Smith & Dovrolis (219) performs novelty detection by analyzing an input image through a series of receptive fields determine if input patch is an outlier. Meanwhile, CURL proposes method to learn dass-discriminative representations through set of shared parameters (Rao et al. (2019)). ). CN-DPM, introduces an expansion-base based approach that utilizes a mixture of experts to learn feature representations (Lee et al. (2020)). Although CN-DPM performs in task-free setting, incoming tasks are multi-class and individual class labels are provided. This supervision is required to train the existing experts and determine when new one is needed. While boundary information is not required for these works, the performances are far below supervised baselines (77.7% on and MNIST 13.2% Omniglot) (Rao et al. (019)).
2

---

Under review as a conference paper at ICLR 2021
F
g++1
g
Detection Training
Splic Fraas
Update with R + 1 Label
Compute Changes Accuracy
Update With Pred. Label
Exemplar Update
Foot
Use Class Imbalance
Perstn
Post
Incoming exposures are split into Erain and Eyat- Eirain is aggregated with Peraén and is used for detection training. É is updated with samples from Eirain labeled as R + 1. The change in model accuracy is assessed using P to identify novelty and class identity. Finally, L is updated with the predicted label; samples from E and E are saved to their respective exemplars.
Figure I:
2.3 NUTTOO-DIITRRIBUTIE DETECTION
This ongoing area of research aims to detect outliers in training and testing data. Current approaches can largely categorized by statistical, distance-based, and deep learning methods (Eskin (2000); Yamanishi et al. (2004): Knorr et al. (2000); Hautamaki et al. (2004); (Sabokrou et al. (018); 8); Kliger
Fleishman (2018)). Recent techniques involve using a threshold to determine class novelty from network confidence values (Hendrycks & Gimpel (2016)). ODIN uses input perturbations to increase softmax scores for neural networks to distinguish in-distribution images from out-of-distribution images (Liang al. (2017)). Vries & Taylor (2018) incorporates confidence branch to obtain out-of-distribution estimations. Our method (iLAP) is the first to incorporate a threshold value that is dependent on class-accuracy changes caused by data poisoning.
APPROACH
In this section, we provide overview of our method. We begin by identifying the learning setting, followed by details of our training process. Finally, insights for choosing threshold values are provided.
3.1 SETTING
In the unsupervised class-incremental setting, learner perceives an input stream of exposures denoted as E1, E² ...., E". Each exposure contains set of images, E ef, ..., ex ). et E xCxH*W where C, H, and W are the number of channels, height, and width of the input image respectively. Each exposure belongs to single class yi N, which has been sampled from class distribution P(C). For each E', does not have knowledge of the true class label y;- Two exemplars, Ptrain = =P) P ..., P& and Pual (P1 p ..., pK)), are maintained at all times, where K denotes the total number of classes has currently determined. The exemplars used to store samples from the exposure for replay and accuracy assessment. The sizes of both exemplars, Pi Perasil and Pá | bounded per class.

---

Under review as a conference paper at ICLR 2021
3.2 DETECTION TRAINING
For each incoming exposure, the model is tasked to determine whether the class associated with the exposure was learned previously. Our solution is to perform a model update by treating the incoming exposure as a new class, we coin this technique detection training. Under the circumstances that the exposure class is repeated, the performance for the previously learned class would suffer drastically after training. The reason for this behavior is because the model has associated two different labels to a similar class distribution.
During detection training, L, copy of L is produced. The incoming exposure is assigned with label split is performed on the incoming exposure to obtain Etrain and Eval. and
K 1. are aggregated with exemplars Ptrain and Peal respectively. The combined samples are used to train via validation- baasdttio--baedd early stopping. We denote the vector {Ag] ) je[K] to represent the percentage
decrease of the class accuracies (computed using Poat) before and after the update. If max (اg}) exceeds threshold, 0, the incoming exposure is likely to have been learned by L. In this case, the correct identity pertaining the exposure is arg max (E(]] is the appropriate label for the new class.
Ag. Otherwise, if is unsatisfied, K +
3.3
FOR DETECTION TRAINING
Fashion-MNIS: Accuracy Change
Imagenette Percent Accuracy Change
Max Percent Decrease
0.8
Decrease
0.6
with
0.6
0.4
0 WorkourC
0.4
@wnkour CV
Repeated Classes
0.2
Repested Classes Non- Repeated Classes
Non- Repeaeed Classes
Class Imbalance Ratio (A)
Class Imbalance Ratio
Figure 2: Graphs depicting the average accuracy decrease for repeated vs. non-repeated classes at various values of class-imballane. As the ratio of class-imbalance increases, the accuracy change for non-repeated classes remains low, with the exception at high ratios when very few samples are present during the model update (red). The accuracy decrease for repeated classes is amplified the ratio caassimmbllncee increases (blue).
Introducing class-imbalance during detection training creates more distinct decision boundary by exacerbating the class-accuracy drop for repeated exposures. Consider a theoretical case where an optimal model has learned K classes. The incoming exposure, E*, contains distribution that is equal that of some previously learned class t/. If the model were updated with equal samples of E labeled K + 1, and pti Prain labeled Vi. the accuracy for class di would become ambiguous during validation ( 50%, K + 1 50%). However, the model were updated with greater sample of K + 1 labels, the accuracy drop for class i would be considerably larger because K + 1 would be favored during inference.
î, has the option to use an imbalanced dataset during detection training where a fraction of the size for each class from Ptrain are used in comparison to the size of Errain Let P saamledd C P Pirains the class-imbalasd ratio À is:
PP Paampteal
A 1
(1)
Etrain
use Fashion-MNIS. (iiao al. (2017)) and Imagenette (Howard) obtain general value for À and for our experiments with other datasets. Performance drops are tracked for the repeated class and non-repeated classes during detection training with various values of X. The
In Figure 2
4

