Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks
Authors: Vijaya Raghavan T Ramkumar
Elahe Arani
Bahram Zonooz
Venue: Accepted by TMLR ()

Abstract: Deep neural networks (DNNs) are often trained on the premise that the complete training data set is provided ahead of time. However, in real-world scenarios, data often arrive in chunks over time. This leads to important considerations about the optimal strategy for training DNNs, such as whether to fine-tune them with each chunk of incoming data (warm-start) or to retrain them from scratch with the entire corpus of data whenever a new chunk is available. While employing the latter for training can be resource-intensive, recent work has pointed out the lack of generalization in warm-start models. Therefore, to strike a balance between efficiency and generalization, we introduce "Learn, Unlearn, and Relearn (LURE)" an online learning paradigm for DNNs. LURE interchanges between the unlearning phase, which selectively forgets the undesirable information in the model through weight reinitialization in a data-dependent manner, and the relearning phase, which emphasizes learning on generalizable features. We show that our training paradigm provides consistent performance gains across datasets in both classification and few-shot settings. We further show that it leads to more robust and well-calibrated models.

Published in Transactions on Machine Learning Research (01/2023)
Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks
Vijaya Raghavan T. Ramkumar', Elahe Arani Bahram Zonooz
mijayarrakkuman rwwkuman waanurrraawiffoo.o eu, fe.arani, bahram. z0n00)) @gmaai.com Advanced Research Lab, Navlufo Europe, Eindhoven, The Netherlands
Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands Contributed equally.
Reviewed OpenReview: https: https://oppnrevveew openreview. net/ ptt/formm? id= WN102MJDST
Abstract
Deep neural networks (DNNs) often trained on the premise that the complete training data set is provided ahead of time. However, in real-world scenarios, data oflen arrive in chunks over time. This leads to important considerations about the optimal strategy for training DNNs, such as whether to fine-tune them with each chunk of incoming data (warm-starr) or to retrain them from scratch with the entire corpus of data whenever a new chunk is available. While employing the latter for training can be resource-intensive, recent work has pointed out the lack of generalization in warm-start models. Therefore, to strike balance between efficiency and generalization, (LURF) online learning paradigm for DNNss LURE interchanges between the unlearning phase, which selectively forgets the undesirable information in the model through weight reinitialization in a data-dependent manner, and the relearning phase, which emphasizes learning on generalizable features. We show that our training paradigm provides consistent performance gains across datasots in both classification and few-shot scttings. We further show that leads to more robust and well-calibrated models
introduce Learn, Unlearn, and Relearn
1 Introduction
"A liltle learning is a dangerous thing." -Alexander Pope
In recent years, supervised learning has achieved human-level performance in many computer vision tasks in which the learner is trained in an offline learning environment with fixed set of training data. However, DNNs deployed in the real world are expected to work in an environment where the data arrive in a sequence of large chunks (mega-batches). Several learning paradigms have been proposed to learn from a stream of data, including, but not limited to, continual learning (Van de Ven Tolias 2019 Thrun 1995). activv
online learning (Settles, 2009), and anytime learning (Grefenstette & Ramsey 1992 Caccia et 2022 Although previous efforts established a solid theoretical foundation, certain subtle issues make it inapplicable to the practical environment.
For an online learning system, it is important that the learner produces high accuracy and generalizes well at any point in time while using limited computational resources (Caccia et al. 2022). Recent research in online learning, however, has shown that training from a previously trained model (warm-start rather than fresh initialization) hinders its ability adapt to new input (Achille et al.] 2018). thus incapacitating the generalization of DNNs (Ash & Adams] 2020 Caccia et al. 2022p. These implications of warm-starting have also been obscrved in online active learning (Huang) 2021 Sener & Savarese 2017 Ash et al., 2019), where they mitigate it by retraining from scratch after every selection. However, training DNNs from scratch every time new data arrives is resource intensive, and the lack of generalization with warm-starting undermines the
The official code available at: https: htmps:///gtten www.ttaa..cm//euurA---A--..

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benefits of training with learned features (Ash & Adams 2020 Thus, the failure of current online learning systems to generalize across data streams without bartering previous computation presents striking lacuna for the large-scale deployment of machine learning systems.
Humans, on the other hand, learn in succession over their lifespan and readily generalize by applying prior knowledge to novel situations and stimuli without the need to learn from scratch. This in the brain is facilitated by a complex set of neurophysiological processes (Goyal & Bengio, 2020). One of such glaring aspects of the brain that allows humans to generalize better is the inherent process of active forgetting (Hardt)
et al. 2013 Davis & Zhong 2017). It plays an active role in the regulation of the learning process to achieve better generalizability in the real world. The growing evidence in neuroscience and cognitive psychology
Gravitz 2019 Izawa et al. 2019)) suggests that the brain actively forgets through the selective extinction of neurons, which shapes the learning-mentn memory process and, therefore, prevents humans from overfitting to experiences (Shuai et al. 2010). Thus, emulating this aspoct of selective forgetting might hold the key to improving generalization in DNNs.
Therefore, we propose general online learning paradigm, which we refer to as Learn, Unlearn, and RElearn (LURE), to address the problem of generalization of parameterized networks. For simplicity, we focus mainly on an online learning scenario in which models achieve good performance at any point in time, termed Any- time Learning (Caccia et al. 2022 We consciously simulate the process of selective forgetting (unlearning) in the DNNs by re-randomizing a subset of weights before training on the new samples. With extensive experiments on multiple datasets, we show that our proposed training paradigm boosts the performance and generalization of the models to a greater extent. Compared to standard online training, LURE significantly improves the robustness of DNNs in tackling more challenging real-world scenarios. Our main contributions are as follows:
'Learn, Unlearn, and Relearn' (LURE), an online training paradigm to improve the performance and generalization of DNNs through the lens of selective forgetting.
We demonstrate the efficacy of LURE in multiple convolutional architectures across different datasets in online learning and a few-shot classification scenario.
LURE exhibits robustness in solving more common challenges in real-world problems, including learning with noisy labels, natural corruption, and adversarial attacks.
LURE is robust to changes in hyperparameters and leads to well-calibrated models.
2
Related Work
Lifelong learning (Thrun 1995 and anytime learning Grefenstette & Ramsey 1992) have gained increasing atlention from the deep learning community due to its relevance in practical settings. Recent research on online learning, (Caccia et al.) 2022 Ash & Adams 2020), has noticed lack of generalization in DNNs when trained in online scttings. Ash & Adams (2020) points out that if model is finctuned from pre- trained model (a 'warm-start"), the resulting new model performs worse than a model trained from scratch (a 'cold-start") even though the new data is sampled from the same distribution as the previously trained data. Thus, the lack of generalization in DNN renders them inapplicable to real-world scenarios.
Recently, several weight reinitialization methods (Taha et al. 2021 Li et al. 2020 Alabdulmohsin et al, 2021 Ash & Adams (220 Zhou et al. ) have been proposed to improve the generalization performance of DNNs by partially or fully refining the learned solution. Zhou et al. (2022) propose a forget and relearn hypothesis to unify disparate existing iterative algorithms under the lens of forgetting. Their approach is based on the consideration that early layers learn generalized representation, whereas later layers memorize. Therefore, they reinitialize and retrain the later layers of the model repeatedly, thereby crasing the infor- mation pertaining lo the memorized difficult examples. Similarly, Ash & Adams (2020) propose a met to improve generalization by shrinking the magnitude the weights and perturbing it by injecting small noisc. However, these weight reinitialization mothods have architecture-speeffe assumptions independent of the data and are handled based on the assumed properties that are inherent to the model and its learning. These methods lack priori knowledge of where and what features, layers, etc. should be reinitialized in
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the general case. Therefore, we propose online training paradigm, to improve the generalization of DNNs through the lens of active forgetting.
3 Method
We propose 'Learn, Unlearn, and Relearn' (LURE), a training paradigm for learning from a sequence of data, which alternately interchanges the unlearning (selective forgetting) and relearning steps. Our proposed online training paradigm consists of three steps: a) learn, b) unlearn, and c) relearn. Our proposed approach is illustrated in Figure [ and is detailed in the Appendix (Algorithm
Learn. We define the Anytime Learning at Macroscale (ALMA) learning environment envisioned in Caccia et al. (2022) where the authors focus on real-world settings. ALMA is a new sub-paradigm of learning from sequential data inspired by anytime learning (Grefenstette & Ramsey
1992) and transfer learning [aan & Yang, 2009). Data is provided to the learner in the form of a stream SB consisting of consecutive batches of samples. Therefore, we also focus on the general classification problem, where data are sampled from an undorlying data distribution Da, with input x E R. and label y E fl....Cc..
