Expert Routing with Synthetic Data for Domain Incremental Learning
Authors: Yewon Byun
Sanket Vaibhav Mehta
Saurabh Garg
Emma Strubell
Michael Oberst
Bryan Wilder
Zachary Chase Lipton
Venue: Accepted by TMLR ()

Abstract: In many real-world settings, regulations and economic incentives permit the sharing of models but not data across institutional boundaries. In such scenarios, practitioners might hope to adapt models to new domains, without losing performance on previous domains (so-called catastrophic forgetting). While any single model may struggle to achieve this goal, learning an ensemble of domain-specific experts offers the potential to adapt more closely to each individual institution. However, a core challenge in this context is determining which expert to deploy at test time. In this paper, we propose Generate to Discriminate (G2D), a domain-incremental learning method that leverages synthetic data to train a domain-discriminator that routes samples at inference time to the appropriate expert. Surprisingly, we find that leveraging synthetic data in this capacity is more effective than using the samples to \textit{directly} train the downstream classifier (the more common approach to leveraging synthetic data in the lifelong learning literature). We observe that G2D outperforms competitive domain-incremental learning methods on tasks in both vision and language modalities, providing a new perspective on the use of synthetic data in the lifelong learning literature.

Published in Transactions on Machine Learning Research (07 (025))
Expert Routing with Synthetic Data for Domain Incremental Learning
Yewon Byun', Sanket Vaibhav Mchta', Saurabh Garg', Emma Strubell', Michael Oberst?,
Bryan Wilder', Zachary C. Lipton
!Carnegie Mellon University Johns Hopkins University
Reviewed OpenReviow:
Abstract
In many real-world settings, regulations and economic incentives permit the sharing of models but not data across institutional boundaries. In such scenarios, practitioners might hope to adapt models to new domains, without losing performance on previous domains (so-called catastrophic forgetting)- While any single model may struggle to achieve this goal, learning an ensemble of domain-specific experts offers the potential to adapt more closely to each individual institution. However, a core challenge in this context is determining which expert to deploy test time. In this paper, we propose Generate Discriminate (G2D), a domain- incremental learning method that leverages synthetic data to train doomain-diseriminatoo that routes samples at inference time to the appropriate expert. Surprisingly, we find that leveraging synthetic data this capacity more effective than using the samples to directly train the downstream classifier (the more common approach to leveraging synthetic data. in the lifelong lcarning literature). We observe that G2D outperforms competitive domain- incremental learning methods on tasks in both vision and language modalities, providing a new perspective the use of synthetic data in the lifelong learning literature.
1 Introduction
To deploy machine learning reliably, it is important to develop methods for adapting models we encounter environments sequentially in the wild. In medical imaging and risk prediction tasks, practitioners often apply models trained on one hospital's 's data to make predictions on samples from other institutions (Zech et al. 2018 Pooch et al. 2020 Guan & Liu 2021). Autonomous vehicle navigation systems must safely operate on different terrains, in different states, and even different countries. Morcover we adapt to each new environment, it is desirable (whenever possible) that this adaptation comes without loss of accuracy in previously seen environments.
In such domain-increment rehearsal buffer to re-train on a portion of past samples (Chaudhry et al. 2019b Lopez-Paz & Ranzato 2017). However, these solutions are often not viable in real-world prediction scttings, where the landscape of regulatory and industry norms place stringent constraints sharing data across institutional boundaries. We focus on the incemmnntlrem learning setting where the set of classes is fixed across domains and explicit data sharing across domains is prohibited. Crucially, domain identifiers are not given during inference time. The goal, after each round, is to produce system that performs well on test examples drawn random among all previously seen domains. At any round, our model only has access to the current data and, thus, simply performing gradient updates is liable to cause catastrophic forgetting, where performance decays on conflict (McCloskey & Cohen 1989 French 1999). To enable lifelong learning in scenarios characterized by stringent constraints data sharing, several settings for this problem have been proposed (Sodhani et al. (002)). For example, generative replay methods utilize generative models to create synthetic samples for experience rehearsal (Shin el al.) 2017 Sun
continual learning problems, some of the most effective methods rely on a
previously seen domains, even when the tasks are not fundamentally
et al. 2020
Qin & Joty 2022). However, these approaches have largely under-performed state-of-the-art
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Expert classifiers
Sequentially lly fine-tune expert for current domain
ft-1 1
ft. 2
fi
At inference time, route to most similar domain!
(x, Y
Generator
{X', Y
Discriminator
{X}
Train generator with real samples. Outputs are used to train discriminator.
Figure 1: Generate to Discriminate (G2D); During training (black text and arrows), we (i) finetune the generator and expert classifier and (ii) finetune a domain discriminator on synthetic images produced by our generator. At inference time (blue text and arrows); we route test samples to the corresponding expert, based on our discriminator's prediction.
discriminative approaches (un et al. 2020 due at least in part to the introduction of noise in the form of low-quality synthetic data.
Without access to rehearsal buffer, rehearsal-free free methods often tackle forgetting with dynamically expand- able systems with domain-specific parameters, such expert models (Aljundi et al., 2017 Rypeść al. 2024 or parameter-officient prompts (Wang et al. 2022d cb... As widely demonstrated in the literature (Wang et al. 2022a bld), such modular approaches allow learning parameters independently across domains to potential negative interference, leading less/mt nno forgetting and better transferring, avoiding tug-of-war scenarios that are exhibited in single, generalist models. However, core remaining challenge is determining which expert or module to invoke at test time. Some recent methods rely on the implicit domain discriminative (zero-shot) capabilitics of foundation models (Aharoni & Goldberg 2020) for an inferenco-time routing strategy (Wang et al. 2022bb. However, as will show, in settings where the zero-shot discriminative performance of foundation models is limited, these methods underperform (see Table D. The limitations and above-diseussed
challenges raise an interesting question: Rather than employing synthetic samples to train the downstream classifier, can synthetic samples serve more effective purpose in domain discrimination? Can we leverage synthetically generated samples to develop inference-tine routing mechanism?
In this paper, we propose a simple yet effective method for expert routing in domain ianaiiniinreemetall learning, Generate to Discriminate (G2D). G2D leverages synthetic data train domain discriminator that routes inference-time samples to set of domain-specifie expert classifiers, each checkpointed after each domain is encountered. Surprisingly we observe that using synthetic data for training a domain router is more effective than using the same samples to augment training data for training the label classifier (as in generative replay methods). We also find that our approach outperforms other competitive domain-incremmnnal learning methods, such as replay, regularization-basse benchmarks in both vision and text modalities. To further stress test current methods in more realistic settings, we introduce a new benchmark, DermCL, which consists of training sequence of four publicly available dermatology medical image classification tasks, each previously utilized independently to evaluate transfer learning or robustness to distribution shifts (Wantlin et al., 2023). We empirically show that competitive prompt-based mothods that rely on the implicit (zero-shot) capabilitics of foundation models (Aharoni & Goldberg 2020 Wang et al.) 2022b) underperform in these settings, where the downstream task (i.c., skin lesion prediction in minority populations) might diverge from tasks seen in pretraining corpora (see Table I emphasizing the need for more realistic benchmarks for a holistic evaluation continual learning methods.
and rehearsal-free (prompt-based) methods across the studied
To summarize, our contributions are follows:
We empirically observe that with the same synthetic samples, training a domain identifier outperforms augmenting training data for the downstream label classifier the common approach to leveraging synthetic data in the lifelong learning literature.
