Your VAR Model is Secretly an Efficient and Explainable Generative Classifier
Authors: Yi-Chung Chen
David I. Inouye
Jing Gao
Venue: ICLR 2026 Poster (2026)
Keywords: generative classifier
generative model
autoregressive model
Primary area: generative models
TL;DR: Your VAR Model is Secretly an Efficient and Explainable Generative Classifier

Abstract: Generative classifiers, which leverage conditional generative models for classification, have recently demonstrated desirable properties such as robustness to distribution shifts. However, recent progress in this area has been largely driven by diffusion-based models, whose substantial computational cost limits their scalability in practice.
To address the efficiency concern, we investigate generative classifier built upon recent advances in visual autoregressive (VAR) modeling. Owing to their tractable likelihood, VAR-based generative classifier enable significantly more efficient inference compared to diffusion-based counterparts. Building on this foundation, we introduce the Adaptive VAR Classifier$^+$ (A-VARC$^+$), which further improves accuracy while reducing computational cost, substantially enhancing practical usability.
Beyond efficiency, we also study several properties of VAR-based generative classifiers that distinguish them from conventional discriminative models. In particular, the tractable likelihood facilitates visual explainability via token-wise mutual information, and the model naturally adapts to class-incremental learning without requiring additional replay data.

Published as a conference paper at ICLR 2026
YOUR VAR MODEL IS SECRETLY AN EFFICIENT AND EXPLAINABLE GENERATIVE CLASSIFIER
Yi-Chung Chen, David I. Inouye, Jing Gao
Elmore Family School of Electrical and Computer Engineering Purdue University
{chen5262, dinouye, jinggao)@purrdue.es
ABSTRACT
Generative classifiers, which leverage conditional generative models for classi- fication, have recently demonstrated desirable properties such as robustness distribution shifts. However, recent progress this area has been largely driven by diffusion-based models, whose substantial computational cost limits their scal- ability in practice. To address the efficiency concern, we investigate generative classifier built upon recent advances in visual autoregressive (VAR) modeling. Owing to their tractable likelihood, VAR-based generative classifier enable signif- icantly more efficient inference compared to liffusion-basee counterparts. Build- ing on this foundation, we introduce the Adaptive VAR Classifier (A-VARC*), which further improves accuracy while reducing computational cost, substantially enhancing practical usability. Beyond efficiency, we also study several proper- ties of VAR-based generative classifiers that distinguish them from conventional discriminative models. In particular, the tractable likelihood facilitates visual ex- plainability via token-wise mutual information, and the model naturally adapts class-incremental learning without requiring additional replay data.
INTRODUCTION
Generative models are trained to directly capture the underlying data distribution of a given dataset, which enables a wide range of applications such as image generation (Han et al.) 2025), image editing (Mu et al.) 2025)), and data augmentation (Trabucco et al. 2023). Given this expressive capa- bility, natural question arises: Can we leverage these powerful generative models for classifica- tion? This question has motivated line of research on the "Generative Classifier." In this paradigm, class-conditional generative models are employed to estimate the likelihood p(rll), where y denotes the class label and the input data. The posterior distribution p(ylz) can then be derived via Bayes' theorem, given the prior p(y). This stands in contrast to conventional discriminative classifiers, which directly model the conditional probability p(y]z). Although generative classifiers less commonly adopted due to the inherent difficulty accurately modeling p(æly)~a substantially harder task than modeling p(y](:))ppi work has shown that they exhibit several advantageous properties distinct from discriminative classifiers.
Early work generative classifiers (Schott al., 2019) employed VAE (Kingma & Welling 2013)
to model the likelihood p(zly) and demonstrated that such classifiers exhibit greater robustness against adversarial attacks compared to discriminative models the MNIST dataset (Deng) 2012).
a finding further supported by Li et al. (2019). In addition, Van De Ven et al. (2021) showed that such generative classifiers achieve superior performance in alass-incremental learning. Building on these successes, subsequent studies explored normalizing flows (Ardizzone et al. 2020 Mackowiak al. (021) and score-based models (Zimmermann et al., 2021) for estimating class-conditional likelihoods, achieving classification performance comparable to discriminative counterparts. More recent work adapted pre-trained text-to-image diffusion models as zero-shot generative classifiers by approximating likelihood through the evidence lower bound (ELBO), revealing desirable properties such as oft-o---iittiuuiion robustness (Li et al.] 2023), attribute binding (Clark Jaini (023), shape bias, and human-like like error consistency (Jaini al. (202)). Further studies have shown that diffusion- based generative classifiers can achieve certifiable robustness (Chen et al. (024a) and are robust to subpopulation shifts (Li et al. 2024), underscoring their promising advantages.