Let Mi be collection of N >> 0 in-distribution samples randomly selected from Da.y for i i {1, 0. The stream is then defined as the ordered sequence Sg MM, We refer to each dataset M; mega-batch, as it is composed of a large number of samples. Consider model fo RD - (1,....C) CH updates its parameters by processing a mini-batch of n < N examples at the time of each mega-batch M in such a way as to minimize its objective function. Since the data are passed as a stream, the model does not have access to the future mega-batcher and is limited to one pass through the entire stream. However, the model might make several passes over the current and some previous mega-batches depending on the avaílable computational budget. Tn ALMA, it is assumed that the rate at which mega-batches arrive is slower than the training time of the model on each mega-batch, and therefore the model can iterate over the mega-batches at its disposal based on its discretion to maximize performance, resulting in overall data distribution that is not i.dd. by the end of the stream. This implies a trade-off between effectively generalizing and learning from the current data at each mega-batch. Therefore, in such settings, we train the randomly initialized network fegcinis on a mega-batch M, belonging to the data stream Da.y for e epochs until convergence. The loss function employed for learning is defined as follows:
l (a.y)~M;
(ee (ar (Jo (x))
where Lee is a cross-entropy loss, t is the number of mega-batch sequences, and a is the softmax function.
Unlearn. Motivated by the symbiotic link between generalization and active forgetting in biological neural networks (Gravitz] 2019 Davis & Zhong, 2017), introduce unlearning step in which the network selectively forgets the connections that are less relevant for the current mega-batch and retains those that are specific for the current mega-batch. We quantify the sensitivity (importance) of each connection in the network in a data-dependent manner to identify task-specific connections. We employ SNIP (Lee et al., 2018), which harnesses the sensitivity of the connection by decoupling the weight from the loss function to find relevant connections. To determine the sensitivity of the connection, we sample a small subset of data from the current mega-batch (TT MM; |x, a x [M,) where is the percentage of data be used as subset). In our case, following Misra al. (2022) use 0.2, however, we can also samples low as 128 (Lee et al. 2018) to estimate the connection sensitivity. We define connection sensitivity mask M E {0, 13th TT that is proportional to the number of parameters (m) in the network. Then we apply sparsity constraint l that specifies the percentage of parameters that must be retained. We compute the connection sensitivity as follows:
Lee ( 3 B:
Lee ((M 5e,) (:)))
():
x) = lim 8
(2)
8
M=1
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T
M
Mi
Dateorwam
Me
fo R
fo
Is+
LURE
LURE
Subsos from
Sucset from
(e)
(b)
(c)
M,
(e)
(b)
(c)
Randomly initialized
Task-specific parameters
O
Unlearned parameters (selective forgetting)
Trained
(a) Identify task-specific parameters; (b) Unlearn task-sp parameters; Reinitialize the unlearned parameters
Schematics of the proposed LURE framework. LURE alternates between the unlearning phase, which selectively forgets the undesirable information in the model through weight reinitialization, and the relcarning phase, which emphasizes learning generalizable features.
Figure 1:
where j corresponds to the parameter index and Ej is the mask vector of the index where the magnitude of the derivatives is then used to calculate the saliency criteria (sz):
(0; 8j Σ k= (x: x)
Following the saliency computation, the connection sensitivity mask is set only retain the top-k task- specific conncctions based on the sparsity constraint & which is given follows:
M, = - Vj E
where š is the bthh largest element in the saliency vector and 1[.] is the indicator function.
Then, based on the salicncics pertaining to the connection sonsitivity, we retain the top-k important con- nections and unlearn the parameters that are not important for the current data. Thus, we induce active forgetting through reinitialization of the connections that are less desirable for the current mega-batch. Finally, the network parameters pertaining to unlearned connections are reinitialized to random values:
{0r
if M, =1
0 = nuea
(3)
OReinit 0R
Otherwise
where are the weight parameters for the previous mega-batch and Reimas corresponds the random initialized value sampled from a uniform distribution. The network with the new paramcters, Bnews then trained new consecutive mega-batch.
Relearn. In this stage, the network with the new initialization (fe...) is updated with the new incoming data M,+1 (in case of no-replay), or with the joint of all the soen data MHz1 U UM; (in case of full-replay) for the e epochs, where is kept the same for each iteration. The network is trained with the loss function shown in Equation [ The unlearn and relearn phases are alternately repeated after the completion each mega-batch training. Thus, by alternating between unlearning and relcarning, we favor the preservation of the task-specific connections that can guide the network towards those desirable traits that efficiently improve performance and generalization.
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4 Experimental set-up
We evaluate our LURE framework on CIFAR-10 (Krizhessky al. 2009).
CIFAR-100
(Krizhevsky et al.
2009), and Restricted Imagenet. (balanced) Myas et al.
2019: Tsipras et al. 2018).
Anytime learning settings. Following Caccia et al. (2022) create the standard for ALMA evaluation using the datasets mentioned above: (1) The training set is randomly divided into [Sgl mega-batches,, each containing an equal number of training instances. For CIFARIO and CIFAR100, we keep [Sal = 8, but for Restricted ImageNet, we keep [Sp| = 3. However, we also perform long sequence experiments on CIFARIO with [Spl 25, 50, 100 to analyze the effectiveness of the proposed approach. (2) We use 10% of the data from each mega-bat batch as a mega- mega-batch batch validation set. Following Caccia et al. (2022). we train each mega- batches for 50 epochs. A test set is employed to evaluate the model's performance it observes the data. Note that this is not used for validation purposes; it is solely used for final reporting. For implementation details and hyperparameters, please refer to Appendix Section A.5
Baselincs. We benchmark our proposed framework against (1) Baseline (BL), a warm-start model, which is continuously trained from the checkpoint of the previously trained model in ALMA settings (without reini- tialization) as proposed by Caccia et al. (2022). (2) Cold-start model (Random init.) trained completely from scratch with each advent of the incoming data. (3) RIFLE (Li et al.) (200)), where the fully connected layer is reinitialized and retrained during transfer learning. (4) Shrink and Perturb (S&P) (Ash & Adams,
2020) which is proposed for online learning, and (5) Later-Layer forgetting (LLF) (Zhou et al. 2022 which
is proposed to improve generalization in the small data regime.
Metrics. For a thorough evaluation, we use CER along with the test accuracy and the generalization gap.
Cumulative Error Rate (CER): This can be defined as follows:
M. TTad
CER = Σ Σ (z)
(4)
where Tz.y represents the held-out test set, f trained on M, y the ground truth label. A model must have lower CER at each mega-batch of training with data stream in order to be effective anytime learner.
Generalization gap: We use the standard generalization gap as a measure to understand whether the model overfitting or underfitting at anytime learning which is given by the difference between training and validation accuracy.
5 Results
5.1
Analysis of Short Sequence
Table shows the results of the ResNet18 model training on multiple datasets with and without different forms of reinitialization. All experiments on CIFAR10 and CIFAR100 were carried out using full replay (Sp = U._ M,)) for total of 8 mega-batches with each mega-batch containing 6250 samples, while the experiments on Restricted ImageNet are performed for [Spl = 3 mega-batches. Our observations from Table are follows: (1) Reinitiallization-aasee
training for online learning improves test accuracy, CER, and generalization to greater extent consistently the three datasets compared to warm-start (BL) and cold-start (Random Init.) models. (2) LURE outperforms the bascline by 4.8%, 6.64% and 4.13% on CIFAR1O, CIFAR100, and Restricted ImageNet respectively, and shows the strongest performance on all datasets compared to the other reinitialization methods. (3) Online training using LURE results in the lowest CER and generalization gap compared to other mothods, improving the model's anytime learning capabilitios. Thus, selective forgetting undesirable information through weight reinitialization brings discernable benefits to the model in online settings.
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Table 1: Evaluation of the model (Resnet18) trained with various reinitialization methods ALMA settings. CIFAR10 and CIFAR100 were trained sequence of [Syl = 8, while restricted ImageNet was trained with | 3 mega-batches.