---

Under review as a conference paper at ICLR 2021
goal is to maximize the performance loss for the repeated class while maintaining the performance for the other classes. In Section a value of 0.6 and a À value of 0.5 are used. À is determined by the point of the maximum distance between the two accuracy curves; 8, the expected accuracy drop for a repeated exposure, is the mean of the two values at Figure
3.4 MODEL AND EXEMPLAR UPDATE
After obtaining the predicted label (K + 1 or arg ge Ag) from detection training, L is trained with the aggregated dataset obtained from Prrain and Etroin. Two sets with the most representative samples from Errain and Etest are created and added to their respective exemplars for future replay. The selection process is determined by ranking the distance between the image features and the class-meat image features. This methodology is consistent with the procedure introduced by Castro et al. 2018).
The availability of Pual allows L to assess how well old and classes considered. If the accuracy for any class interpreted by L falls below a percentage, the class altogether discarded. This allows the model to remove extraneous classes that were learned insufficiently have been forgotten. In Section 4 a percentage threshold of 20% is used for all experiments.
EXPERIMENTS
The performance of our framework is evaluated using series of image classification benchmarks: MNIST, SVHN, CIFAR-10. 10, CIFAR- 100, and CRIB (LeCun (1988); Netzer et al. (2011); Krizhevsky al. (2009); SSooaanov al. (2019). First, compare novelty detector related OOD methods.
Next, we evaluate performance of iLAP to that of other incremental learners: BiC (Wu et al. (2019)
(supervised) and IOLICV (unsupervised).
4.1 EXPERIMENTAL DETAILS
For the following experiments, a ResNet-18 model (He et al. (2016)) pre-trained with ImageNet is used for iLAP and all baselines (additional experiments without pretraining are presented in Appendix A.4). = 0.5 and = 0.6 are used for iLAP with class-imbalance detection training, while À 0 and A = 0.4 are used for iLAP without iass-immalaacce detection training. The parameters are maintained across all benchmarks.
For each exposure, the model is trained for 15 epochs with 16 batch size, using Adam (Kingma & Ba (2014)) optimizer with validation-batt based early stopping: a learning rate of 2e-4 used. The feature extraction layers
times lower learning rate at 2c-5. For all models, the exemplar size per class is equal to the exposure size. The exposure validation split ratio 0.8 (e.g. for exposure size 200, iLAP: [Errai]] 160 and [Erar] 40). The thresholds used for IOLíCV in 4.1 were determined by maximizing the F-score for the classification of in-distribution exposures versus of-distribution exposures. To obtain the best performance possible, the entirety of the dataset was used. The values are 0.46, 0.63, 0.57, and 0.62 for MNIST, SVHN, CIFAR- 10, and CIFAR-100 respectively.
4.2 OUT-OF-DISTRRIBTIIO DETECTION RESULTS
The OOD detectors are assessed an incremental setting with size 200 exposures. The detectors are evaluated on their ability to determine whether exposure is novel by using the common established metrics: FPR95, AUROC, and AUPR (Hendrycks & Gimpel (2016)). Details the compared works and evaluation methods described in Appendix|A.] A. The results illustrated in Table
5