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Despite these advances, research on generative classifiers for image classification remains relatively underexplored. The recent emphasis on diffusion- based generative classifiers introduces two limi- tations. The first and most critical challenge is scalability. By design, generative classifiers suffer from efficiency issues, as their computational complexity grows linearly with the number of classes, which severely limits applicability large-scale datasets such as ImageNet with 1,000 classes. This issue is further exacerbated by the lack of tractable likelihoods in diffusion models. Specifically, the diffusion-basse method adopts ELBO as an approximation of likelihood (Li al. 2023), which involves multiple forward passes. To obtain reliable approximations for classification, diffusion- based methods typically require dozens to hundreds of function evaluations, creating significant barrier to practical deployment. The second limitation is the narrow perspective that arises from the recent exclusive focus on diffusion-base approaches. It is unclear whether the desirable properties reported in diffusion-basel studies are shared across different generative classifiers.
Recent advances visual autoregressive (VAR) modeling (Tian et al., 2024) present promising and efficient backbone for generative classifiers. However, a naive implementation of VAR classi- fier (VARC) yields suboptimal performance. To address this, we propose the Adaptive VAR Clas- sifier (A-VARC), framework designed improve accuracy while reducing computational cost. A VARC integrates two techniques: (i) likelihood smoothing, which enhances accuracy by produc- ing more robust likelihood estimates, and (ii) partial-scale candidate pruning. which accelerates inference by exploiting the model's multi-scale architecture for candidate pruning. Together, these components form flexible and efficient generative classifier that outperforms the naive VARC base- line. In addition, we introduce A- A-VARC* an enhanced variant finetuned using the recently proposed Condition Contrastive Alignment (CCA) method (Chen al.) 2024b). With these improvements, A-VARC+ achieves accuracy comparable to the DiT-based (Peebles & Xie 2023) diffusion clas- sifier on ImageNet-100 ioncuurrnn less than 1 % drop- while requiring 89x less computational cost. This dramatically reduces the computational burden of generative classifiers and significantly improves their practical feasibility.
Beyond efficiency, we also investigate additional properties of VAR-based generative classifiers. Although we do not observe the same level of distribution-shin robustness reported for diffusion- based approaches, likelihood enables visual explanations via token-wise mutual information, capturing the relevance of individual tokens to the target label. Moreover, unlike discriminative classifiers that typically suf- fer from catastrophic forgetting in class-incrementll learning, VAR-based models, trained to model class-conditional likelihoods independently, naturally adapt to such tasks without requiring replay data. Collectively, these findings highlight new and complementary research directions for genera- tive classifiers.
analysis uncovers other distinctive advantages. In particular, the tractable
The main contributions of our paper are summarized as follows:
We investigate VAR-based generative classifiers and introduce A-VARC+, which further improves both performance and efficiency. Notably, A-VARC- achieves accuracy compa- rable to the DiT-based diffusion classifier while requiring 89x less computational cost.
We conduct a comprehensive evaluation of generative classifiers across multiple model families and datasets under well-controlled setup, providing a clearer understanding of their strengths and limitations.
We show that the tractable likelihood of VAR-based generative classifiers enables visual explainability and allows the model to naturally adapt to class- iaas--nncrementll learning without the need for replay data.
2 RELATED WORK
2.1 GENERATIVE CLASSIFIER
The discussion of generative classifiers can be traced back to Ng & Jordan (2001). who studied
Naive Bayes and showed its superior data efficiency compared to its discriminative counterpart. Subsequent research has explored generative classifiers built upon different backbone architectures.
Early works (Schott et al. 2019 Li et al.] 2019; Ghosh et al. 2019) employed VAEs to model
the likelihood and demonstrated strong adversarial robustness.
Van De Ven al. (021) further
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showed that such classifiers achieve superior performance in inss-incremental learning. Follow-up studies investigated normalizing flows. Fetaya et al. (2200) highlighted key limitation of con- ditional likelihood-
generative classifers, noting that class-conditional information may be underutilized when trained with a maximum-likelihon objective. Ardizzone et al. (2020) showed that this issue be alleviated by introducing reweighted discriminative term, and Mackowiak] et al. (2021) demonstrated that such design enables additional features such as explainability and out-of-distribution detection.