Datasets
Methods
Test Accuracy (t)
CER (4)
Generalization Gap (4)
BL
89.47 20.51
11760
8.98
Random init.
92.22 +0.07
12323
7.56
RIFLE
90.08 t0.37
11844
6.98
CIFAR10
LLF
91.43 10.70
10103
7.22
S&P
91.76 10.26
10206
7.72
LURE
93.32
9622
6.60
BL
62.96 20.53
39010
31.54
Random init.
64.53 +0.61
37253
27.23
RIFLE
61.38 10.26
39302
27.81
CIFAR100
LLF
67.04 10.05
34575
21.23
S&P
64.48 10.11
36303
28.58
LURE
69.60 +0.82
33037
22.37
BL
81.39 40.45
2967
4.90
Random init.
82.87
2852
4.88
RIFLE
82.05 1:.22
2722
4.63
Restricted ImageNet
LLF
82.10 t9.88
2854
4.88
S&P
80.80 1:033
2996
5.10
LURE
85.52 +00.2
2699
4.84
5.2 Analysis of Short Sequence with Buffered/No Replay
We investigate real-world scenario in which access to the entire dataset used to train the model is restricted due to data privacy or memory restrictions. Tables and 3 show the results of training the ResNet18 model on multiple datasets with buffered replay (buffer size -187 and without replay, respectively. All experiments on CIFAR10 and CIFAR100 were carried out for a total of mega-batches with each mega- batch containing 6250 samples, while the experiments on Restricted ImageNet. are performed for |Sa] 3 mega-batches. LURE with and without buffered replay consistently outperforms baselines and other methods across all datasets. For example, LURE with buffered replay improves performance by 1.7%, 9.4%, and 2.5% over standard training (BL) on CIFAR10, CIFAR100, and R-ImageNet, respectively. Moreover, that the performance of the cold-start models (Random Init.) is below par in low buffer and no-reply sottings compared to full-replay setting (Table D as the model does not have access to past knowledge or data. In the challenging case of no-replay settings, our proposed method has a profound impact on standard training compared to other reinitialization methods. This demonstrates that the observed benefits of LURE are not limited to replay-based methods alone. Thus, unlearning and relearning each mega-batch of training help to learn more generalizable representation in different replay scenarios.
also observe
5.3 Analysis of Moderate and Long Sequence (|Sal 25, 50, 100)
Computational systems deployed in the real world are often exposed longer mega-batch sequences of data and need to be updated frequently. Therefore, it is quintessential for the online model to perform well under longer mega-batch sequences. Table 4 shows the results of the ResNet18 model training the CIFAR10 datasct. All experiments were carried out using full replay for longer sequence of mega-batches 25, 50, and 100. We observe that LURE consistently outperforms the baseline and other methods across varying sequences of
As the sequence of mega- batches increases, the number of samples available per mega-batch roduces drastically. Similar to Caccia al. (2022), we observe that regularly updating the model on fewer samples significantly exacerbates CER, resulting in poor Anytime learning performance. Therefore, long-sequence online learning with reinitialization, especially LURR,, reduces the CER and the generalization

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Table Evaluation of the model (ResNet trained with buffered replay (buffer size=186) ALMA settings.
CIFARIO and CIFAR100 were trained in a sequence |SBl = mega-batch, while restricted ImageNet was trained for [Sp] 3.
Datasets
Methods
Test Accuracy (t)
CER (4)
Generalization Gap (4)
BL
88.01 20.83
15784
13.01
Random init.
75.26 +0.07
22276
17.79
RIFLE
85.58 t0.55
14748
13.50
CIFAR10
LLF
87.51 10.82
15045
13.22
S&P
84.62 10.74
15549
14.72
LURE
89.8
13629
11.92
BL
62.68 20.74
39178
31.33
Random init.
58.36 +0.42
40921
27.14
RIFLE
60.44 10.29
40040
28.19
CIFAR100
LLF
67.29
34416
24.86
S&P
65.07 10080
36040
27.79
LURE
72.04 +0.35
33037
26.59
BL
75.97 20.56
3284
8.72
Random init.
61.47
4023
5.41
RIFLE
77.90 10.
3324
8.42
Restricted ImageNet
LLF
76.40 d9.00
3245
9.07
S&P
74.75 10.38
3857
8.20
LURE
78.58 +00.4
3376
7.94
gap to a greater extent compared to standard training, thus enriching the predictive capabilities of the model at any given time.
5.4 Analysis of Few-Shot Experiments on Restricted ImageNet
For many real-world classification problems, machine learning models deployed often need to be updated labeled data that are scarce and may not be initially available for training. It is possible for new sets of labeled data to become available gradually as they are labeled. Therefore, it is important for the system to function properly in such online few-shot settings. These experiments are performed on the Restricted ImageNet dataset and quantitatively evaluated with the same metric described in Section 4 We use the same hyperparameters (epochs, Ir scheduler). backbone architoctures (ResNet50) and optimizer as used in the experiments in ALMA settings (see Appendix Section A.5/. However, following Misra et al. (2022). we limit the availability of samples pertaining to the classes to 270. Note that do not any techniques proposed in the few-shot literature to boost performance. Table shows the results of the methods in few- ion-based training
shot settings where we vary the sequence of mega-batches. We observe that improves performance and generalization over the baseline, even in challenging few-shot classification. For a mega-batch sequence 70, LURE outperforms baseline, LLF, and S&P by a relative improvement of 4.11%. 2.01%. and 3.15%, respectively, while for sequence of 30, it is on par with LLF terms of accuracy. In addition, LURF outperforms both the important baselines (BI and Rand. Init.) comfortably. In both settings, LURE achieves the lowest CER and the generalization gap, demonstrating the superiority of our proposed approach.
5.5 Analysis on Various Architectures
Here, we examine the versatility our proposed LURE framework for multiple architectures on the CIFAR10 datasct. We consider ResNet18 (He al. 2016)). ResNet50 (He et al. 2016). Sider-Resnet50-2 (Zagoruyko & Komodakis 22166), VGG16 (Simonyan & Zisserman 2014). We chose these models explicitly because of their widespread popularity in common computer vision tasks and the breadth of research done them for

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Table 3: Evaluation of the model (ResNet18) trained without replay ALMA settings. CIFAR1O and CIFAR100 were trained in a sequence of SSl 8 mega-batch, while restricted ImageNet was trained for [Sa] 3.
Datasets
Methods
Test Accuracy (1)
CER (4)
Generalization Gap (4)
BL
80.86 11.01
16789
16.85
Random init.
73.56 +0.45
22731
17.17
RIFLE
86.35 t0.14
14536
15.53
CIFAR10
S&P
81.92 20.02
16133
16.36
LLF
86.11 10.59
15294
14.66
LURE
88.96
13953
12.14
BL
48.79 19.79 20.75
44128
49.95
Random init.
35.50 +0.66
55221
67.03
RIFLE
45.65 10.42
44113
51.46
CIFAR100
LLF
50.38 10.54
44816
50.80
S&P
49.26 10009
44581
51.45
LURE
55.37 #0.63
43348
46.69
BL
73.36 20.88
3450
9.68
Random init.
60.87
4179
7.18
RIFLE
74.87 10.21
3375
8.36
Restricted ImageNet
LLF
76.40 $9.06
3515
8.15
S&P
73.36 10.78
3464
8.13
LURE
77.97 +00.88
3367
7.90
Table Evaluation of the model (ResNet18) on CIFAR10 for longer sequences of mega-batches.
# Mega-batches batches
Methods
Test Accuracy (t)
CER (4)
Generalization Gap (4)
BL
89.94 +0.54
43029
6.80
Random init.
89.89 20.38
43529
4.86
RIFLE
90.35 20.54
41308
5.55
LLF
89.80 +0,92
42961
6.52
S&P
88.37 #0.26
43578
5.94
LURE
90.55 10.34
42790
6.79
BL
89.12 +0.01
87843
6.02
Random init.
89.62 20.42
91040
6.02
RIFLE
89.2 29.2520.58
91426
6.16
50
LLF
90.26 10.89
87826
5.75
S&P
88.32
85798
6.11
LURE
90.97
85487
5.18
BL
89.64 $0.09
176954
6.46
Random init.