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Leveraging this observation, we develop simple method for expert routing in the domain-incremenne learning selting, without any access to real data from previously seen lasks, that outperforms considered baselines in both vision and text modalities.
Towards evaluation of current methods in more realistic settings, propose a new donmint-incremental learning benchmark consisting of a sequence of four real-world, dermatology classification tasks.
2
Related Work
The majority of domain--nnnenne incremental continual learning methods fall into (i) parameter-based and (ii) data- based regularization techniques &Sodhani et al. 2022). We also touch upon more recent approaches, dubbed (ili) prompt-based techniques that leverage pretrained models.
seaameeer-baeed Regularization Approaches. Most notable works in this category, namely Elastic Weight Consolidation (EWC; Kirkpatrick et al. (2017)) and Synaptic Intelligence (SI; Zenke et al. (0117) assess the importance of parameters related to previous domains and use a penalty term to safeguard the knowlodge stored those parameters while updating them for new domains. Another work similarly preserves the knowledge of previous tasks by using the initial task knowledge as regularizer during training (Li &
Hoiem 2017).
Data-based Regularization Approaches. Several competitive methods in the literature retain subset of data from previous domains as an episodic memory, which is sparsely replayed during the learning of new domains. Each differ in whether the episodic memory is utilized during training, such as GEM (Lopez-Pas & Ranzato, 2017j, A-GEM (Chaudhry et al., 2019a). ER @Chaudhry et al. 2019bb, MEGA (Goo et al. 2020), or during inference, such as MьPA (de Masson D' A'Atuume et al.] 2019 Wang et al. 2020). These methods assume access to retaining truc data from previous domains. Yet, this assumption is often violated in practice. Towards operating under more practical assumptions, deep generative replay-based methods have been introduced (DGR; [Shin et al. (2017). LAMOT, [Sun et al. (2020), LFPT5; Qin & Joty] (2022)
where generative models are used to generate synthetic samples for experience replay, often referred to as gencrative replay. However, these approaches have largely under-performod state-of-the-an discriminative approaches (Sun et al., 2020 due at least in part to the introduction of noise in the form of low-quality synthetic samples.
Prompt-based and Modular Approaches. Without access to a rehearsal buffer, rehearsal-free methods tackle forgetting with dynamically expandable systems with domain-speeifie parameters, such as expert mod-
els AAljundi et al. 2011 Rypeść et al.
2024 Li el al. 2024 Park 2024 parameter-elfiiiet prompts (Wang) I The key challenge in such approaches is determining
et al.
2022b 2024 Le et al. 2024 Yu et al. 2024).
which
expert or module to invoke at inference time. Some methods assume access to task identity at test
Li et al. 2019 Wang et al. 2022a. access to previous samples (Doan et al,, Araujo et al.] 2024). However, these assumptions generally restrict practical use in settings where data sharing between institutions are prohibited. In response to the increasing popularity of pretrained models, recent methods rely the implicit domain discriminative (zero-shot) capabilities of foundation
time
De Lange et al. 2021 1
2023
models (Aharoni & Goldberg 2020 Zhou et al., 2024) for an inferencc-time routing strategy (Wang et al.
2022b Mchta et al. [2023) demonstrates that protrained initializations implicitly mitigate the issuc of continual learning, exemplified by L2P (Wang et al.] 2022d), DualPrompt (Wang et al.) 2022c), S- S-Prompts (Wang (Smith et al.] 2023b), involves learning a small number of parameters
forgetting when sequentially finetuning models. more recent line of work, known as prompt-
et al., 2022b), and per domain in the form of continuous token embeddings or prompts while keeping the remaining pretrained model fixed. Although such methods allow continual learning without rehearsal, they depend on access to pretrained models that provide a high-quality backbone across all domains, which may not be available in sensitive environments in real-world deployment (e.g., healthcare). While many methods have been proposed, it is unclear whether they will work well in real-world deployment settings when their implicit assumptions are violated.
More broadly, modular approaches utilizing expert models have demonstrated effectiveness across various scttings Zhou el. al. 2022,, including (but not limited to) multi-task learning (Haximeh al. 2021 Fan al. 2022
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3 Preliminaries
In domain-inerementar continual learning, the primary goal is to learn a model that adapts to cach new domain while mitigating catastrophic forgetting on previously seen domains (Van de Ven & Tolias 2019 Formally, we consider a sequence of T domains, Dj + + Dr, where D, = { V 1 represents a dataset corresponding to domain t, sampled from an underlying distribution (x )). xf E X is the i-th image or text passage and y y is its label. N₁ is the total number of samples for domain t. Under this premise, Vt, the marginal or conditional distributions over X' and y can change, i.e., Pi(x') & Pa-1(2)) and P())) * P(+1()(A)), while the label space y remains fixed across all domains. The goal is to learn a prodictor fa :X + V, a neural network paramcterizod by A E Rº, to minimize the average expected risk Ry across all T domains
T
N e(Sola))
1
Dro DT)
(1)
k=0
where E is loss function. As standard in the literature, we take ft as the cross entropy loss.
Since the predictor fo only has access to the current data D, during each phase t of the training sequence, this prevents the direct minimization of the average expected risk (Equation D If model sequentially trains by focusing only on minimizing the empirical risk of the current domain, it risks catastrophic forgetting of knowledge aequired from previous domains. Therefore, to demonsstrte the model's learning behavior over the sequence of domains and analyze catastrophic forgetting of the previously seen domains, we evaluate specific domain t using the test dataset of that domain, Diest P, Y),
the model after training and all test datasets from previously seen domains, Diest ~ P:(X, Vi E [l,..., -1.. the domain identity known only during sequential training and not during inference-tinee The goal of domainn-nncremental continual learning is to produce system that performs well test samples randomly drawn from all previously seen domains. Let as,t denote the accuracy on domain s after training on domain
1]. We remark that
t. Following prior work (Lopez-Paz & Ranzato 2017 we compute the average accuracy (At) metric after training on the domain This is given by
A =
Ctatt-
(2)
4 Generate to Discriminate (G2D)
Motivated by the limitations of previous works (Shin et al., 2017 Sun et al. 2020 Qin & Joty 2022 rather than employing syuthetic data to augment training data to train the downstream label classifier, ask whether synthetic data can be used to develop an inference-time routing mechanism. Access to an accurate domain discriminator would answer the question of which expert or module to invoke at inference- time, without any access to real data or ground truth annotations from which domain each data is sourced. Alongside this domain discriminator (router), we learn and maintain a set of domain-speciff experts, which we invoke during inference- eeterence-tmeeeeeeee according to the router's predictions. We refer to our method of using synthetic data learn an expert routing mechanism as Generate to Discriminate (G2D).