---

Under review as a conference paper at ICLR 2021
MNIST
CIFAR-10
FPR95
AUROCT
AUPR
FPR95
AUROC1
AUPR
MSP
0,14#.09
0.98#.01
0.95#.02
0.60#.13
0.861.04
0.71= 09
CE
0.35+..13
0.80#.04
0.67#.09
0.642.13
0.78
0.54= 03
ODIN ZeroShot OOD IOLICV
0.12#t009
0.98#.01
0.95#.02
0.55+.12
0.87+.04
0.71= 09
0.03=t.03
0.99
0.99:t011
0.63+.12
0.67+.06
0.47= 00
0.124.08
0.98
0.95
0.60
0.781.06
0.60 08
iLAP w/o CI (Ours)
0.194.02
0.92
0.68
0.581.03
0.69-1.03
0.40 = 05
iLAP w/ CI (Ours)
0.0 -
1.0
1.0 :.
0.32 .10 0.93
+ .03
0.81 + .08
SVHN
CIFAR-100
FPR95
AUROCT
AUPR1
FPR95
AUROC1
AUPRT
MSP
0.08#.04
0.98
0.98
0.20#.04
0.95+.02
0.71 .99
CE ODIN ZeroShot OOD IOLFCV
0.5
0.78+.03
0.72.1.77
0.55
0.821.01
0.63 02
0.124:01
0.94
0.93
0.21#.04
0.94
0.91 = 01
0.06:F.04
0.97
0.98+.01
0.23+.03
0.97+.01
0.94= 02
0,284.11
0.96#.01
0.95
0.28#.05
0.96
0.94= 01
iLAP w/o (Ours)
0,08#.02
0.92#.02
0.892.04
0.27
0.92..05
0.72-.12
iLAP w/ CI (Ours)
0.0 .0
0.99 + .0
0.98 + 0
.08 .02
0.99 + .0
0.98 + .0
Table Comparison of technique, with and without class-inbalance, recent novelty detection methods. FPR95, AUROC, and AUPR are used to evaluate performance. Our method is the most effective the incremental learning setting where exposure samples are limited.
Test Aceuracy (%)
MNIST
SVHN
CIFAR-10
CIFAR-
CRIB
100
BiC (Supervised)
98.0 + 0.2
89.2 + 0.3
74.9 + 0.4
72.5 + 0.3
88.5 + 0.2
IOLICV (Unsupervised)
88.0 + 3.5
46.8
45.3 + 4.7
61.6 + 0.6
66.8 + 2.1
iLAP w/o CI (Ours)
90.3 + 1.7
1.7
60.9 + 1.4
65.0 1.2
59.0 + 1.5
iLAP w/ CI (Ours)
98.0 = 0.2
88.1 0.8
6.74
68.6 6 0.7
67.8 + 1.7
Table 2: Comparison of iLAP to IOLfCV and BiC by test accuracy (%) using the MNIST, SVHN, CIFAR-10, CIFAR-100 and CRIB datasets.
No. of Unique Classes Learned
MNIST
SVHN
CIFAR-10
CIFAR-
CRIB
100
BiC (Supervised)
10.0 + 0.0
10.0 + 0.0
10.0 + 0.0
100.0 + 0.0 50.0 + 0.0
IOLICV (Unsupervised)
9.2
1.2
8.7
89.0 0.5
42.0
iLAP w/o CI (Ours)
10.0
1.0
1.0
96.0
43.0 0.1
iLAP w/ CI (Ours)
1.0
1.00
10.0
9.55
45.0 - 0.2
Table 3: Comparison of iLAP IOLICV and BiC by the number of unique classes learned using the MNIST, SVHN, 10, CIFAR-100 and CRIB datasets.

---

Under review as a conference paper at ICLR 2021
4.3 INCREMENTAL LEARNING RESULTS
The accuracy of learner L is computed using the ground-truth mapping m [R] - [X] with the equations:
(w)l+ if [(x) E m my)
S(r, v)
0,
otherwise
Accuracy = Enwyttst [S y)]
The learner accuracy the mean of the sample accuracy scores evaluated on the test set, where (x, y) represents single sample. For each sample with label let m- represent the corresponding labels from the learner. the case that a learner output does not belong to set m -1 (y), an accuracy score of 0 is assigned (class is not detected). Otherwise, accuracy score of m i-T is designated penalize the learner if m is non-injective and have attributed multiple labels to single ground
truth class. Performance results are illustrated in Table & Additional visualizations are provided
in Appendix A.2 A.3 and
ANALYSIS
Traditional OOD methods that rely on distance-based thresholds are restricted by the supervised samples that are available. These values are non-intuitive and vary drastically across datasets (whereas our percentage threshold are = 50% for all datasets). In the incremental learning setting, early mistakes are amplified as more exposures are introduced, a proper threshold initialization dictates a model's feasibility. However, we argue that even with a good threshold these methods will consistently fail in particular conditions. The purpose this section is to discuss the results obtained from our experiments. Subsequently, highlight a few cases that are overlooked by traditional distance-based methods.
5.1 OUT--FF-ISTTRBUTION DETECTION ANALYSIS
iLAP with class-imbalance detection training (CI) was able outperform related OOD methods for the MNIST, SVHN, CIFAR-10, 10, and CIFAR-100 100 benchmarks under all metrics Table However, the results for iLAP without CI were not as definitive. CE performed the worst in the incremental setting, possibly because the performance of the confidence branch is reliant on larger training samples. IOLiCV's method performed on par with related methods.
Learned Exposures
0.700
Learned Class
0.675
0.650
0.625
0.600
0.25
Classes Learned
Figure 3: Visualization depicting the behavior of class feature distances as more classes are learned by the network. The optimal threshold computed by optimizing the F-score is the midpoint of the learned and novel class values when all 100 classes are incorporated. However, this threshold will mistakenly recognize repeated classes new classes early during incremental training.
7