$9.29 10.38
188978
5.61
RIFLE
89.93 10:27
170678
6.02
100
LLF
89.48 20.77
173505
7.21
S&P
88.01 +0.44
182294
5.56
LURE
91.95 20.53
170178
5.66
different learning paradigms. o the performance and generalization gap of the model trained different architectures. The experiments are performed with full replay for |5p The results demonstrate that LURE significantly outperforms standard training across multiple architectures while the LLF and S&P fail to improve. Therefore, reinitializatinn of the weight parameter in data-dependent manner using

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Table 5: Few-shot experiments using ResNet50 on Restricted ImageNet.
# Nega-batccees
Methods
Test Accuracy (t)
CER. (4)
Generalization Gap (4)
BL
45.82 +0.56
73514
47.93
Random init.
45.79 20.57
73497
45.77
RIFLE
44.58 +0.48
74071
46.30
30
LLF
47.60 10.61
72868
43.50
S&P
45.92 #0.35
72954
45.61
LURE
46.97 #0.79
71542
42.56
BL
53.43 +0044
156332
41.17
Random init.
54.20 10.68
150473
38.17
RIFLE
53.25 20.52
153716
36.31
70
LLF
54.53 10:68
149493
32.57
S&P
53.90 +0.24
150332
40.87
LURE
55.65 20.52
148478
28.35
Table 6: Evaluation of methods using different architectures on CIFAR10 dataset (|Sg) 4).
Architechture
Methods
Test Accuracy (f)
CER (1)
Generalization Gap (4)
BL
89.31 40.01
5657
7.90
Random init.
92.78 10.31
5459
7.56
RIFLE
92.11 10.35
5378
7.77
18sNet18
LLF
91.80 10.m
5466
7.68
S&P
90.50 #0.61
5667
7.80
LURE
93.73
4409
7.48
BL
89.53 +0.58
6854
7.83
Random init.
91.87 10.86
7539
6.78
RIFLE
90.51 40.38
6740
6.95
ResNet50
LLF
89.49 10.00
6782
6.78
S&P
89.15
6940
6.98
LURE
92.75 20.73
6682
6.62
BL
90.10 40.89
5978
6.84
Random init.
92.23 40.27
6615
6.73
RIFLE
89.98 10.42
5953
6.02
2
LLF
89.72
6000
5.70
S&P
89.38 +0.01
6292
5.96
LURE
93. 78 2.054
5557
5.81
BL
89.32 44.74
5650
9.62
Random init.
91.7
5913
7.22
RIFLE
$8.25 10.38
6083
6.89
VGG16-BN
LLF
87.85 40.08
6124
6.17
S&P
88.25 +0.96
5720
8.11
LURE
92.67 +0.47
4439
8.55
connection sensitivity by LURE more effective improve generalization in different architectures than reinitializatio based on assumed model properties and learning (as done by LLF and S&P)

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Noise
Blur
Weather
Digital
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69.3
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88.8
S&P
71.3
52.5 61.7 59.0
63.9
77
47.9 65.1 72.0 69.6
90.5
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67.2 89.5
46.3
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55.1
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79.9 77.5
mCA
Gaussian
Shot
Impluse
Speckle
Defocus
Glass
Gaussian
Motion
Zoom
Brightness
Snow
Frost
Fag
Spatter
Contrast
Elastic
JPEG
Pixelate
Saturate
Figure 2: Robustness to natural corruptions on CIFARI0-C (Hendrycks & Dictterich robust against the majority of corruptions compared lo other reinitialization methods.
2019
LURE is more
6 Robustness Analyses
6.1 Robustness to Natural Corruptions
In practice, DNNs are often deployed in real-world scenarios where they are exposed to constantly changing environments, often influenced by changes in lighting and weather. Therefore, the robustness of DNNs data distributions that are subject to natural corruption is pertinent. Here, we evaluate the benefit of LURE robustness to common corruption using CIFAR10-C (Hendrycks & Dietterich 2019). Models are
trained clean images and tested on CIFAR1O-C. Following Hendrycks & Dietterich (2019) use the
mean Corruption Accuracy (mCA) to measure performance under natural corruption. Figure 2 shows the accuracy of the models on 19 different corruptions averaged on five severity levels. Compared to baseline (68%), Random initialization (69.3%), RIFLE (64.7%), LLF (72%), and S&P (71%), LURE (74%) delivers a higher mCA types of corruption. Evidently, unlearning and relearning at each mega-batch of training bring discernible benefits in terms of robustness natural corruptions.
6.2 Robustness to Adversarial Attacks
DNNs have been shown to be vulnerable to adversarial attacks in which imperceptible perturbations are added to inputs during inference. The adversarial images are designed to fool the network to make false predictions (Szegedy et al. 2013). We perform a PGD-10 attack (Madry et al. 2017) on models trained on the CIFARIO dataset with varying attack strengths. As observed Figure 3 [Teft), LURE exhibits greater resistance to these attacks of varying strengths. Thus, compared standard training, training model in the LURE framework facilitates online learners to learn high-level abstractions that are not sensitive to small perturbations in the data.
6.3 Robustness to Noisy Labels
The success of supervised learning often depends on the availability of large amounts of high-quality anno- tations. However, the availability of high-quality annotated datasets can be extremely expensive and time consuming to collect. Therefore, it is paramount to have robust training noisy labels, as studies have shown that DNNs can easily memorize samples and susceptible to noisy labels (Arpit et al. 2017). we train ResNet18 on sequential CIFAR10 with noisy labels for a mega-batch sequence of [Sp] 4 and evaluate the performance on a clean test. set. We corrupt every ground truth label with a specified probability (noise rate) by randomly sampling from a uniform distribution over a large number of classes. Figure 3 Riight presents the test accuracy of the reinitialization methods under different percentages of noisy labels. The results show that the reinitialization- based training paradigm robust to the presence of noisy labels during training compared to the warm-started baseline. Furthermore, LURE consistently outperforms the L.I., RI-
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Adversarial perturbation
Noisy Training
97.5
BL
16
95.0
Rand. Init.
Adversarial Accuracy
RIFLE
92.5
14
LLF
Accuracy
90.0
S&P
12
LURE
87.5
85.0
82.5
6
80.0
0.25
0.5
4
20
40
Attack Strength (Epsilon)
Noisy labels (%6
Figure 3: (Left) Robustnoss to adversarial attacks; (right) Robustness to training under noisy labels. In both robustness analyses, LURE shows significant performance improvement compared to the baselines considered.
Block-1
Block-2
Block-3
Block-4 & FC
1-2
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Mega-bal see-batchhss hes
98.0
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Layers ResNet18
Figure 4: Layer-wise percentage overlap of the retained parameters in consecutive mega- mega-batches batches in the full- replay scenario. The results for the no-replay and buffered replay scenarios are provided in Figures 9 (a).). respectively.
FLE, and S&P baseline comfortably across different noisy label rates. Thus, reinitializing based on selective forgetting helps to learn generic representation that is less sensitive to noise in the dataset.
6.4 Robustness of Connection Selection across Training Steps
The proposed LURE framework is based on the selection of connections (that is, SNIP (Lee et al. 2018 to selectively forget the parameters that have the least impact on performance at each mega- batch of training. Therefore, important evaluate the consistency of the connection selection across the sequence mega- batches. We conduct this analysis on the short sequence Anytime learning sccnario (full replay, ((S5b = 2)) CIFAR10 with ResNet18. We globally retain 80% and reinitialize (unlearn) 20% of the parameters iteratively the end of each mega-bate batch of training.
mega-batch
To study the consistency of the retained connections, we save the connection sensitivity mask (M) containing ones and zeros for the parameters retained and to be reinitialized, respectively. Visualizing the connection sensitivity mask can be challenging, as the parameter count in each layer the backbone is overwhelming, Therefore, we calculate the percentage overlap of retained parameters between the connection sensitivity mask generated the end of consecutive mega batch training a metric to analyze the connection con- sistency. Figure 4 shows the layer-wise percentage overlap of the retained parameters across consecutive
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mega-batches. The overlap percentage of retained connections is quite high in the earlier layers across all mega-batches, while it decreases in the later layers (layer 4 in ResNet) as it learns class-specific information pertaining to the new (unseen) samples. Although the overlap percentage is lower in the later layers, our method selectively unlcarns few parameters in the earlier layers as well depending on the incoming data. This flexible nature of our method for unlearning and regulating connections layers facilitates improved generalization in Anytime setting- This result is consistent not only in the full replay, but also in buffered and no-replay scenarios of anytime learning. The results for the no-replay and buffered replay scenarios are provided in Figures 9 [a.),, respectively.
both the latter and early
6.5 Sensitivity Hyperparameters
Machine learning systems often deployed in the real world, where explicitly rumning a hyperparameter search for each update can be computationally exhaus- tive. Therefore, similar to Zaidi et al. (2022). we explore the sensitivity of our method to the choice of weight de- cay and learning rate. Figure [ shows the test accuracy achieved by changing the learning rate and the weight decay values intended for CIFAR-10 training in ALMA scenarios for the mega-batch sequence of [Sa] = 4. Com- pared to baseline training without reinitialization, LURE is less sensitive to the choice of hyperparameters. A de- tailed comparison with other methods provided in Ap- pendix, Figure g. For example, the performance of normal training decreases to 75% with learning rate of 0.005 and weight decay of 0.001, while the performance of LURE remains above 94% throughout. Therefore, LURE can improve generalization in regimes where it is infeasible to perform exhaustive hyperparameter tuning.