4.1
Generation of Synthetic Samples
At each domain Dr, we finetune a generative model G with samples from the current domain {(xt, yf) M. For our generator for the vision domain, we finetune an off-the-shelf, text-to-image Stable Diffusion (Rombach) et al. 2022 model (see I for further details). For our generator for the text domain, we use pretrained T5 La-Large v1.1 model (Raffel et al. 2020) and optimize via prompt tuning (Lester et al. 2021 which learns continuous input token embeddings (see $B.1 and $C.2 for further details). After finetuning these models on each domain, we then sample synthetic examples M, from G, which will be used in approach for learning an expert routing mechanism, as well as in the comparison to generative replay methods. We note that after sampling the synthetic samples from Mr, we discard G. In other words, at each domain D., our method only assumes having access to the current. domain's generator G and does not require access to previous ones.
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4.2 Training Expert Classifiers and the Routing Function
Given synthelically generated data from our generators on domains Di,..., Dr, we finetune domain discrimi- nator (router), Da, on the union of the synthetically generated samples Mi U... M U Me, for domain identity prediction (i.e., I-way classification). More formally, we construct dataset of 1 i) E M.} to train a domain discriminator Da, :X' - 3/ where y (1, In short, our domain discriminator learns to predict domain membership, or route samples to their corresponding most similar domains.
At each domain D,, we sequentially finetune our classifier fe, as the expert on domain t, and add fe, to our list of experts Je....... fe. likely domain identity i, and the test sample is routed to the corresponding expert classifier Jo, for our final class prediction (see Figure] 1 for a schematic visualization of our approach). For our expert classifiers and domain discriminator for the vision domain. we use a vision transformer (ViT B-16) (Dosovitskiy et al. 2020) pretrained on ImageNet (Dong et al. 2009). For the text domain, we use pretrained BERT-Base (Kenton the source hold-out performance to select the best combination of parameters (see $C.1 and $.22 for further details).
At inference time, we use our domain discriminator to predict the most
& Toutanova 2019 backbone. To select our hyperparamcters,
Algorithm 1: Generate to Discriminate (G2D)
Input: A stream of domains Di,..., Dr, which are only accessed one at time
1: for t t= 1 to T do
2: Get access to current domain D:
(1) Finetune Generator: Train domain-specific generator G, on current domain data D.
(2) Generate Synthetic Data: Sample synthetic set M, from G.
5: (3) Train Expert Classifier: Finetune expert classifier fo, on real data from De
Add fo, to expert pool (Jop....,
(4) Train Domain Discriminator:
Construct synthetic dataset ((r,(( E
Train a domain discriminator Do on this dataset to predict domain label i E (1....,()
10: end for
11: Inferenec:
12: Given test input
13: Predict domain f = Dz(x)
14: Route z to expert fe, for final label prediction
5
Experimental Setup
We compare our approach to generative replay approaches and other doomain-incrementall approaches across a variety of domain-incrrmmetall learning benchmarks both vision and text modalities.
5.1 Datasets and Metrics
For our vision experiments, we look at standard inneemnnaacrmmettlll benchmarks: DomainNet (Peng et al, 2019) and CORe50 (Lomonaco Maltoni 2017). and Derm (see $5.1 our newly introduced benchmark curated from real-world dermatology tasks (schandl et al., 2018 Cassidy al. 2022 Pacheco et al. 2020
Daneshjou et al.) 2022). We use the standard ordering for DomainNet: real, quickdraw, painting, sketch, infograph, and clipart. For CORe50, we use the standard setting of a sequence of 8 domains, with three fixed test domains that are out-of-distribution (OOD). On this task, we remark that learning domain discriminator essentially learns similarity function between domains and the OOD test example, routing each new example lo the expert trained on the most similar in-distribution (ID) domain. For both dat taseesss we report. average accuracy averaged over 5 random seeds.
For our text experiments, we evaluate (QA) benchmark as introduced in the work de Masson D' D'Autume et al. 2019). The benchmark consists of three QA datasets: SQuAD v1.1 (Rajpurkar et al. 2016),, TriviaQA (Joshi et al. 2017) and QuAC (Choi et al. 2018). TriviaQA has two sections, Web and Wikipedia, which are considered as separate datasets. We
method the standard domain- incrementalee question-answeriing
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use four different orderings of domain sequences (sce AA.2 Following prior works (de Masson D Autume el al. 2019 Wang el al. 2020); we compule the F1 score for the QA lask and evaluate the model at the end of all domains, i.e., we compute A4-
DermCL Benchmark. The lack of realistic benchmarks in the continual learning community and the artificial temporal variation in existing benchmarks have been highlighted in the literalure (Lomonaco & Maltoni 2017 Lin et al. 2021; Wang et al. 2022c). Towards a more holistic evaluation of existing methods realistic variation in domain shifts, we propose a new domain-incremennall continual learning benchmark (DermCL), which presents real world distribution shifts in dermoscopic images, due to differences in patient demographics, dataset collection period, camera types, and image quality. The benchmark spans four domains of dermoscopic image datasets- HAM10000 (Tschandl et al. 2018). BCN2000 (Cassidy et al.) 2022): , PAD-UEFSS-00 (Pacheco et al., 2020), and DDI (Daneshjou et al. 2022). for classification task over 5 unified labels of skin lesions. Notably, DDI has been attributed to exhibiting a large performance drop, due to the presence of more dark skin tones from minority groups, exemplifying how failures of backward transfer (i.e., catastrophic forgetting) can have catastrophic outcomes. All four datasets in the sequence are publicly available (see AA.3 for details). We believe that this provides an important evaluation for modern ionmann-inermmental learning approaches, as they often rely on large pretrained models that may have very different pretraining datasets from such real world tasks (e.g., medical imaging).
5.2 Baselines
Generative Replay. A common way to use synthetic data in lifelong learning is butfer of limited synthetic samples used to train the label classifier, introduced by Shin et al. (2017), often referred to as Generative Replay. More precisely, at domain D,, the generator G. is finetuned with samples from the current. domain {(af, v() generates synthetic samples M, from Gt, and stores these samples in a replay buffer. At domain D2+1, the classifier fe is sequentially trained on the union of synthetic samples from previous domains (xr, 1) E M.} and real samples from the current domain ((x), ( We include specific implementation details in B.2
Other Baselines. First, we assess vanilla sequential finetuning (T)qFT), which does not employ any continual learning regularization techniques. Second, we compare with a traditional, parameter-based regularization method, elastic weight consolidation (EWC; Kirkpatrick et al. (017). For vision experiments, we further compare with recent advances in prompt-based methods which have greatly boosted state-of-the-ate tet-thh--rr art performance across many vision benchmarks. Specifically, we compare our method with L2P (Wang et al., 2022d) and S-Prompts (Wang et al. 2022b as they exhibit competitive performance on domain-incceemnnall learning tasks 2
Oracle and Quasi-Oracle Baselines. We also compare with the following oracle baselines. First, we compare against the setting of when access to real data from all domains is allowed every lask step, termed the multi-task learning (MTL) baseline. This is equivalent to training on the union of all existing data and can be viewed as an upper bound on performance (i.e., oracle performance) when there is no significant. negative transfer between domains. Next, we compare against the following quasi-oracle basclines. We compare against a setting we term Oracle Router, where have access to all real examples simultanconsly in training the expert router (as opposed to ours in G2D trained via synthetic samples). We compare against Experience Replay (ER: Chaudhry et al. (2019b)), a data-based regularization method, where the buffer retains limited real samples for previously seen domains. For both ER and Generative Replay, we follow the precedent set in the work of de Masson D' D'Autume et al. (2019), retaining and sampling samples in proportion to the dataset sizes. In the text domain, we additionally compare our approach with methods that leverage replay buffers with real samples for task-specific rest-time adaptation, namely Memory-based Parameter + (MbPA++) 4de Masson D' D'Autume et al. 2019) and Meta-MbPA (Wang et al.) 2020 Meta-MbPA trains the model to attain a more suitable initialization for test-time adaptation and achieves the stale-of-the-art performance on the Question-Auswering benchmark. Note that our setup prevents access to real samples or domain identities from previous domains, making these oracle and quasi-oracle baselines like Experience Replay, MbPA++, Meta-MbPA. Oracle Gate, and MTL inapplicable in practice. However, we
We note that there exist two variants of S-Prompts bViddbased, and based, For fair comparison, we compare with the variant that uses the same underlying pretrained checkpoint as the other baselines and G2D.