More recently, the rapid progress of diffusion models has motivated their adoption for generative classification. Zimmermann et al. (202I) derived class-conditional likelihoods via reverse SDE, showing improved performance on CIFAR-10 (Krizhevsky et al.) (009). Alternatively, [Li et al. (202)) employed the BLBO as a proxy for likelihood estimation, demonstrating robustness to distri- bution shifts. Follow-up works (Clark & Jaini, [2033] Jaini et al., 2023) further highlighted intrigu- ing properties such as human-like shape bias and error consistency with human judgments. Recent studies extended these findings by showing that ELBO-based diffusion classifiers can achieve sub- stantial improvements in certified robustness (Chen et al. 2024a) and mitigate shortcut learning caused by spurious correlations (Li et al.] 2024). While some studies have explored autoregressive generative classifiers in NLP tasks (Li et al. [2024] Kasa et al. (025). the recent investigation of autoregressive-baseee generative classifiers for image classification remains limited, with [Jini al.] (2023) providing only preliminary results. In this work, we study VAR-based generative classifiers, which provide new perspective on the development of generative classifiers.
2.2
IMAGE AUTOREGRESSIVE MODEL
For image generation, autoregressive models transform the intractable problem of modeling all pixel dependencies simultaneously into tractable sequence of prediction tasks. [arochelle & Murray (201T) demonstrated the feasibility of building neural autoregressive models for image generation. Follow-up works (Van Den Oord
2016 Van den Oord et al. 2016 Salimans al. 2017) intro- duced architectural improvements and performed next-pixel prediction in a raster-scan manner. The development of VQ- VA-V (Van Den Oord al.) 2017) further enabled encoding images into shorter sequences of discrete tokens, greatly improving scalability. Subsequent works (Razavi et al.) [2019] Ramesh et al.) 2021 Esser al. 2021 Sun et al. 2024) demonstrated the outstanding generative capability of such models. Moving beyond conventional next-token prediction, Tian et al. (2024) proposed visual autoregressive (VAR) modeling with next-scale prediction, which generates im- ages in a multi-scale, coarse-to-fine order and achieves performance superior to earlier approaches. Building upon this advance, we show that, with its tractable likelihood and next-scale prediction, the VAR model can also serve efficient and explainable generative classifier.
PRELIMINARY
3.1 GENERATIVE CLASSIFIER
Given an image-label pair (x. y), the goal of classifier is model the conditional probability p(y(z) for classification. Unlike discriminative classifiers, which directly learn this distribution, generative models trained to estimate the class-conditional likelihood p(xly). Using Bayes' theorem, the parameterized posterior pe (aylz) can then be expressed as:
Po ys (1)p(s)) po (ye xx) E, Pa(z (z v,)p(1;)
(1)
where p(y:) denotes the class prior. A common assumption is that the prior uniform across all classes, in which case the prediction of a generative classifier is obtained by:
arg max (e y:) ye
Note that performing classification using Eq. 2 requires the conditional likelihood Pa (z (y.) for every possible class yi, and thus the computational complexity scales linearly with the number of classes.
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3.2 DIFFUSION CLASSIFIER
For diffusion models, the class-conditional likelihood p(ælyy is intractable. To address this, the dif- fusion classifier Li et (2023) employs the evidence lower bound (ELBO) as a surrogate objective. The classification decision can then be obtained as:
arg min Ex, [ M1) - -el - - (O(,
(3)
where Eg the noise prediction model and is the timestep used for determining the noise level. The intuition is that with the correct class condition, the noise prediction error will be smaller. However, obtaining a reliable estimate typically requires dozens to hundreds Monte Carlo samples, resulting in substantial computational overhead.
3.3 IMAGE AUTOREGRESSIVE MODEL
Autoregressive models provide principled way to represent the complex distribution of high- dimensional data by factorizing into a product of one-dimensional conditional distributions. To model the likelihood p(zly), image is first tokenized into sequence of tokens (I1, T2. (LL)) The class-conditional likelihood can then be expressed as:
p< v) p(11, 2L v)
π p(a: 01, ... ,I-11
(4)
A common tokenization strategy is to use quantized autoencoder, such VQ- VAE Van Den Oord et al. 2017), which converts an image feature map f E hxx into discrete tokens E [V V typically ordered in raster-scan fashion. However, [Tian et al. (2024) identifies limitations of raster- scan ordering, including loss of structural information and inefficiency, and instead proposes next- scale prediction. In this approach, a feature map f /xxxxxC is quantized into K multi-scale token maps (T1, T., ('K ), each with progressively higher resolution h2 X Wk, culminating in rk, which matches the original resolution w. Specifically, for given image C the tokenization process is defined as:
f E= (r1,72, (Tk) 2ff).