---

Under review as a conference paper at ICLR 2021
5.2 UNSUPERVISED INCREMENTATL LEARNING ANALYSIS
iLAP with CI was able to beat IOLFCV by 10.0, 41.3, 22.1, 7.0, and 1.0 percentage points for the MNIST. SVHN, 10, CIFAR- 100, and CRIB benchmarks respectively Table iLAP was also able to maintain its performance when exposure sizes are decreased Appendix A.3 We found that the reason for the lower performance beat for CIFAR-100 and CRIB is not directly attributed to the larger number of classes the dataset. Rather, the problem lies with how the exposure sequence for the incremental learning setting is created and how the cestance-based threshold is calculated.
The threshold for IOLfCV is computed by maximizing the F-score for the binary classification of novel versus non-novel classes. In our experiments, the entirety of the dataset was used to compute the baseline's threshold. Although this is impractical, we wanted to illustrate that AP is able to beat the baseline even under the most optimal conditions.
In Figure 3 we illustrate the behavior of the class feature distances as more classes are incorporated by network. The most optimal threshold that maximizes the F-score lies in the mid-point between the two graphs when all 100 classes are learned. However, because the threshold is fixed, the novelty detector fails to correctly identify repeated classes early on during training and is more inclined to label repeated classes as unseen, (shaded red area in Figure 3). Consistent with the described setting in (Stojanov al. (2019), each class within benchmark is repeated an equal number of times in randomized sequence. For datasets with a large number of classes, there is higher chance that the repeated exposures are further apart. Therefore, IOLICV seemingly performs comparatively better on CIFAR-100 1-0 than CIFAR-10 10, but would fail if early repeated exposures were frequent.
5.3 CLASS SIMILARITY
say banod
o61 (nOovine
G81 (ecorreq)
70-
10-
-20
-20
-10
Figure 4: TSNE plot depicting model which has been trained to classify lamp vs. boy. An incoming exposure with class girl is introduced to the model. Comparing the feature distributions of this model, boy and girl have almost identical distributions; the OOD detector mistakenly assesses them to be same class (left). In contrast, our method first attempts to learn a feature space where the two classes separable and identifies boy and girl correctly (right).
iLAP was able to detect all classes for MNIST, SVHN, and CIFAR-IO and on average 96.5 out of 100 classes for CIFAR-100. Meanwhile, IOLICV struggles identify unique classes for all evaluated benchmarks. Through closer inspection, we found that the distance-based method is unable distinguish classes when they are too similar.
Consider two classes, 1 and 22, that can be separated by a classifier C in learned feature space F. incoming exposure, class k3, shares a similar distribution to class hy in feature space F, but separable in some feature space F'. The distance- based method is highly probable to fail because it is likely to categorize A and K3 as identical classes. However, because our method always trains new class, C is forced to learn the feature space F' in which these two
the incoming exposure classes are separable. Figure 4 fillustrates two prior classes, boy and lamp, in some feature space. The incoming exposure, class girl, is unable to be distinguished from class boy by the distance- based method Figure (left). However, because detection training always attempts to classify the incoming exposure as new class, our method is able identify F Figure 4 (right).
8

---

Under review as a conference paper at ICLR 2021
6 CONCLUSION
To achieve learning in an unsupervised ctass-incrementee setting, reliable novelty detector is needed. Current methods utilize detection threshold that is calibrated using class feature distances. In our work, we illustrate that the use of a static distance- based threshold is not only impractical but also unreliable. Instead, we introduce a technique that leverages confusion error perform novelty detection by always training the incoming exposure as new class. Using a series of image classification benchmarks, we illustrate that our method is able closely rival supervised performance despite the lack of labels.
REFERENCES
Alexander A Alemi, lan Fischer, and Joshua V Dillon. Uncertainty in the variational information bottleneck. arXiv preprint arXiv: 1807000906, 2018.
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars. Memory aware synapses: Learning what (not) to forget. In Proceedings of the European Conference Computer Vision (ECCV), pp. 139 154, 2018.
Francisco M Castro, Manuel naríneJiménez, Nicolás Guil, Cordelia Schmid, and Karteek Alahari. End-to-end incremental learning. In Proceedings of the European Conference on Computer Vision (ECCV), pp. 233-248, 2018.
Hyunsun Choi and Eric Jang. Generative ensembles for robust anomaly detection. 2018.
Matthias De Lange, Rahaf Alijundi, Mare Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, and Tinne Tuytelaars. continual learning survey: Defying forgetting classification tasks. arXiv preprint arXiv: 1909.08833 2020.
Terrance VeVries and Graham W Taylor. Learning confidence for out-of-diitriiution detection in neural networks. arXiv preprint /802 04865, 2018.
Eleazar Eskin. Anomaly detection over noisy data using learned probability distributions. 2000.
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio. An empirical investi- gation catastrophic forgetting in gradient- based neural networks. arXiv preprint arXiv: 13 1316.221, 2013.
Ville Hautamaki, Ismo Karkkainen, and Pasi Franti. Outlier detection using k-nearest neighbour graph. In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., volume 3, pp. 430 433. IEEE, 2004.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
Dan Hendrycks and Kevin Gimpel. A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint 1rXiv:1610.021.66 2016.
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich. Deep anomaly detection with outlier exposure. arXiv preprint urXiv:1812 1al.:1882.044000 2018
/ttphtttttttttb Jeremy Howard. Imagewang. URL /ttpppps//gaaaaa...ccaaaaaaaaaaa http:///aaaaaaaiiaaanetttttttttt
Nitin Kamra, Umang Gupta, and Yan Liu. Deep generative dual memory network for continual learning, arXiv preprint 1710 10368, 2017.
Diederik P Kingma and Jimmy Ba. Adam: method for stochastic optimization. arXiv preprint arXiv: 14/2.6980, 2014.
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei Rusu, Kieran Milan, John Quan, Tiago Ramalho. Agnieszka Grabska- Barwinska, et al. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences, 114 (13):3521-3526, 2017.