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Table 1: Vision Results. For DomainNet and CORc50, we report performance in terms of average accuracy. For DermCL, performance is reported in terms of average AUC (due to high label imbalance). denotes results obtained from Wang et al. (2022b). For Experience Replay and Generative Replay, we use a fixed buffer size of 15/class for DomainNet and 100/class for DermCL, according to the lower bound on samples per class in the actual dataset; and 50/class for CORe50, following previous practice (Wang et al. 2020
Method
DomainNet
CORe50
DermCL
Seq-FT
57.45 + 0.36
83.10 0.50
70.49 2.51
EWC
57.25 = 0.18
82.10 1.89
75.26 0.69
L2P
40.21
78.3 0.11
74.20 = 1.42
S-Prompts
56.13 + 0.39
83.38 0.36
81.59 + 0.22
Generative Replay
60.16 = 0.64
92.50 + 0.40
81.23 = 1.12
G2D (Ours)
70.03 1 0.02
94.05 + 0.53
85.24 + 0.85
Oracle Router
71.24 + 0.18
95.44 0.11
91.92 + 0.31
Experience Replay
65.64 + 0.47
93.65 + 0.60
89.17 + 0.79
Upper Bound (MTL)
75.10 + 0.09
97.19 + 0.12
93.34 1 0.96
still include these baselines for a more comprehensive comparison to demonstrate how G2D performs against. methods that (i) retain accoss to real samples from previous domains or (ii) assume access to ground-truth domain identity.
6 Results
Table 2: Text Results. Performance is reported terms of average F1 (averaged over 4 domain scquences). denotes results obtained from Wang et al. (2020). ER, MbPA++ and Meta-MbPA uses a buffer size of 1% actual samples.
We observe that G2D outperforms com- petítive methods in the considered vi- sion and text benchmarks: DomainNet, CORe50, DermCL, and Question Answer- ing (Table 1 2 Notably, on datasets characterized by significant domain shifts that amplify negative backward trans- fer, such as DomainNet and Derm CL our method achieves substantial perfor- mance improvements. Additionally, G2D demonstrates strong out-of-distribution (OOD) performance compared to prior basclines, as evidenced by results on CORe50, where evaluation encompasses three OOD datasets (e.g., domains never seen during training). Amongst the con- sidered baselines, the comparison with Generative Replay is particularly instructive for understanding how to most effectively utilize synthelic data in the domaann-inermmental learning selting. In this comparison, we use the same set of synthetic samples, but in different ways Our results demonstrate that using the same set of synthetic sample for domain discrimination consistently outperforms their use for augmenting training data for downstream classification (i.e., Generative Replay) across all considered benchmarks (see Table [ and Table 2. Fundamentally, G2D relies on the generator to accurately model samples from different domains in order to train the domain discriminator. Generative Replay, on the other hand, requires an accurate modeling of downstream class labels rather than domains. Thus, our results empirically suggest that modeling the difference in domains is a relatively casier task than modeling the difference in downstream classes (for the considcred gencrative models).
Method
Question Answering
Seq-FT EWC
56.6 5.7
55.9 t 3.7
Generative Replay
59.5 0.9
G2D (Ours)
64.7 0.2
Oracle Router
65.4 + 0.0
Experience Replay
62.6 + 1.4
MbPA++
61.9 + 0.2
Meta-MbPA
64.9 + 0.3
Upper Bound (MTL)
68.6 0.0
3 Nomaannett contains highly heterogenous domains, as highlighted by prior works (Wang al. 2022b DermCL exhibits substantial domain shifts, due to inclusion of patient populations with diverse racial backgrounds and skin tones.
WWe include the specific details and implementation of Generative Replay in $B.2
Tn Table 12 and Figure 3 (see Appendix gF. we include example visualizations of generated samples for both modalities.
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Table 3: We report the accuracy of domain discrimination (expert routing) for the considered baselines complement the analysis presented in Figure 2 and $6.1 Note the oracle discriminator refers to model trained on real samples, serving as the reference upper bound. 4_" denotes not applicable.
Dataset
S-Prompts Disc.
G2D Disc. (ours)
Oracle Disc.
CORe50 QA
90.81 0.27
97.38 0.93 94.5 + 0.2
98.94 + 0.07
97.1 + 0.0
400
0.457 3.400 (2.40
Figure 2: (Three left-most plots) Domain discrimination visualizations for CORe50 benchmark (8 domains) From left right, the first subplot shows how domains are clustered via S-Prompts (Wang et al. 2022b). In the second and third subplots, the clusterings for our domain discriminator and the oracle discriminator are visualized. (Three right-most, plots) Visualizations of domain clusterings for the QA benchmark (4 domains). The first subplot highlights the implicit domain discriminative nature of pretrained BERT-Base representations (Kenton & Toutanoval 2019). In the second and third subplots, we visualize the clustering of represcntations from our discriminator and the oracle discriminator trained using real samples. Interostingly, the discriminators trained with synthetic samples closely mirrors the performance and clustering patterns of the discriminators trained using real data.
While our motivation to operate under more practical assumptions restricts data sharing across domains, we also include comparisons with competitive methods that assume access to real data from previously seen domains, for more comprehensive evaluation. This allows to understand the utility of our method even when such data constraints are relaxed. We observe that although our approach operates without access to real data from previously seen domains, it retains competitive performance with Experience Replay (see Table [ and Table 2 and even outperforms the previous stale-of-Che-art in test-time adaptation-basse continual learning, Meta-MbPA on text experiments (see Table 2) These findings underscore the ability of our method to enhance performance even in scenarios not characterized by stringent constraints on data sharing.
6.1 Analysis of Expert Routing Performance
To better understand our observed empirical gains, examine domain discriminator (expert router) independently from the overall pipeline. For this purpose, we make a departure from the downstream classification task and focus solely on analysis of the expert routing procedure.