(5)
where E-) denotes the encoder and 2(-) the quantizer. The multi-scale token maps can be projected back pixel space reconstructed image through codebook and decoder D(-) as follows:
= lookup( Z, (r1, "1,,r,,"" ,rk)), a =
(6)
where lookup(Z,* means taking the corresponding vector in codebook Z. For high-capacity VQ- VAE models, the difference between æ and à is generally negligible. Therefore, the VAR model trained on the discrete token set, which is formulated as:
p0( v) po(ri,r2. ... ,TK (v) ΠI (o 11,,2,"" ,rr-,,
where each F%k E [V]h* x is the token map at scale k. containing hy x wy tokens.
4 ADAPTIVE VAR CLASSIFIER
With the tractable likelihood defined in Eq 7 VAR model can be directly converted into VAR classifier (VARC) using Eq 1 Since the token maps (r1, T2, ... ,",, are readily available after to- kenizing test image with VQ-VAE, the likelihood can be estimated with single forward pass, making VARC a more efficient classifier compared to diffusion-based methods. However, this naive adaptation yields suboptimal performance. To address this, we first introduce the Adaptive VAR Classifier (A-VA (A-VARC), which integrates two key techniques: likelihood smoothing and sert candidate pruning. By design, A- V-RACC adaptively balances accuracy and efficiency, offering sig- nificant improvements over the naive VARC baseline. We then present A- VARC+ an enhanced variant that applies CCA finetuning achieve further performance gains.
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4.1 LIKELIHOOD SMOOTHING
While Eq 7 provides formulation for likelihood estimation, we observe that it lacks smoothness and may lead suboptimal performance. To illustrate this issue, we add small perturbation to the feature map f. producing noised feature map frosse and reconstructing corresponding image anoise as follows:
= f noise D(lookup(2 (Z, Q(froiie)))
(8)
where NO, o²) is sampled from a Gaus- sian distribution with small variance o². Al- though and gnoise are visually almost indis- tinguishable (see Fig[I. their token maps @(f) and 2 ( differ drastically, with 69% of kens changed. This discrepancy in token maps also causes observable variations the esti- mated likelihood. However, since 2 and groise are perceptually similar, the corresponding like- lihoods should ideally differ only slightly in der to yield stable classification.
Clean
With Noisa
Figure Visual comparison of images with and without noise perturbation.
To address this problem, propose smoothed class-conditional likelihood defined as:
(a. v) = (o((f (1) (((,00
where S is the number of samples used for smoothing. Our empirical results demonstrate that like- lihood smoothing effectively improves classification accuracy. Although this approach introduces additional computational cost, find that using only small value of S already yields noticeable gains, and the overhead can be further mitigated through the candidate pruning strategy.
4.2 PARTIAL-SCALE CANDIDATE PRUNING
While the tractable likelihood of the VAR model alleviates the computational cost of likelihood es- timation, the most critical efficiency challenge of generative classifiers still remains. Specifically, as shown in Eq. [ the computation cost scales linearly with the number classes, since classification requires evaluating the claas-conditional likelihood for all possible classes. This limitation greatly restricts the applicability datasets with large numbers of classes. To address this, prior works adopt two-stage procedure: first, apply quick but coarse likelihood estimation method to filter out unlikely candidates, and then use a more accurate but computationally expensive estimation on the remaining classes. For instance, Li et al. (2023) employs such strategy by using 25 samples to approximate the ELBO in Eq. for all classes, followed by 250 samples for the top-5 candidates identified in the first stage to refine the predictions.
Inspired by this idea, A-VARC adopts a sim- ilar but more aggressive candidate pruning strategy. Unlike conventional autoregressive image generation models, the VAR model employs next-scale prediction, which gener- ates images in a coarse-to caase-to--fine multi-scale or- der. This design encodes global structural in- formation each scale with varying resolu- tions. Because each scale contains global in- formation, we find that the partial informa- tion in the first few scales is often sufficient to discriminate between classes with large vi- sual differences (e.g., distinguishing tench from a hen). This observation motivates a more efficient pruning strategy. Specifically,
100
10 Accuracy
GFUUPS
120
Computation Cost (GFLOPS)
Top-10 Accuracy 196)
-80
60-
Scale
Figure 2: Top-10 accuracy and computation cost vs. number of scales.