---

Under review as a conference paper at ICLR 2021
Mark Kliger and Shachar Fleishman. Novelty detection with gan. arXiv preprint 18X.v:1802.10560, 2018.
Edwin M Knorr, Raymond Ng, and Vladimir Tucakov. Distance-based outliers: algorithms and applications. The VLDB Journal, 8(3-4):237-2553 2000.
Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009.
Yann LeCun. The mnist database of handwritten digits. http://yann. lecun. com/exdb/mmist// 1998.
Soochan Lee, Junsoo Ha, Dongsu Zhang, and Gunhee Kim. A neural dirichlet process mixture model for fask-free continual learning. arXiv preprint arXiv:2001.0068) 2020.
Shiyu Liang, Yixuan Li, and Rayadurgam Srikant. Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv preprint
2017.
David Lopez-Paz and Mare' Aurelio Ranzato. Gradient episodic memory for continual learning. In Advances in Neural Information Processing Systems, pp. 6467-6476, 2017.
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng. Reading digits in natural images with unsupervised feature learning. 2011.
German Parisi, Ronald Kemker, Jose Part, Christopher Kanan, and Stefan Wermter. Continual lifelong learning with neural networks: A review. Neural Networks, 2019.
Dushyant Rao al. Continual unsupervised representation learning. In Advances in Neural Informa- lion Processing Systems, pp. 7645-7655, 2019.
Sylvestre- Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. icarl: Incremental classifier and representation learning. In Proceedings of the IEEE conference on Computer Vision and Patlern Recognition, pp. 2001-2010, 2017.
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne. Experience replay for continual learning. In Advances in Neural Information Processing Systems, pp. 348-358, 2019.
Mohammad Sabokrou, Mohammad Khalooei, Mahmood Fathy, and Ehsan Adeli. Adversarially learned one-class classifier for novelty detection. In Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 3379-3388, 2018.
Chandramouli S Sastry and Sageev Oore. Zero-shot out-of distribiittiiitinonn detection with feature correlations. 2019.
Walter Scheirer, Anderson de Rezende Rocha, Archana Sapkota, and Terrance E Boult. Toward open set recognition. IEEE transactions pattern analysis and machine intelligence, 35(7): 1757-1772, 2012.
Walter Scheirer, Lalit P Jain, and Terrance E Boult. Probability models for open set recognition. IEEE transactions on pattern analysis and machine intelligence, 36(11):2317-2332, 2014.
Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F Núñez, and Jordi Luque. Input complexity and out-of-distribution detection with likelihood- generative models. arXiv preprint arXiv: /480, 2019.
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jíwon Kim. Continual learning with deep generative replay. In Advances in Neural Information Processing Systems, pp. 2990-2999, 2017.
James Smith and Constantine Dovrolis. Unsupervised progressive learning and the stam architecture. arXiv preprint S033::9940000000 2019.
Stefan Stojanov, Samarth Mishra, Ngoc Anh Thai, Nikhil Dhanda, Ahmad Humayun, Chen Yu, Linda B Smith, and James M Rehg. Incremental object learning from contiguous views. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8777-8786, 2019.
10