For the vision domain, we assess the performance of our discriminator with the (1) oracle discriminator (trained on buffer of real samples) and (2) S-Prompts (Wang et al.) 2022b), previous state-of-the-ant Somain-inerrmental learning method that leverages a routing mechanism. S-Prompts leverages K-Means during training to store centroids for each domain; then during inference time, it employs KNN to identify the domain of a given test image feature by determining the domains of the K nearest centroids. In Figure we present (-SNE plots (Van der Maaten & Hinton 2008) illustrating domain clusterings between these three methods for CORe50, the benchmark comprised of the longest domain sequence (eight domains). We observe that method achieves more accurate clustering and domain identification than the S-Prompts method (sce Table 3).
For the text domain, we present I-SNE plots visualizing the domain discriminative capability [Aharoni Goldberg 2020) of pretrained language model and the G2D discriminator trained on synthetic samples (see
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Notably, in the pretrained language model's clustering, there is confusion between the TrWeb (orange) and TrWiki (green) domains, both derived from the same TriviaQA dataset (Joshi et al. 2017]. Similarly, the TrWiki (green) and SQuAD (red) domains, originating from the same Wikipedia source, necessitate explicit discriminator training. We clearly observe that training an explicit discriminator results in more accurate clustering. This is remedied in the clustering of the G2D discriminator, closcly matching the behavior of the upper bound, namely a discriminator trained using real samples (see Table B Overall, for both vision and text domains, we observe that G2D improves domain identifiability (expert routing) and demonstrates competitive performance to a discriminator trained real data, exhibiting similar clustering patterns.
Figure 2
7 Discussion, Limitations, and Conclusion
In this work, we propose simple yet effective method for data to train a domain discriminator, enabling an inference-tine routing mechanism. We establish that this approach is more effective than using the same synthetic samples to directly train the downstream label classifier (as past works have explored). While we do not claim that this phenomenon is universal, our findings consistently held true across many different domain--ncrementall learning benchmarks we investigated for both modalities. We leave a deeper theoretical investigation behind the mechanisms of this empirical finding for future work. Further, our analysis of the capabilities of our domain discriminator, which encompasses existing domain discrimination approaches, unsupervised clustering methods, and discriminator trained on real samples, finds that our method improves expert routing performance across both modalities. Building on this, expanded formulations of experts and modular architectures, such as mixture of experts (Shazeer et al. 2017) and its derivatives thereof (e.g., multi-gate mixture of experts (Ma et al. 2018)), offer intriguing possibilitics for complementary extensions of our method, which we leave for future study. Motivated by our interest in rcal-world settings (c.g., clinical hcalthearc), where the prohibition on data sharing especially well-motivated, and by the lack of realistic benchmarks in the continual learning community more broadly learning benchmark to further stress-test competing methods. In
learning that leverages synthetic
we introduce a new Nomain-incremeett addition to its practical relevance in the problem setting at hand, our observations provide interesting insights into different perspectives on the use of synthetic data in such scenarios where real data cannot be shared.
Limitations. A potential limitation of our method is that the number of experts scales linearly with the number of domains encountered. While this introduces potential computational overhead, it is unlikely to be prohibitive in our motivating scenarios (i.c., healthcare), where the number of domains (c.g., institutions) is typically modest, often on the order of tens. This ensures the method remains practical for real-world applications, though strategies to address scaling for larger domain counts warrant further exploration. We report a detailed breakdown of the computational cost and storage requirements of our method in Appendix B33 Further, we remark that there exist potential privacy concerns, when leveraging generative models to sample synthetic data, where memorization could potentially reveal sensitive information from the original data in the training dataset. For these concerns, there is important, separate field of work in improving the differential privacy Dwork & Roth 2014 Dwork et al. (0066) of such methods, by training these models with privacy constraints (Dockhorn et al. 2022 Lyu et al., 2023 Cao et al., 2021). In this work, we do not take a stand on when or whether the sharing of generative models (or synthetic samples) should be permissible. While this is an important issue to be considered prior to real-world deployment of any method that involves generative models, how institutional practices develop and how the regulatory environment evolves will be informed, to a large degree, by exploratory research that characterizes both (i) the potential benefits; and (ii) the potential risks associated with the dissemination of generative models trained on real data. We see our research as helping to elucidate the potential benefits of synthetic data in such settings.
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A
Dataset Details
In this section, we provide additional details about the datasets used in our experiments. We provide the overall dataset statistics in Table 4
A.1
Overall Dataset Statistics
Table 4: Per-domain statistics for the domain-incrementte learning benchmarks used in both vision and text experiments.
Dataset
Domain
Train
Validation
Test
0
13491
1499
44972
1
13495
1500
44972
2
13487
1499
44972
3
13494
1499
44972
CORe50 Lomonaco Maltoni 2017
4
13490
1499
44972
5
13490
1499
44972
6
13469
1497
44972
7
13485
1498
44972
Real
108814
12091
32040
Quickdraw
108674
12075
51749
Painting
45373
5041
21849
DomainNed Pong et al.
2019
Sketch
43389
4821
20915
Infograph
32419
3602
13581
Clipart
30171
3352
14603
HAM10000 Tschendl ct al
2018
7210
2003
DermCL
BCN2000 Cassidy al. 2022
18238
2027
5066
PAD-UET 2- Pa heeu
2020
1655
184
459
DDI Duncshjou ct 2022
419
105
132
SQuAD 1.1 Rajpurkar et. al.
2016
81000
9000
10000
TriviaQA (Web) Joshi al.
2017
68400
10000
Question Answeringde Masson D' Autuuee et al. 2019
TriviaQA (Wiki) Jushi ct
2017
54000
QuAC
ChoC Choi 2018
72000
8000
7000
A.2 Question Answering Task Orders
For our Chestion-Answeriie (QA) benchmarkde Masson D' D'Autume et al. four sequences of dataset orderings for a more comprehensive evaluation. We report the average accuracy over all sequences.
(009 consider the following
1. QuAC + TriviaQA (Web) + TriviaQA (Wiki) + SQUAD
2. SQUAD TriviaQA (Wiki) + QuAC + TriviaQA (Web)
3. TriviaQA (Web) + TriviaQA (Wiki) + SQUAD - QUAC
TriviaQA (Wiki) + QuAC TriviaQA (Web) + SQUAD
A.3 DermCL Benchmark Details
This benchmark offers a sequence of four dermatology imaging tasks. Distribution shifts are present across all four domains (HAM10000 Tschandl et al. 2018 Cassidd et al. (2022), PAD-UEFS PADD-UEPS-2(PPcceeeo et al. (2020), and, DDDDnusshoou et al. (2022) in both demographics and data collection techniques. Dermoscopic image classification intraclass variations encompassing lesion texture, scale, and color. The label space for DermCL defined as the following 5 unified labels: MEL (melanoma), NEV (nevus), BCC (basal cell carcinoma). AKIEC (includes actinic keratoses, intraepithelial carcinoma, squamous cell carcinoma), and Other diseases. We provide additional details about each domain in the following Table All four datasets in the sequence are publicly available at BCN2000, PAD-UEFS-21 and https:://ddi-datasedt github. Daneshjou al. 2022 (for DDI).
diverse palient populations present complexities arising from (but not limited to)
Wantlin et. al. (2023) (for HAM10000,
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Table Per-domain information for the DermCL Benchmark.