BL
LURE
Weight decay (x110-1
91.5
91.9
91.5
91.0
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92.8 92.5
92.5
0.5
1
0.5
10
Learning rate (x1002)
Learning (x10-2)
Figure 5: Sensitivity to hyperparameters. LURE is more robust to the changes in weight decay and learning rate to standard online training.
We underline that LURE's effectiveness exceeds that of other reinitialization techniques and that it should be viewed as a seneroa-purpose online training paradigm, as it is more resilient to typical problems found world datascts than the conventional online training method. Extensive experiments on the model char- acteristic analyses, such as model calibration and convergence to flatter minima, are provided in Appendix Sections
analyze the robustness of connection selection during training steps and also across experiments trained with different learning rates. Finally, we have added a discussion section comparing LURE with other baselines and the benefits it brings in practical settings where the training budget is limited.
A.3 and A.4 respectively. Furthermore,
7 Conclusion and Future work
We introduce Learn, Unlearn, and RElearn (LURE), an online training paradigm to improve DNN perfor- mance and generalization through the lens of selective forgetting. LURE alternates between the unlearning phase, which selectively forgets undesirable information in the model, and the relearning phase, which em- phasizes learning generalizable features. Empirical results show that the proposed framework improves per- formance and generalization across a wide range of architectures and datasets, both online and in challenging few-shot classification. Our framework is robust to learning with noisy labels and adversarial attacks and increases generalization in many real-world scenarios. One advantage of our work is that have observed distinct empirical tendencies when re-initialization succoods. In future work, it would be interesting to study the dynamics of reinitialization in other lifelong learning scenarios, such as continual learning, where domain shifts and catastrophic forgetting are more common. Furthermore, studying activation-base selection may help us selectively identify and retain the most important weights in online learning settings. Further research in these areas may provide a more in-depth theoretical explanation for why reinitialization succeeds or fails.
connection
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References
Alessandro Achille, Matteo Rovere, and Stefano Soatto. Critical learning periods in deep networks. In International Conference on Learning Representations, 2018.
Ibrahim Alabdulmohsin, Hartmut Maennel, and Daniel Keysers. The impact reinitialization on general- ization in convolutional neural networks. arXiv preprint 0rXiv:2109.0020 2021.
Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kan- wal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al. A closer look at memorization in deep networks. In International conference on machine learning, pp. 233 242. PMLR, 2017.
Jordan Ash and Ryan P Adams. On warm-starting neural network training. Advances in Neural Information Processing Systems, 33:3884 3894, 2020.
Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. Deep batch active learning by diverse, uncertain gradient lower bounds. arXiv preprinl ar X 1906. 0006006771 2019.
Lucas Caccia, Jing Xu, Myle Ott, Marcaurelio Ranzato, and Ludovic Denoyer. On anytime learning macroscale. In Sarath Chandar, Razvan Pascanu, and Doina Precup (eds.), Proceedings of The 1st Confer- ence Lifelong Learning Agents, volume 199 of Proceedings of Machine Learning Reseorch, pp. 165 182. PMLR, 22-24 Aug 2022. URL https::/pprcceedingg mlr wwt.ppressvisv wttp9///ccaa2aa.....o html.
Ronald L Davis and Yi Zhong. The biology of lorgetting a perspective. Newron, 95(3):490 503, 2017.
Anirudh Goyal and Yoshua Bengio. Inductive biases for deep learning of higher-level cognition. arXiv preprint ar Xrxi::2011 15091, 2020.
Lauren Gravitz. The forgotten part memory. Nature, 571((76)):S12 S12-S1 2019.
John J Grefenstette and Connie Loggia Ramsey. An approach to anytime learning. Tn Machine Learning Proceedings 1992, pp. 189 195. Elsevier, 1992.
Oliver Hardt, Karim Nader, and Lynn Nadel. Decay happens: the role of active forgetting in memory. Trends cognitive sciences, 17(3):111 120, 2013.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In
Proceedings of the IEEE conference compuler vision and pallern recognilion, pp. 770 70-77788-- 2016.
Dan Hendrycks and Thomas Dictterich. Benchmarking neural network robustness to common corruptions and perturbations. arXie preprint arXiv:1933. 12261,221,11 2019.
Kuan-Hao Huang. Deepal: Deep active learning in python. arXiv preprint arXiv:2111.15255 2021.
Andrew Tlyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry. Adversarial examples are not bugs, they are features. Advances neural information processing systems, 32, 2019.
Shuntaro Izawa, Srikanta Chowdhury, Toh Miyazaki, Yasutaka Mukai, Daisuke Ono, Ryo Inoue, Yu Ohmura, Hiroyuki Mizoguchi, Kazuhiro Kimura, Mitsuhiro Yoshioka, et al. Rem dlee-active mch neurons are involved in forgetting hippocanpus-dependen memories. Science, 365(6455): 1313, 2019.
Alex Krizhevsky Learning multiple layers of features from tiny images. 2009.
Fabian Kuppers, Jan Kronenberger, Amirhossein Shantia, and Anselm Haselhoff. Multivariate confidence calibration for object detection. In Proceedings of the IEEE/CVF Conference Computer Vision and Pallern Recognition Workshops, pp. 326 327, 2020.
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip HS Torr. Snip: Singlo-shot network pruning based on connection sensitivity. Xix preprint arxiv: 181.00000 2018.
13

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Published in Transactions on Machine Learning Research (01/2023)
Xingjian Li, Haoyi Xiong, Haozhe An, Cheng-Zhong Xu, and Dejing Dou. Rifle: Backpropagation in depth for deep transfer learning through re-initializing the fully-connected layer. In International Conference Machine Learning, pp. 6010 6019. PMLR, 2020.
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning modcls resistant to adversarial attacks. arXiv preprint arXi::106..0000 2017.
Diganta Misra, Bharat Runwal, Tianlong Chen, Zhangyang Wang, and Irina Rish. APP: Anytime Progressive Pruning. arXiv, April 2022. doi: Aarvv 2204.01640.
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht. Obtaining well calibrated probabilities using bayesian binning. In Twenly-Ninlh AAAI Conference on Artificial Intelligence, 2015.
Sinno Jialin Pan and Qiang Yang. A survey transfer learning. IEEE Transactions knowledge and data engineering, 22(10):1345-13 1359, 2009.
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3))211-22,, 2015.
Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks: A core-set approach. arXiv preprint Xrx: 10011108.000 008,0 2017.
Burr Settles. Active learning literature survey. 2009.
Yichun Shuai, Binyan Lu, Ying Hu, Lianzhang Wang, Kan Sun, and Yi Zhong. Forgetting regulated through rac activity in drosophila. Cell, 140(4):579-588) 2010.
Karen Simonyan and Andrew Zisserman. Vory deep convolutional networks for large-scale image recognition. arXiv preprint aiv: 1409. 1556, 2014.
Christian Szegedy, Wojeiech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. Intriguing properties of neural networks. arXiv preprint ar1iv:1312. 6199, 2013.
Ahmed Taha, Abhinay Shrivastava, and Larry S Davis. Knowledge evolution in neural networks. In Pro- ceedings of the IEEE/CVF Conference Computer Vision and Pottern Recognition, pp. 22843-12855 2021.