---

Under review as a conference paper at ICLR 2021
Chenshen Wu, Luis Herranz, Xialei Liu, Joost van de Weijer, Bogdan Raducanu, et al. Memory replay gans: Learning to generate new categories without forgetting. In Advances In Neural Information Processing Systems, pp. 5962-59699999 59722, 2018.
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye. Zicheng Liu, Yandong Guo, and Yun Fu. Large scale incremental learning. In Proceedings of the IEEE Conference on Computer Vision and Puttern Recognition, pp. 374-382, 2019.
Han Xiao, Kashif Rasul, and Roland Vollgraf. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017.
Kenji Yamanishi, Jun-Ichi Takeuchi, Graham Williams, and Peter Milne. On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Mining and Knowledge Discovery, 83)):75-300,, 2004.
Jaehong Yoon, Eunho Yang. Jeongtae Lee, and Sung Ju Hwang. Lifelong learning with dynamically expandable networks. arXiv preprint arXiv: aeti:170..044444 2017.
Friedemann Zenke, Ben Poole, and Surya Ganguli. Continual learning through synaptic intelligence. In Proceedings of the 34th International Conference Machine Coarning-Voluune 70, pp. 3987- 3995. JMLR. org, 2017.
A APPENDIX
A.1 OOD EXPERIMENTAL DETAILS
brief description of the evaluation metrics used for Table
False Positive Rate 95% (FPR95): This measure determines the False Positive Rate (FPR) when True Positive Rate (TPR) is equal to 95%. FPR is calculated as FP4IN where FP is the number of False Positives and TN is the number of True Negatives. TPR is TP where TP is the number of True Positives and FN is the number of False Negatives.
FP
calculated TP+FN
Area Under the Receiver Operating Characteristic (AUROC): This metric illustrates the relationship between the TPR and the FPR. This measure determines the probability that novel class will have higher detection score compared to a non-novel class.
Area Under the Precision-Recal (AUPR): This metric is constructed by plotting precision versus recall. The AUPR curve treats the novel examples as the positive class. A high area value represents high recall and high precision.
The training detection method used in iLAP compared to set of related works in OOD. The following works reflect the acronyms used in Table
MSP (Hendryeks & Gimpel (2016)): MSP is computed from trained classifier to perform outtof-distnbuutioo detection. The mean MSP for all images belonging to an exposure is used to determine novelty.
CE (DeV Vries & Taylor (2018)): CE requires extending model with confidence branch to obtain a set of values. These values reflect the model's ability to produce correct prediction. When the mean estimation value for set of input images is low, the sample is likely to be novel.
ODIN (Liagg et al. (2017)): ODIN uses temperature scaling and input perturbations to widen the MSP difference between in-distribution and out-of-distribution samples. The optimal value for temperature and perturbation magnitude are found by minimizing FPR using grid search. 2 and 0.0012 used for temperature and perturbation magnitude values respectively, for both the MNIST and CIFAR-10 10 dataset.
Zero-Shot OOD (Satryy & Oore (2019)): Zero-Shot OOD uses pairwise correlations of network features detect novel samples. The class-conditional feature correlations,
11

---

Under review as a conference paper at ICLR 2021
the training data, computed across all layers of the network. The mean correlations then compared with the pairwise mean feature correlations from test sample to obtain deviation value.
IOLICV (Stojanov et al. ): IOLICV determines distance-based threshold puted using average-feature means from a network trained from supervised samples. Two initialized classes are used compute the threshold by finding the optimal point using precision-reed recalll analysis.
A.2 MAIN EXPERIMENT RESULTS
The following are produced with the use of a GTX TITAN gpu. For each exposure, model trained for 15 epochs with 16 batch size, using an Adam optimizer. The learning rate used is 2e-4. The feature extraction layers use a ten times lower learning rate at 2e-5. The input size is 224.
Accuracy
0.975
Validation Accuracy
0.950
No. Classes Learned
0.925
0.900
0.875
w/ imbalance
ILAP w/o class imbalance IOLICV (unsupervised)
ILAP w/o class imbalance
IOLICV (unsupervised)
(Supervised)
Exposure No.
Exposure No.
Figure 5: Visualizations comparing accuracy (left) and number of classes detected (right) for the MNIST benchmark.
Accuracy
0.9
Validation Accuracy
0.8
Learned
0.7
0.6
w/ imbalance ILAP w/o class imibalance JOLICV (unsupervised) (Supervised)
w/ imbalance IAP w/o class imbalance IOLFCV (unsupervised)
0.
(Supervised)
Exposure No.
Exposure No.
Figure 6: Visualizations comparing accuracy (left) and number of classes detected (right) for the SVHN benchmark.
12

---

Under review as a conference paper at ICLR 2021
CIFAR-10 Accuracy with 200ES
CIFAR-10 No. Classes with 200ES
imbalance imbalancce IOLICV (unsupervised) BIC (Supervised)
0.9
Validation Accuracy
No. Learned
0.8
0.7
IAAP w/ class imbalance
0.5
3
IOLFCV (unsupervised)
IAA w/o class imbalance
(Supervised)
0.4
20
60
80
Exposure No.
Exposure No.
Figure 7: Visualizations comparing accuracy (left) and number of classes detected (right) for the
CIFAR-10 benchmark.
CIFAR-100 Accuracy with 200ES
CIFAR-100 No. Classes 200ES
ILAP imbalance ICV (unsupervised) BIC (Supervised)
100
Validation Accuracy
No. Classes Learned
0.90
0.75
ILAP w/ class imbalance
0.70
ILAP w/o class Imbalance
IOLFCV (unsupervised)
(Supervised)
50
75
100
125
150
175
25
100
125
150
175
Exposure No.
Exposure No.
Figure 8: Visualizations comparing accuracy (left) and number of classes detected (right) for the CIFAR-100 benchmark.
Accuracy
ILAP w/ class imbalance (unsupervised)
Validation Accuracy
0.9
Classess Learned
BIC (Superosed).
0.8
0.7
w/ imbalance
0.6
IA w/o class imbelance
IOLICV (unsupervised)
(Supervised)
175
125
150
175
Exposure No.
Exposure No.
Figure 9: Visualizations comparing accuracy (left) and number of classes detected (right) for the
CRIB benchmark.
A.3 EXPERIMENTS WITH LOWER EXEMPLAR SIZES
In this section, we showcase iLAAP's results at lower exposure sizes.
13