Domain
Description
HAM10000
HAMio00((sccaanl Tschandl et al.
20188
The HAM10000 dataset was collected over the past 20 years from hospitals Austria and Australia using dermatoscopes.
CGN200CCassiide et al. 2002
The BCN2000 dataset collected from Spanish hospitals between 2010 and 2016 using dermatoscopes.
PAD-UEFS-2(Pachee et al.
2020
The PAD-UEFS- dataset was collected from Brazilian hospitals in 2020 using smartphone cameras.
DDI DDI Daneshjou et al. (2022
The DDI dataset contains images collected from pathology reports in Stanford Clinies from 2010-2020, representing 570 unique patients with diverse skin tone representation. It is notable for inducing significant performance drops in models, due the presence of more dark skin tones and uncommon diseases.
B Additional Implementation Details
In this section, we provide additional details about the (1) implementation of our method, (2) implementation of generative replay, (3) computational costs, and (4) hardware requirements.
B.1 G2D Implementation Details
Vision. For our generator, we use an off-the-shelf, text-to-image Stable Diffusion (Rombach et al. 2022) for our generative model (see Appendix $ for further hyperparameter details). During inference, we prompt the text-to-image conditional diffusion model with the text label to sample generations. For our expert classifiers and domain discriminator, we use Vision Transformer (ViT B-16) (Dosovitskiy et al. 2020 initialized with the pretrained ImageNet (Deng al. 2009) checkpoint. For our hyperparameter search, we use the source hold-out performance to select the best combination of parameters (see Appendix $C.1 for further details). For parameter efficiency, we finetune our expert classifiers with low-rank adaptation (i.c., LoRA) Hu al.) 2021), where we only adapt the attention weights and keep the remaining paramcters of the UNet architecture frozen.
Text. For our generator, we use prompt tuning to learn the parameter-cfficient models (Lester et al. 2021 We use the pretrained T5-Large v1.1 checkpoint adapted for prompt tuning as the backbone (Raffel al,, 2020) and the prompt embeddings conditionally generate a document content, question, and answer, all separated by the special tokens. During the generation process, we provide multiple text prompts. We use the following text prompts to conditionally generate synthetic samples:
initialized randomly. We input special token into the model and
text_ _oxt_prompt- =
"Generate article, question and answer.",
"Generate context, question and answer."
"Generate answers by copying from the generated article.", "Generate factual questions from the generated article."
During generalion, use ancestral sampling, which selects the next token randomly based on the model'ss 's probability distribution over the entire vocabulary, thereby reducing the risk of repetition. We generate samples with a minimum length of 50 tokens and maximum of 1,000 tokens and retain only those samples that contain exactly question-answer pair with the answer included in the generated document content. For training our domain discriminator and expert classifiers, we use low-rank adaptation (LoRA; Hu et al.) 2021) and freeze the pretrained BERT-Base (Kenton Toutanova 2019) backbone (see Appendix C.2 for Turther details).
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B.2 Generative Replay Implementation Details
We implement Generative Replay following standard practice (Shin et al. 2017 with the following updates to address limitations of the original approach: (1) The generative model originally used by Shin et al. (2017), WGAN-GP, is quite outdated. To ensure a fair comparison, we revisit Generative Replay using the same generative models employed in our method (G2D)
Stable Diffusion (Rombach et al. 2022) for vision data and T5 (Raffel et al. 2020) for text data. (2) In the original Generative Replay implementation, the generator is sequentially uned (in addition to the classifier) on each domain in the sequence. This introduces the challenge of catastrophic forgetting in the generative model and introduces additional artifacts caused by continually training on noisy samples. In order to address this issuc, there is an important line of work [mith ct al. 2023a) focused on investigating catastrophic forgetting within the learning procedure of generative models, but such investigations fall beyond the scope of this study. Instead, for the purposes of this work, we mitigate this issue by finetuning the generator from the pretrained checkpoint rather than the finet checkpoint from the previous domain. This is applied consistently to both G2D and Generative Replay to ensure fair comparison. In summary, these adjustments allow us to compare the performance of Generative Replay with our method, ensuring both approaches utilize the same fixed set of synthetic samples.
B.3 Computational Cost and Storage Requirements
We ackuowledge that our method incurs additional computational and storage costs, and practitioners should weigh these factors when choosing between approaches. Compared to naive sequential fine-tuning (Seq-FT), our method adds a total of 5-8 hours (per domain) of compute time on a single A6000 GPU, due to fine-tuning and sampling from the generator, and 1-3 hours (per domain) for training the domain discriminator. However, we note that this can all be done in parallel with expert training (that takes 1-8 hours on each domain is done in FTq-FT) and, thus, our approach incurs additional total time of up to 4-7 hours per domain. In terms of storage, the main overheads come from (1) storing synthetic samples (e.g., 2.4GB for a buffer of 5,000 samples), and (2) retaining fine-tuned expert classifiers (~330MB each). For the generator and discriminator, only retain the current domain's models-~18GB and ~330MB respeetively-ratiee previons versions. Overall, we believe the additional resource demands are manageable and not unreasonable relative to practices recent machine learning workflows. A detailed breakdown of these requirements is provided in Table 0
than storing all
Table 6: Computational and storage requirements for each component. Feq-FT denotes standard sequential fine-tuning, For Syuthetic Data, (1) the train time denotes sampling time and (2) we report storage in terms of a total buffer size of 5000 samples. We note that the training time differs based on each dataset (and the number of samples in their respective train sets).
Train Resource
Train Time (hrs)
Storage (GB)
Expert Classifier
1 A6000 GPU
1 1~8 8 / domain
330 MB domain
Domain Discriminator
1 A6000 GPU
1~3
domain
330 MB
Generator
A6000 GPU
4 ~ 6
domain
18 GB
Synthetic Data
1 A6000 GPU
1 1~3/
domain
2.4 GB (for 5000 samples)
FT
1 A6000 GPU
1 8~8) 8
domain
330 MB
B.4 Hardware Requirements
All experiments were conducted using NVIDIA RTX A6000 and NVIDIA RTX 6000Ada graphics cards.
C Hyperparameter Details
C.1 Vision Experiment Hyperparameter Details
For all of our baselines, experts, and domain discriminators, we use the same pretrained model checkpoints, namely, the VIT-B/16 checkpoint trained ImageNet las___as__ppccaeaa 2_24),2224__ released by Pytorch Image
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Models (timm). For our generative model, use Stable Diffusion (Rombach et al. 2022) conditional diffusion model with weights from the detailed in Table and Table For all hyperparameter searches, we use the source hold-out performance select the best combination of parameters.
checkpoint. Hy Hlppeparaaeters are
Table 7: Finetuning hyperparaneterrs for the generative models our vision experiments.