Sebastian Thrun. Is learning the n-th thing any casier than learning the first? Advances in neural information processing systems, 8, 1995.
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Alcksander Madry. Robustness may be at odds with accuracy. arxiv preprint 1805 12152, 2018.
Gido M Van de Ven and Andreas S Tolias. Three scenarios for continual learning. arv preprint arXiv:1904.07 077944 2019.
Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. Xixiv preprint arXic:1605.0714 2016.
Sheheryar Zaidi, Tudor Berariu, Hyunjik Kim, Jörg Bornschein, Claudia Clopath, Yee Whye Teh, and Razvan Pascamu. When does re-initialization work? arXiv preprint arXie:2206.1000 2022.
Hattie Zhou, Ankit Vani, Hugo Larochelle, and Aaron Courville. Fortuitous forgetting in connectionist networks. arxiv preprint arXix:2202, 00155, 2022.
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A Appendix
A.1 Broader Impact and Societal Relevance
We believe that our findings can potentially be harnessed to enhance the test accuracy and robustness of any machine learning system deployed in the real world where the aspect of generalization is crucial, they are continuously trained sequential data. For example, consider large-scale social media website in which users continually upload images and content. To recommend material, filter out inappropriate media, and choose adverts, the organization requires up-to-date prediction models. Every day, millions of fresh data points may arrive, which must be quickly integrated into operational ML pipelines. In this scenario, it logical to envision having a single model that is regularly updated with the most recent data. Every day, additional training on the model with the updated and larger dataset might be undertaken. In these scenarios, the proposed framework (LURE) can improve the generalization and performance of the model to greater extent as opposed to new training from the parameters of yesterday's model without reinitialization.
Furthermore, in applications such as autonomous driving and industrial robotics, where the deployed model needs to be frequently updated in order to stay in syne with the surroundings. Using LURE as a training paradigm to update the model can boost performance and generalization in a computationally efficient way, as provides a better initialization for continuous training compared to warm-starting or updating the model from scratch.
In addition to the above scenarios, proposed framework can be conceivably harnessed in applications of deep active learning where the goal is to find the most informative data to label with an oracle and incorporate into the training set. However, current active learning frameworks retrain models from scratch afler each querying step, which is computationally expensive and partially responsible for deleterious envi- ronmental ramifications. The LURE framework allows models to be efficiently updated without sacrificing gencralization and performance, thus having a positive impact on society.
A.2 Ablation studies
To examine the influence of the individual components of our LURE network in ALMA settings, we perform the following ablation study.
Effect of Ratio of Reinitialized Parameters. Table 7 the effect of varying the number of initialized parameters on the performance and generalization of the model in CIFAR10. We train the model in ALMA settings using the LURE framework by varying different percentages of reinitialized parameters (5%, 10%, 20%, 30%, and 40%). Experiments were carried out using full-replay with ResNet18 for [Spl = 8. The results show that the unlearning of 5% percentage of parameters has no impact on performance, while the unlearning of more than 30% has less impact on test accuracy. We find that reinitialization 20% of the parameters results in the best performance.
Table 7: Evaluation varying the percentage of reinitialized parameters during training on CIFAR10 dataset using ResNet18.
Reinitialized Params (%)
Test Accuracy (t)
CER (L)
Generalization Gap (4)
5
89.84
11955
6.83
10
91.81
11571
7.22
LURE
20
94.32
9622
6.60
30
90.73
11806
7.91
40
90.79
11559
6.57
Effect of Importance Estimation Method. We investigate the effect of various methods of importance ostimation on our proposed training paradigm. For this, we consider Fisher Importance (FIM), weight magnitude, random, and SNIP. demonsteesssssratess the performance and generalization of the model trained with the LURF framework with different. selections to estimate important parameters on CIFAR10 using
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Table 8: Evaluation with different importance estimation on CIFAR1O dataset.
Importance criteria
Test Accuracy (f)
CER (4)
Generalization Gap (4)
BL
89.31
5657
7.90
FIM
92.73
4346
8.39
LURE
Weight Magnitude
92.18
4547
8.53
SNIP
93.73
4409
7.48
Table 9: Evaluation with varying the quantity of data for importance estimation on CIFAR10 dataset.
# samples
Test Accuracy (1)
0.2 M
93.73
LURE
128
93.62
ResNet18 for [SB] = 4. Unlearning and relearning with SNIP, Fisher information, and weight magnitude results in better performance compared to bascline. This shows that our training paradigm is not only limited to SNIP, but any importance estimation criterion can be used to identify the dataset-specifie connections.
Varying the quantity of data used for Importance estimation In our experiments, we randomly sampled 20% of the data from each mega-batch and used it to estimate the importance of the paramoters before selective forgetting. Here, we analyze the impact of the number of data used to determine the important estimation on the final performance. Similar Lee et al. (2018), we used as few 128 samples to estimate the important parameters using SNIP. Table 9 shows that LURE is not sensitive to the variation in the input data used to estimate the importance as the final performance remains unchanged.
Perfect Calibration
Output
Gap
100
Top-1 Accuracy (%)
LLF
S&P
LURE ECE: 3.66
80
ECE: 4.10
ECE: 4.21
ECE: 4.33
60
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0
0.0
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Figure 6: Confidence estimates and the corresponding Expected Calibration Error (ECE) of the CIFAR-10 ALMA trained models. Lower ECE is better. Our method is well calibrated, with confidence estimates closer to perfect calibration compared to BL, LLF, and S&PP.
A.3 Model Calibration
DNNs are often deployed in safety-critical applications, where it is essential to have model that has sufficient sense of uncertainty about its predictions. Therefore, we evaluate the calibration of models trained with different reinitialization methods in ALMA settings. The common metric for identifying miscalibration in classification is the Expected Calibration Error (ECE) (Naeini et al. 2015). The ECE measures the discrepancy between absolute accuracy and average confidence as weighted average. The lower the ECE, the better calibrated the model is. Figure 0 shows the ECE values along with reliability diagram on CIFAR10 using the calibration library by Kuppers al. 2020). The result shows that BL. LLF, and S&P
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are highly miscalibrated and far more overconfident than the proposed LURE framework. Thus, in addition to improving performance and generalization, online learning using selective forgetting can effectively improve calibration, thus improving reliability in contexts where safety is of absolute importance.
A.4 Convergence to Flatter Minima
DNNs that converge to flatter minima loss land- scape have greater adaptability to new tasks with- out straying too far from the optimal parameters for
100
BL
S&P
previous lasks. Furthermore, solutions that reside in flatter minima dictions do not change significantly with minor per- turbations. We apply independent Gaussian noise to all parameters of the CIFAR-10 10 trained model, as described in [Alabdulmohsin et al. 2021). Fig- ure shows that the solution reached by LURE, LLE, and S&P is more robust. lo model perturbation than standard training. Our method is significantly less sensitive to perturbations than the other meth- ods, and the performance gradually decreases. More specifically, for every amount of noise introduced into the model parameters 0, the change LURE training accuracy smaller than in standard train- ing, implying that the solution provided by LURE appcars to reside in flatter local minima. We ar- gue that training the model by alternating between learning and unlearning stages leads to a larger val- ley, which could better explain our model's ability to consolidate generalizable features.
Top-1 train Accuracy (%)
80
LLF
more robust because the pre-
LURE
60
40
20
20
40
60
Perturbation (ax10-3)
Figure 7: Robustness of the model perturbed by vary- ing degrees of Gaussian noise. Our method consider- ably robust to Gaussian perturbations, as the decline in performance is gradual, suggesting convergence to flatter minima.
A.5 Implementation Details
Datasets. We empirically evaluate our proposed framework on three different data sets: (a) CIFAR-10 Krizhevsky et al. 2009) (b) R-FAR-100 (Krizhevsky et al., 2009) and (c) Restricted Imagenet (balanced) Illya et al. 2019 Tsipras al., 2018). CIFARIO and CIFAR110 consist of 50,000 training images and 10,000 test images, each of size 32 x 32, divided into 10 and 100 classes, respectively. Restricted ImageNet (balanced) is a subset of the original ImageNet. data set (Russakovsky et al. 2015) consisting of 89517 training images and 3450 test images, each of 224 x 224 size divided into 14 classes consisting of five subclasses each. For ease of computation, we resize the images to 32 x 32 for our experimental settings.