---

Under review as a conference paper at ICLR 2021
HS4 Accuriay Clossen
10009
MUST Aconvcy anlcissn Leated will 5065
VISST Accundy and Classes with 2005
2.0
Lournaa
2.6
0.0
Figure 10: Visualizations depicting iL.AP's performance the MNIST benchmark with 100, 50, and 20 exemplar sizes (left to right).
Accuracy
S0ES
Number of Clasoses Leaar Learreed
Validation Accuracy
0.6
Accuracy Number of classes
Exposure
SVHN Accuracy Learned
CIFAR Accuracy
Leamned S0ES
10
Validatio Accuracy
Validation Accuracy
09
08
0.5
07
0.4
Nummber
06
0.5
05
75
125
150
Exposure No.
Exposure No.
Figure 11: Visualizations depicting iLAP's performance on the SVHN, CIFAR-10 10, and CIFAR-100 benchmark with a exemplar size of 50.
A.4
EXPERIMENTS WITHOUT PRE- TRAINING
CIFAR-100 Accuracy ES 200 without Pretraining
CIFAR-100 No. Classes with 200ES without Pretraining
imbalance
w/o class
Validation Accuracy
IOLICV (unsupervised)
BIC (Supervised)
No. Classes Learned
0.6
40
IAA w/ class imbalance
0.5
20
ILAP wio class imbalance
IOLCC (unsupervised)
0.4
BIC (Supervised)
75
100
125
150
175
50
75
100
125
150
175
Exposure No.
Exposure No.
Figure 12: Visualizations comparing accuracy (left) and number of classes detected (right) for the
CIFAR-101 100 benchmark without the use of pre-trained weights.
14