Hyperparameter
Value
Number of steps
150,000
Resolution
512 le
Learning Rate Learning Rate Scheduuler
Constant
Optimizer
AdamW Loshchilov & Hutter 2017
Batch Size
Weight Decay
1e-2
Table 8: Finctuning hyperparametors for the classifiers in our vision experiments
Hyperparameter
DomainNet
CORe50
DermCL
Number of Epochs
20 50
10
10
Learning Rate
0.005 ~ 0.01
0.07
0.005
Optimizer
SGD
SGD
SGD
Momentum
0
0
0.9
Batch Size
128
128
128
LoRA Rank
16
16
16
LoRA Alpha
16
16
16
To ensure that we are evaluating our baselines comprehensively, we also run a hyperparameter search for different regularization values [0.5, 10, 100] for the Elastic Weight Consolidation (EWC) method, and use À 1. For Experience Replay and Generative Replay baselines, retain (or sample) 15/class samples for DomainNet and 100/class samples for DermCL, according to the lower bound on samples per class in the actual dataset; and 50/class samples for CORe50, following previous practice (Wang al.) 2020). For of our basclines, we the same pretrained model checkpoints, namely, the ViT-B/16 checkpoint trained ImageNet srit_base_pape pacteeeaachh___11 (_24), released by Pytorch Image Models (timm).
C.2 Text Experiment Hyperparameter Details
For expert classifiers and domain discriminator, use LoRA and freeze the pretrained BERT-Base (Kenton & Toutanova] 2019) backbone. The BERT-base architecture has 12 Transformer layers, 12 self- attention heads, and 768 hidden dimensions (110M parameters)) For our generative model, we use prompt tuning to learn arrameeer-effiieen models (Lester et al.) 2021). We use the pretrained T5-Large v1.1 checkpoint adapted for prompt tuning as the backbone (Raffel et al. 2020), and the prompt embeddings are initialized randomly. Hyperparameters are detailed Table and Table 10 The hyperparameters for baseline hods set as described in Wang al. (2020). For Experience Replay (and Generative Replay), ) retain (or sample) 1% of examples which account. for around 6,000 examples across all four considered domains.
D Additional Experiments
D.1 Additional Discriminator Comparisons
We present the results on the remaining datasets for domain discrimination, comparisons between the S-Prompis discrininator, our G2D discriminator, and oracle discriminator trained samples with ground
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Table Finctuning hyperparameters for the generative models in our text experiments. We set the prompt length to 400 tokens as our prompt length, which accounts for 819k trainable paramcters (roughly 0.1% of the total number of parameters in T5-Large). For our learning rate scheduler, we use warmup ratio of 0.01 and linear decay over epochs.
Hyperparameter
Value
Prompt Length (Tokens)
400
Optimizer
Adam
Kingma & Ba. 2014)
Learning Rate
1.0
Batch Size
8
Weight Decay
le -
Maximum Sequence Length
512
Table 10: Finetuning hyperparameters for the classifiers in our text experiments.
Hyperparameter Question Answering
Number of epochs
5 (discriminator). (experts)
Learning rate
5e
Optimizer
Adam
Dropout
0.1
Batch Size
8
Max Input Length
384
LoRA Rank
32
LoRA Alpha
32
truth domain identifiers. tasks.
We observe that our discriminator outperforms the alternative approach all
Table 11: We compare the performance of the G2D domain discriminator with the S-Prompts discriminator (that uses KMeans and KNNs). We also provide comparison to discriminator trained real samples (Oracle Discriminator). The G2D discriminator outperforms the S-Prompts discriminator on all tasks.
Dataset
S-Prompts Disc. G2D Disc. (ours)
Oracle Disc.
DomainNet
80.33 + 0.05
83.74 + 0.99
85.03 + 0.74 98.94 + 0.07
CORe50
90.81 + 0.27
97.38 + 0.93
DermCL
71.22 0.10
75.22 = 0.48
80.45 + 3.18
E
Discussion on Generative Models in Healthcare
In general, and for good reason, practice in healthcare moves considerably slower than exploratory machine learning rescarch. It is generally the case that idcas take root in the rescarch community long before they show up in the clinic. Following this convention, due to the recency of successes of generative models (relative to discriminative models), contractual or regulatory requirements surrounding generative models is still in nascent stages of development.
What is the current slatus quo? The setling where model weights may be shared but not the actual training
data is a well-known setting in the healthcare domain (Kamran et al. 2022 Ulloa-Cers Cerna et al. 2022 Walsh et al. 2023). We elaborate on two examples: (1) Kamran et al. (2022) presents multisite external validation study for early identification of COVID-19 patients at risk of clinical deterioration, which require sharing the model trained on private EHR data from one US hospital with 12 other US medical centers; (2) Ulloa-Cerna
ee al. (2022) presents a multisite external validation study for model development for identifying palients increased risk of undiagnosed structural heart disease, which requires sharing the model trained private
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EHR. data and patient echocardiggraphy reports from one site with 10 other independent sites. While these are generally examples of discriminative models being shared across facilities as opposed to generalive models, this demonstrates the general principle that in such domains, model sharing often permissible in settings where data sharing is not.
On one hand, it seems intuitive that healthcare institutions might be queasier about sharing generative models than sharing discriminative models. On the other hand,
Healthcare institutions are even queasier about sharing real data and to this end there is a large ream line of work investigating the use of generative models for direct sharing or for producing synthetic datascts that could be disseminated in lieu of actual patient data (Chen et al. 2021 Coyner
ct al. 2022
DuMont Schütte et al. 2021
From standpoint of most contractual regulatory requirements, it is not yet clear even if generative models sit in different category than discriminative models or if they should follow the same current regulatory requirements for discriminative models.
How institutional practices develop and the regulatory environment evolve will be informed, to a large degree, by exploratory research that characterizes both (i) the potential benefits and (ii) the polential risks associated with the dissemination of generative models trained medical data.
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F Example Generations
We include example generations for both image and text domains.
F.1 Examples of Generated Images
Class Label
Synthetic Images
Real Images
Aircraft Carrier
Bicycle
Birthday Cake
Alarm Clock
8
Ambulance
Figure Comparison of sampled synthetic images and real images from the DomainNet benchmark.
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F.2
Examples of Generated Text
Table 12: Generated samples (context, question-answer pair) for the SQuAD domain. For the incorrectly generaled samples, underline one possible correct answer.
Dataset
Fields
Generated sample
Correct
SQuAD
Contert:
During the late 19th and early 20th centuries, the city's trade sector expanded greatly, and through the 20th century, more than half of its residents worked toward higher status in the military. Other industries included industry, commerce, public administration, and medicine. Its largest sector was public services police, fire services, and healtheare and was the nation's third largest.
Question: What is one of the industry sectors that were the biggest?
Answer:
public services
Incorrect
In the United Kingdom there is general agreement between the government and the private sector in principle that both private and publicly funded institutions of higher education constitute university colleges. Further, there is a mutual agreement between the independent college and the university to promote higher education. However, in both cases all the institutions of higher education either controlled private individuals or by a national agency, in such way as to protect freedom of expression. Question: What are of the institutions of higher education that are controlled by private individuals?