BL
LLF
S&P
LURE
Weight decay
0.0000
91
92
91
91
91
91
92
92
93
93
93
93
92
92
92
92
0.0001
90
90
90
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92
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94
0.0005
85
82
84
83
93
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0.0010
75
76
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0.005
0.01
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Figure 8: The sensitivity of different reinitialization methods to hyperparameters. The initialization-
training paradigm is more robust against the change in weight decay and the learning rate compared to standard online training.
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Published in Transactions on Machine Learning Research (01/2023)
Algorithm 1 Training LURE in ALMA settings
input: Data stream SB = (M M,), Model f3 replay, Sparsity a
1: i
2: while S [SBl do
if replay then
4:
Mi U M,
5: else
6:
Mi M;
S/ 15-I-raan(MM
D Training learning step
8:
0.2.M;
9:
M + Importance Estimation a)
10:
Retain the task specific weights based on M
11: Randomly reinitialize the task irrelevant parameters in få
> Selective forgetting
12:
Model with this new initialization for next Mi41 training
Implementation Details. The efficacy of our framework is demonstrated in ALMA settings (Caccia et al. 2022). ResNet18 (He et al., (016)) is used the backbone for most of the experiments on CIFARI0 and CIFÁR100, while ResNet50 (He et al., 2016 is used for restricted ImageNet experiments. We initialize the networks randomly and use stochastic gradient descent (SGD) with momentum 0.9 and weight decay le-4 to optimize it. We follow the same procedure as that followed by Caccia et al. (2022); Misra el al. (2022). Networks are trained iteratively for batche with a batch size b = 64 for 50 epochs per mega- mega-batch batch of training without carly stopping. A step learning rate scheduler with an initial learning rate of 0.1 decayed at steps 20 and 40 is employed during the mega- batch training of our method. The standard data augmentation technique, that is, flipping and random cropping, is used. All training settings (Ir, b, e) are kept constant throughout the mega-batch training. We randomly divide the data set into mega-batches with an equal number of samples in each mega-batch. For cach mega-batch M,, divide it into train set with 90% of the samples and a validation set with the remaining 10% samples. We then randomly sampled 20% of the training data from each mega-batch to build the set π used to identify task-specific parameters through SNIP after each mega-batch of training. We use the default parameters for the SNIP algorithm specified above. Finally, we maintain a separate held-out test set, which used to evaluate the model's performance after training on each mega-batch. Unless specified, we keep the number of mega-batches to for all the experiments. For training S&P, a shrink coefficient of 0.4 and a noise of 0.001 are applied for the weights of the entire network before training on the new mega-batch of data. Similarly, for LLF, we reinitialize blocks 3 and 4 of ResNet (He et al. 2016) before the start of each mega-batch of training whereas for RIFLE we only reinitialize the last fully connected classification layer. For few-shot experiments, we do not consider existing techniques proposed in the few-shot literature. We limit the number of samples from each class to 270, which are sampled randomly in a class- balanced way. Finally, we use the same hyperparameters to perform experiments with different datasets and architectures.
A.6 Robustness of connection selection to change in learning rate.
Reiterating from Section 6.5 where our LURE framework is less sensitive to the choice of hyperparameters such as learning rate and weight decay. Here, we explore the sensitivity of connection selection to the choice of hyperparameters. Similar to the experiments in Section 6.4 we calculate the layer-wise percentage overlap of retained parameters across different training setups at the end of mega batch training. We compute the overlap between models trained with different learning rates. We keep the seed and other hyperparametors consistent across the experiments. The results in Figure 10 demonstrate that the selection of connect ions is more robust to the model trained at different learning rates. While most of the connections retained in the carly layers remain consistent across training sctups, the later layers (layer 4) showed less overlap for the model trained with different learning rates, Neverthcless, our connection selection is robust to the choice of learning rate.
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Published in Transactions on Machine Learning Research (01/2023)
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Figure 9: Layer-wise percentage overlap of the retained parameters across consecutive mega-batches, The percentage of connections overlapped between consecutive mega-batches remains consistent in the early and mid layers (block-1, 2, and 3) whereas changes in the latter layers the model is trained on the new incoming data.
A.7 Evaluating the redundancy of parameters removed during the unlearning phase.
Since our proposed method selectively forgets connections during training, evaluate the redundancy of the unlearned connection in the model in terms of performance and robustness. To analyze this, we measure the performance and robustness of dense and sparse models before and after selective forgetting. For this, we measure the performance of the ResNet18 model trained on CIFAR1O with [Spl = 8 after the first mega-batch training. The performance evaluated with the clean test dataset, while generalization is evaluated with the test images subjected to types of natural corruptions. Table 10 shows that the drop in test accuracy between the dense model and the sparse model (containing 20% fewer parameters than the dense model) is just 0.31% which is insignificant. The relative drop in test and robust accuracy with respect to train accuracy is less with the sparse model when compared to the dense model. In addition, in robustness and generalization analysis, the sparse model either outperforms or achieves the same accuracy when compared to the dense model. This shows that the connect ions that are constantly reinitialized during training contain trivial information that redundant fact and adds insignificant value to model training in terms of gencralization and performance. Thus, we empirically show that selective forgetting is crucial for retaining previous information while freeing the model's capacity to learn incoming data, thereby improving generalization in anytime learning.
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Published in Transactions on Machine Learning Research (01/2023)
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Figure Layer-wise overlap percentage of the retained parameters across training runs with different learning rates.
Table 10: Evaluating the redundancy of the parameters removed during the unlearning phase. The relative improvement between the train accuracy and test/robust accuracy is shown in brackets.
Parameters Train Accuracy (f) Test Accuracy (t)
Robust Accuracy (t)
Full model
11173962
75.54
71.78 (0.95%)
55.80 (0.74%)
Sparse model
8939169
74.04
71.47 (0.96%)
56.20 (0.76%)
A.8 Comparison of LURE with warm-start and cold-start training.
Figure 11 shows comparison between ResNet18 trained using warm-start, random initialization, and LURE CIFAR-10 for a mega-batch size of [[Spl = 2). For the first 50 epochs, the models are trained with 50% of data. Then, it is trained on 100% of data for another 50 epochs. Random Init. (cold-start) are models trained on 100% of the data from scratch. The dotted black line at 50 epochs represents the end of the first mega-batch training. The region between the two dotted lines shows the computationally resource intensive nature of the cold-start training to obtain optimal performance compared to warm-start and LURE, In practical settings where the training budget is limited, it is natural to maintain single model that is updated with the latest data at regular intervals. Training model from scratch at the onset of new data is rosource-inennsiv and drains the training budget. Also, in data privacy or memory-limited scenario where it is difficult lo store the previously trained data, it is less inluitive and seems wasteful to sacrifice all previous computations (past knowledge acquired) for much-needed generalization. While the warm- start training damages generalization, our proposed method (LURE) brings discernible benefits practical online settings: (1) LURE achieves faster convergence when compared to the cold-start (Random. Init.) model in settings where anytime performance is required, thus saving computational resources. (2) Tmproves generalization and robustness in anytime settings compared to warm-start (BL) and cold-start (Random. Init.) models. (3) LURE can also improve generalization in no-replay and buffered-replay scenarios. LURE with selective forgetting balances the trade-off between lack of generalization in warm-start models and the enormous computational expense retraining models from scratch through reimitialization.
20

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Published in Transactions on Machine Learning Research (01/2023)
100
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Figure 11: A comparison between ResNet models trained using warm start, random initialization, and LURE on CIFAR-10 ([[S] = 2). For the first 50 epochs, the models are trained with 50% of data. Then, is trained on 100% the data for another 50 epochs. Random Init. (cold-start) are models trained on 100% of the data from the start. The dotted black line 50 epochs represents the end of the first mega-batch of training. The region between two dotted lines shows the computationally resource intensive nature of the cold-start training to convergence compared warm-start and LURE. LURE mitigates the trade-off between warm-start and cold-start training in Anytime learning settings.