Reviews:
[{"review_id": "rgOKd2r-tiS", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 4, "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The paper studies an unsupervised class-incremental learning setting where a single class appears in each exposure, the classes can repeat and remain unknown during episodic training.  A set of exemplars is used to evaluate accuracy changes, based on which novelty is determined. The ideas is novel, but I is less scalable and the approach currently lacks key analysis and comparisons with the incremental learning methods and open-set approaches. \n\nPros`:\n+ An novelty detection approach that considers the changes in accuracy of the previous tasks as a new task is learned by assigning a new label to the incoming episode. A threshold value is then used to detect novelty.\n\nCons:\n\n- The basic intuition is that if a previous class is observed again, and the performance on old similar class will go down significantly. I feel this assumption is weak and can only be relevant in specialized cases, e.g., what if a very similar confusing class is observed? The currently evaluated datasets (SVHN, MNIST, CIFAR) do not consider such fine-grained cases. \n- The evaluations in comparison to SOTA incremental learning methods is insufficient. Only a single approach, BiC, is considered for comparisons. \n- The class imbalance based approach looks like a practical hack and is sensitive to the hyperparameters. \n- The propose approach will incur a high computational cost with training the model at each episode to detect novelty. The computational cost comparison is not performed in the experimental section. \n- The open set literature solves the same problem of OOD detection, however no comparison with SOTA methods is shown. I would recommend authors to check a nice survey on this topic: \"Recent Advances in Open Set Recognition: A Survey\"\n- The paper is not well-written. Fig. 2 comes before class-imbalance discussion.", "extra_scores": {}, "extra_text": {}}, {"review_id": "oSzYirz_04", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 6, "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper proposes to tackle the problem of unsupervised class-incremental learning, where the training data is composed of a sequence of \"exposures\". Each exposure is comprised of a set of images that pertains to a single class, where the class label is unknown while the boundaries between exposures are known. The key difficulty in such unsupervised class-incremental learning is to determine whether an arriving exposure belongs to what the classification model $L$ has learnt previously or is a novel one, thus relating to the problem of novelty detection. The proposed method address the novelty detection by an interesting idea: they always treat the current exposure as a novel class and use it to train the copy of classification model $\\hat{L}$ together with the training exemplars of previously-learnt classes, if the current exposure actually belongs to one of the previous-learnt classes, the confusion occurs to make the classification accuracy significantly decrease (over a threshold) on that specific class, where the accuracy is computed based on the validation exemplars. Moreover, a technique of introducing class-imbalance into such confusion-based novelty detection is proposed and helps to boost the robustness of novelty detection. \nThere are some pros and cons of this paper as listed below.\n\nPros:\n+ The idea of using confusion to address the novelty detection is novel and interesting, where the corresponding threshold is easier to be determined and contributes to better out-of-distribution performance in comparison to other related works of using static distance-based threshold. \n+ The introduction of class-imbalance works well with the confusion-based novelty detection and its contribution is experimentally verified on various datasets.\n+ The overall performance of the proposed method on unsupervised incremental learning is better than an unsupervised baseline (IOLfCV) and comparable to a supervised one (BiC).\n\nCons:\n- The figure.2 is a little bit difficult for understanding the properties of seen and unseen classes with respect to class-imbalance ratio $\\lambda$ at the first sight, e.g. why the curve of unseen classes would go up along with larger $\\lambda$? Perhaps it is better to replace the terminology of \"seen\" and \"unseen\" classes by \"repeated\" and \"non-repeated\" classes?\n- There is another closely-related type of incremental learning: unsupervised continual learning. Although its setting is more difficult than the unsupervised class-incremental learning which is tackled in this paper, it would still be nice to have the baselines of unsupervised continual learning for providing more insights to the readers. \n- As the mostly-related work of this paper is Stojanov et al., CVPR 2019 (also addressing the unsupervised class-incremental learning problem), why the CRIB dataset proposed by Stojanov et al. is not used for evaluation here in order to have more direct comparison?\n- Moreover, as indicated by Stojanov et al., the repetition of classes (e.g. how frequent a learnt class arrives again for learning) plays an important role for the model performance, there should be clear description on the experimental setting of repetition as well as the investigation on it in this paper. \n- Furthermore, in the paper of Stojanov et al., they experiment with the classification models of having pre-trained feature extraction or being learnt from scratch. However, in this paper only the classification model pretrained on ImageNet is adopted. There should be experimental results and corresponding discussion on having the classification model trained from scratch for better understanding how the proposed confusion-based novelty detection behaves. \n- Lastly, it is also important to investigate on the forgetting effect. When updating the classification with predicted label, are the techniques for avoiding catastrophic forgetting used (e.g. knowledge distillation)? If not, how the proposed method prevents the catastrophic forgetting from happening? If the forgetting does happen, will the confusion-based novelty detection still be working?\n\nIn brief, this paper proposes interesting idea of having confusion-based novelty detection approach to tackle the unsupervised class-incremental learning, but it needs more experiments and discussions to make the paper more complete and ready for ICLR. I would expect to see the concerns listed above being well addressed in the rebuttal.  ", "extra_scores": {}, "extra_text": {}}, {"review_id": "dgdDV7De7U-", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 2, "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This article proposes a method for predicting whether a batch of data is of the same class as one of the classes already seen by a classifier or whether it contains data from another class. The idea is to then be able to incorporate this batch to the previous training set, in an unsupervised learning context. It is assumed that each batch contains data from only one class. Experiments are there to show the interest of this method for anomaly detection or incremental learning.\n\nI find it difficult to formulate an opinion on this paper because I don't think I have managed to understand the detail of what is actually done. For example with regard to the detection of out of distribution data, the classic problem is whether a data is out of a distribution. Here it is not a data but a batch of data that is considered. I don't really see, under these conditions, how to compare to classical OOD methods. \n\nAs far as incremental classification is concerned, I don't understand the definition of the metric given in section 4.3 and therefore I'm not sure I understand what the task is really about. The fact that it's unsupervised makes it away from standard problems.\n\nIt seems to me that the paper lacks a clear definition of the tasks addressed and the means to evaluate performance. ", "extra_scores": {}, "extra_text": {}}, {"review_id": "_zxsTe5iLgb", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 3, "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The authors propose a novelty detection module to help unsupervised class-incremental learning. The novelty detection relies on the percentage of accuracy drop during a model update when treating incoming data as a new class. If the model maintains high accuracy, then the module treats the incoming data as familiar, thereby choosing one of the existing classes as the correct label. The paper investigates the effectiveness of the proposed method on MNIST, SVHN, CIFAR-10, and CIFAR-100.\n\nThe main weakness of the submission might be that the proposed novelty detection is not well motivated. The bottleneck of the proposed pipeline comes down to whether the accuracy drop on the selected subset is a good indicator of out-of-distribution (OOD) detection. The submission does not provide theoretical insights nor direct references that show it is actually the case. In fact, in the experimental section (Sec 4.1), the authors have to use several different accuracy threshold settings (0.46, 0.63, 0.57, 0.62) for different datasets, demonstrating that accuracy drop might not be a reliable indicator.\n\nBesides, the direct competing method CURL (Rao et al.) is cited but not compared. iCarl [R1] should be quite related as well. The authors also use quite a different backbone network (ResNet-18) than other competing methods. Therefore, it is hard to justify whether the proposed approach is more effective than other baselines. \n\nThe method described here is also quite similar to the field of active learning. It would be great to discuss the relationship between the proposed novelty detection and other active learning literatures.\n\nSec 1 first sentence “continually learning systems remains to be a major obstacle in the field of artificial intelligence” is quite a strong statement. I believe there are other major obstacles in AI and they should be discussed as well.\n\nSec 1 paragraph 3, “an agent must conduct two procedures successfully”. The authors do not clearly define what is an “agent” in the context. It is hard for the readers to follow through the manuscript.\n\nSec 3.4, “After obtaining the correct label”. Actually the label technically is not “correct” but assumed to be correct for the class-incremental learning.\n\n[R1] Rebuffi et al. Icarl: Incremental classifier and representation learning. In CVPR 2017.\n", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper presents a continual learning method based on a novelty detection technique. All reviewers are concerned about various issues, especially, motivation, experiment, and presentation. One of the reviewers was initially positive about this paper but downgraded his/her score due to unresolved problems in the proposed method. Considering all the comments and communications with the authors, AC believes that this paper is not ready for publication yet.
Decision: Reject