Contert:
Answer: private individuals
22


Reviews:
[{"review_id": "sDxLNQUmTS", "reviewer": "Reviewer_3i76", "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- The paper studies an important problem of domain-incremental learning, in particular an expert routing mechanism. The proposed method seems valuable as it focuses on the problem of training/updating the discriminator. \n- The paper is well-writen and is easy to follow. Technical aspects, when presented, are presented to the right level of detail. \n- The paper is accompanied by a new benchmark on a specific type of task which adds value to the proposed method.\n\nWeaknesses:\n- As a continual learning paper, it is expected that the experiments examine important aspects such as forgetting and/or transfer (forward or backward). The current set of experiments is limited to examining overall accuracy, which can be misleading at times. Furthermore, what is the performance across the sequential training/inference? What is the performance while the number of tasks increases? All these aspects are fundamental in continual learning research.\n- Despite some numbers presented in the appendix, the paper lacks a serious examination of the computational requirements of the proposed method, especially in comparison to other methods. While a clear advantage is the lack of need of storing past task examples, what is the cost of generating examples for tasks especially as the number of tasks/domains increases? Some theoretical bounds of thorough experimental examination of this would be necessary to convince the reader that the method does not incur significant extra-costs compared to other strategies. \n- A single architecture is chosen for expert classifiers in the vision and text domains. I would expect experiments to show that the discriminator works with at least a few types/architectures as expert classifiers, in terms of both performance and computational cost.", "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 paper presents Generate to Discriminate (G2D), a method that learns a domain discriminator for routing during domain-incremental learning. The proposed discriminator helps to decide which expert classifier to route an example to at inference time. The discriminator is trained on synthetic data from previous tasks. The authors claim that training this discriminator on synthetic data provides almost the same performance than training it on actual task data. Experiments are presented for standard domain-incremental benchmarks, and the proposed method's performance is compared against several baselines including MTL, regularization-based methods, experience replay methods, and diverse oracles.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- Please examine additional continual learning metrics, larger number of tasks, and provide conclusions of the behaviour of the proposed method. \n- Please provide a more comprehensive analysis of the computational cost of the proposed method. \n- Please examine the impact of the chosen expert classifiers on the performance of the system.", "broader_impact_concerns": "No.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "6smoxejF78", "reviewer": "Reviewer_mhFX", "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:\n1. The paper proposes G2D, an effective domain-incremental method that leverages synthetic data to train a domain-discriminator that routes samples at inference time. The proposed method outperforms several previous methods, especially on DomainNet.\n2. The benchmark introduced by this work is more realistic than previous datasets and would be useful for future study of domain-incremental learning.\n\nWeaknesses:\n1. Even though the paper conducts some analysis and visualization in Section 6.1, it lacks more in-depth understanding of why using synthetic data to train domain-discriminator would lead to much stronger performance than using synthetic data to train downstream classifiers directly.", "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": "1. This paper proposes Generate to Discriminate (G2D), a domain-incremental learning method that leverages synthetic data to train a domain-discriminator that routes samples at inference time to the appropriate expert. Combined with a group of models trained with different domain data, G2D could be used in scenarios that require domain-incremental learning. Experimental results show that G2D outperforms several domain-incremental learning methods in both vision and language modalities.\n2. The paper conducts careful analysis to understand the domain discrimination boundaries of G2D. While leveraging synthetic data is not a new idea, one contribution of the paper is the empirical evidence that using synthetic data to train a domain discriminator is more effective than using the same synthetic samples to train the downstream label classifier.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. Question: How many generators the method need to keep if there are $N$ domains?\n2. Following up with the question, I think the paper could be strengthened if there are more discussions about factors besides the performance. For example, compared with using synthetic data to train downstream classifiers, the proposed method may require more training or storage budget.", "broader_impact_concerns": "I do not see broader impact concerns in this work.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "jNeJdWHI0i", "reviewer": "Reviewer_RphK", "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": "Strengthens:\n1. The paper introduces a new and effective use of synthetic data—training a domain discriminator for expert routing—rather than the conventional use for data augmentation, which shows consistently better performance.\n\n2. The method operates without access to real data from previous domains, making it suitable for privacy-sensitive applications where data sharing is restricted.\n\n3. The paper includes detailed analysis of domain discrimination performance, showing that synthetic-data-trained discriminators can approach the accuracy of those trained on real data.\n\n\nWeaknesses:\n\n1. The authors are encouraged to add another subsection in the related work to discuss what are the other research directions conducted on different domains, e.g., unsupervised domain adaptation (e.g., [1], [2]), domain generalization (e.g., [3], [4]), to justify what are the major differences among these different task settings when compared with domain incremental learning. Some other prompts (e.g., [1], [2], [6]) based technique to deal with domain gap needs also be mentioned in the related work.\n\n[1] Bai, S., Zhang, M., Zhou, W., Huang, S., Luan, Z., Wang, D., & Chen, B. (2024, March). Prompt-based distribution alignment for unsupervised domain adaptation. In Proceedings of the AAAI conference on artificial intelligence (Vol. 38, No. 2, pp. 729-737).\n\n[2] Du, Z., Li, X., Li, F., Lu, K., Zhu, L., & Li, J. (2024). Domain-agnostic mutual prompting for unsupervised domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 23375-23384).\n\n[3] Peng, K., Wen, D., Yang, K., Luo, A., Chen, Y., Fu, J., ... & Stiefelhagen, R. (2024). Advancing open-set domain generalization using evidential bi-level hardest domain scheduler. Advances in Neural Information Processing Systems, 37, 85412-85440.\n\n[4] Zheng, G., Huai, M., & Zhang, A. (2024, March). AdvST: Revisiting data augmentations for single domain generalization. In Proceedings of the AAAI conference on artificial intelligence (Vol. 38, No. 19, pp. 21832-21840).\n\n[5] Peng, K., Wen, D., Saquib, S. M., Chen, Y., Zheng, J., Schneider, D., ... & Stiefelhagen, R. (2024). Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain Generalization. arXiv preprint arXiv:2412.18342.\n\n\n2. The experiments are only conducted using ViT B, the cross backbone generalization capability needs to be checked. The authors are encouraged to replace the backbone with some CNN architectures to see its performance (e.g., ResNet50).\n\n\n3.  Per-domain performances are not provided, which are also interesting.\n\n\n4. Section 4.2 needs more details to make the method clearer. The authors are encouraged to describe it via algorithm format.\n\n5. The number of expert models grows linearly with the number of domains, potentially introducing significant computational and memory overhead in large-scale settings.\n\n6. The study primarily targets classification problems; the applicability of G2D to other task types (e.g., regression, structured prediction) remains unexplored.", "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": "The paper proposes Generate to Discriminate (G2D), a method for domain-incremental learning that uses synthetic data not to augment classifiers directly, but to train a domain discriminator that routes test samples to the appropriate expert model. This routing mechanism allows for effective handling of catastrophic forgetting without access to real data from past domains, outperforming traditional generative replay and regularization-based methods in both vision and language tasks. Additionally, the authors introduce a new benchmark, DermCL, highlighting challenges in medical imaging with domain shifts, and show G2D's strong performance in such realistic scenarios.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please make corresponding modifications based on the weaknesses I mentioned in the review.", "broader_impact_concerns": "The broader impact of this submission lies in its potential to significantly improve machine learning deployment in real-world, privacy-sensitive domains like healthcare, finance, and autonomous systems, where sharing raw data is often infeasible. By leveraging synthetic data for expert routing instead of classification, the proposed method (G2D) enables effective domain adaptation and lifelong learning without violating data-sharing constraints, making it highly applicable in regulated environments.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)