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Reviews:
[{"review_id": "sDesXl2XRI", "reviewer": "Reviewer_8nNS", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "S1: The work is well-motivated, as re-initialzation strategies with forgetting effects could really improve the generalization performance.\n\nS2: The manuscript is well-written, I really enjoy reading Figure 1 which illustrates the idea well.\n\nS3: The proposed method to select parts of parameters for Unlearn operation sounds interesting (though it was not totally new), where the comparison between sailency vectors looks cool though it might be compuationally intensive.\n\nW1: Authors mentioned RIFLE, which also selects part of parameters (FC-layer) for random re-initialization, I am wondering whether it is possible to compare RIFLE in online learning settings.\n\nW2: There might exist some other work in transfer learning also challenges the forgetting issues while fine-tuning a model to a new set of samples. They needs to select part of parameters to freeze the weights while training the rest. Some of them are also based on attention or sailency map, such as DELTA (Li et al. 2019) at ICLR that leverages the activation maps to determine the importance of filters/neurons. I am wondering whether it is possible to use such type of sailency vector/map to Unlearn. Because, at least, they are with lower computational complexity. \n\nW3: There are many work to analyze the gradient descent in online learning setting (Langford et al. 2009) or one-pass SGD (Zhu et al. 2021), of-course, for linear regression or two-layer NNs. I am wondering whether it is possible to extend these analyses to online learning for DNNs, through some proxy models such as Neural Tangent Kernel or two-layer NNs, and still could explain why LURE works.\n\nReference\n \nLi, Xingjian, Haoyi Xiong, Hanchao Wang, Yuxuan Rao, Liping Liu, and Jun Huan. \"DELTA: DEEP LEARNING TRANSFER USING FEATURE MAP WITH ATTENTION FOR CONVOLUTIONAL NETWORKS.\" In International Conference on Learning Representations. 2019.\n\nLangford, John, Lihong Li, and Tong Zhang. \"Sparse Online Learning via Truncated Gradient.\" Journal of Machine Learning Research 10, no. 3 (2009).\n\nZhu, Hanjing, and Jiaming Xu. \"One-pass Stochastic Gradient Descent in overparametrized two-layer neural networks.\" In International Conference on Artificial Intelligence and Statistics, pp. 3673-3681. PMLR, 2021.\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This work introduces an online learning algorithm LURE incorporating re-initialization and re-training strategies to improve the performance of DNNs in online learning settings. Simply, LURE aims at forgetting the so-called \"undesirable\" parameters in a DNN and re-trains these parts thru re-initialization and re-training. Authors also brought bag of tricks for re-initialization (Unlearn). Some of Unlearn strategies here have already existed, such as SPIN. The most interesting part is the estimation of connection sensitivity and the way to compare sensitivities to decide the parts of parameters for Unlearn. Comprehensive experiments have been carried out to validate the performance of LURE. Afterall, the comparisons against baselines of other online DNN training algorithm demonstrates the superiority of LURE.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please address the weakness points in the above section.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "o9pDjCKGYI", "reviewer": "Reviewer_ky58", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n- The problem the paper tackles is interesting, and as far as I know, the given solution is novel.\n- The paper is easy to follow and understand, proposing a relatively intuitive algorithm.\n- The experimental design includes many different tasks, showing the advantages of the suggested algorithm across many different problems.\n\nWeaknesses:\n- The biological motivation of the work is not well supported and is based upon several superficial similarities between neural networks and the brain. Yet, it is overstated in the paper as a major contribution, which I find misleading and problematic.\nWhile the model is indeed inspired by biological ideas, it can not serve as a major contribution, as the connection is far from being fully supported by evidence, but rather anecdotical.\n\n- One of the main motivations of the paper, as stated repeatedly in the abstract and introduction, is to improve over 2 \"naïve\" strategies for learning stream-like data. The first baseline is training at each mega batch from scratch on all previously given data. The second baseline is to fine-tune the last network for the currently given data. It is stated that the first baseline is very slow, while the second baseline generalizes badly. As such, I would expect that the given method is compared to these baselines as well, as well to other methods in the literature. The comparisons should include the computation times of each method, hopefully showing that the suggested method is indeed faster than the first baseline, and generalize better than the second baseline. \n\n- Until section 4, the paper feels very repetitive: all the ideas appear multiple times, in different variations. The entire representation can be much more concise, which will strengthen the work. On the other hand, in sections 5 and 6, many experimental designs are poorly explained, making the exact experiments that were done rather confusing. For example,  In section 5.4, many details are missing. What is the exact few-shot learning framework that is used here? It is unclear from the text itself.\n\n- The results of the paper should be understandable without previously reading Caccia et al 2021. As many of the experimental frameworks follow the design in Caccia et al, a better explanation of each experimental design method is required. This point is amplified as Caccia et al 2021 is relatively new and not yet peer-reviewed, so without proper background reading, one cannot be sure that the experimental design is a valid one.\n\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper introduces a training method for datasets that arrives in chunks.\nInstead of training the network from scratch on the entire dataset whenever a new chunk arrives or fine-tuning the previous model according to the new chunk of data, they propose to employ a forgetting-based algorithm. Given the new chunk of data, they suggest reinitializing only part of the neurons in the previous model, based on their usefulness in the classification of the previous chunk. After the re-initialization, the only train part of the model.\n\nThe paper show experiments in a large array of tasks, suggesting it can improve previous baselines in several different aspects, including performance, calibration, robustness, and more.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- Add to all the experiments 2 baselines: training at each iteration from scratch, and fine-tuning at each iteration. This point is crucial, as it is unclear whether the current baselines are sufficient to convey the point the experiments are trying to show. \n\n- Add a comparison of the running time of each of the methods, with emphasis on the 2 baselines suggested above. From the implementation details alone, it is unclear to me whether the proposed method can be fast in practice, or is it impractical. This point is also crucial.\n\n- Magic numbers: There are several magic numbers along the paper (for example, sampling 0.2 of M_i, or dividing CIFAR-10 by 8 parts. Please explain for each number whether it is critical for your method to work well, or if your method is robust to it. This point can strengthen the paper, but not as critical as the points above.\n\n- Biological motivation: In the current state, the importance of biological motivation in the paper is highly overstated -- as you don't have any experiments or clear support for such claims. The paper can be improved by tuning down these claims.\n\n- Making sections 1-4 more concise. (can improve the paper, but not crucial)\n\n- Elaborating on experiments done in section 5. (important point)\n\n---------------------------\nSome minor changes:\n\n- Some of the citations seem to be misplaced: for example, when introducing \"online learning\", the relevant citation should be to an early work that introduces the concepts of online learning, and not Ash & Adams (2020).\n\n- In page 3, should \"0.2 X M_i\" be \"0.2 X |M_i|\"?\n\n- In (4), should it be f_\\theta^i\n", "broader_impact_concerns": "not relevant", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "cIrs1vumqK", "reviewer": "Reviewer_aSpm", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n1. The motivation of the paper is clear.\n2. The paper is well-written and the authors conduct extensive experiments to show the effectiveness of the method.\n\n\nWeaknesses:\n\n1. It would be interesting to visualize the connections retained in each mega-batch to understand how the selection method works. \n2. It is not clear why the method can achieve better results than baselines. The authors argue that \"selectively forgetting\nextraneous information and relearning it through weight reinitialization brings discernible benefits to the model in online settings.\", but it is not clear what does it mean by \"extraneous information\". Although the test accuracy will not change as shown by the authors, will the \"extraneous information\" be changed due to different training hyperparameters such as learning rates?", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "In this paper, the authors propose Learn, Unlearn, and Relearn (LURE) for online learning with deep neural networks. The main idea is to leverage selective forgetting to improve the generalization of DNNs in online settings. The Unlearn stage selectively forgets the connections that are less relevant for the current mega-batch and retains those that are specific for the current mega-batch. The parameters are reinitialized and trained on the consecutive mega-batch based on the selection criteria. The authors conduct experiments on short sequences and long sequences to show the effectiveness of the methods. ", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "The authors should provide better understanding of the method to support the statements made in the paper. One way is to visualize the connections retained and the other way is to evaluate the robustness of the connection selection in terms of training setups. ", "broader_impact_concerns": "No concerns. ", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper explores an interesting approach to online learning that makes the use of selective forgetting. The results show favourable results compared to re-training, a naive warm start, as well as other approaches in the literature. 

Concerns of the reviewers mainly focussed on lack of clarity, missing baselines, and slightly unconvincing biological motivation. With additional experimental details, baselines, and some toning down of the bio-inspired motivation, the paper meets TMLR's acceptance criteria.

Congratulations on the acceptance to TMLR!
Decision: Accept (poster)