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introduce partial-scale likelihood approximation, defined as:
K'
Po. (2 v) = T2, 72. TK ()) = II Po(TR 11, ,3--1.9)
(10)
where K' < K denotes the number of scales used. Fig. 2 shows the top-10 accuracy and per- image computation cost on ImageNet- 100 across different values of K". The results indicate that the approximated likelihood fo. (xly) with a small K' achieves comparable top- accuracy to the full-scale likelihood, while offering substantial efficiency improvements, making it well suited for candidate pruning. The efficiency gain stems from the reduction in token length: the full multi-scale token maps contain 680 tokens, whereas the first five scales include only 55 tokens-abow 8% of the total- due the smaller h& and W/ at lower resolutions. This partial-scale pruning strategy significantly reduces the computational burden A VARC, allowing resources to be focused on the most likely candidates.
4.3 CLASS INFORMATION ENHANCEMENT VIA CCA
One possible factor contributing to the suboptimal performance of VAR-based classifiers is that class-conditional information may be underutilized when training with maximum-likelihodd ob- jective, as discussed in (Fetaya et al. 2020). Prior work has attempted to address this limitation by incorporating an additional discriminative term into the training objective strengthen class infor- mation (Fetaya et al. 2020; Ardizzone et al., 2020, Mackowiak et al. 202Tb. While this approach does enhance class-conditional information and improve classification accuracy, the improvement comes at the expense of generation ability. A similar phenomenon is observed in image generation tasks, where conditional image generation results may not strictly align with the given condition when sampling directly from the conditional distribution. A common remedy in image generation is classifier free guidance (Ho & Salimans (2022), which enhances conditional information by extrap- olating between conditional and unconditional distributions. However, find that this technique is ineffective for generative classifiers and, in fact, degrades performance through our empirical results and also reported by previous work [i al.) (2023). We hypothesize that this is because faasiffier-free guidance sharpens the density of a subset of the distribution to produce visually ap- pealing images, but weakens the model's general likelihood estimation ability, also argued by (Krraas et al. 2024).
In this work, we want find a general solution that enhances the class conditional information and benefits both the generation and classification tasks. Fortunately. we found that the recent advance in VAR finetuning provides an ideal solution. Chen et al. (2244) introduced a novel finetuning objec- tive called Condition Contrastive Alignment (CCA), which was originally proposed to enhance the class-conditional information through finetuning technique to eliminate the necessity of classifier- free guidance. Specifically, given pretrained conditional generative model ps. the CCA objective encourages the target model po to strengthen class-conditional information as follows:
bigg8 [3log log Pe 12] Alog -= log (2 1999))
Cg CCCA (x, ye0)) = - log ssig
(1I)
where sig(() denotes the sigmoid function, and | and are hyperparameters. The pretrained model Ps remains fixed during finetuning. Intuitively, the first term encourages the model to increase the likelihood under the ground-trum -ruth label y. while the second term penalizes high likelihood under an incorrect label neg, This objective effectively reinforces class-conditional information in condi- tional generative models. Our empirical results demonstrate that applying CCA to A-RARC further improves classification performance by guiding the model to focus on more object-relevant regions, as illustrated in Fig. 6 in the Appendix. We denote this enhanced version as A-VARC
5 COMPARATIVE ANALYSIS
In this section, we conduct detailed experiments to evaluate the performance of the proposed A- VARC+ and verify if the robustness property is transferable our VAR-based method.
Datasets and Evaluation Metric. We evaluate the proposed A VARC across a diverse set of datasets to assess its performance from multiple perspectives. For general classification ability,

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report results on ImageNet- 100, randomly sampled subset of ImageNet (Deng et al. 2009), ) pro- vided by (Tian et al. 2020) with 50 samples per class. This smaller subset enables us to conduct methods that have higher computational cost for a fair comparison. To evaluate robustness, we con- duct experiments on five distribution-shiff datasets: ImageNetV2 (Shankar et al. 2020), ImageNet- R (Hendrycks al.] (202Ta), ImageNet-Skett
(Wang et al. 2019), ObjectNet (Barbu et al.] 2019), and I (Hendrycks et al. 2021b) For these datasets, we evaluate on subsets of 100 classes, except for ObjectNet,, where we use the 113 classes overlapping with ImageNet. To accelerate eval- uation, we use 10 samples per class. Top-1 accuracy and per-image computation cost (in GFLOPs) are reported as the performance and efficiency metrics, respectively.
Baselines and Implementation Details. In this work, we compare the proposed method against both discriminative and generative classifiers. We focus on models trained on ImageNet to conduct a fair comparison. For discriminative classifiers, we include ResNet-18. ResNet-34, ResNer-50, and ResNet-101 (He et al.) 2016), as well as ViT-L/32, ViT-L/16, and ViT-B/16 (Dosovitskiy et al.) 2020). For generative classifiers, we evaluate IBINN (Mackowiak et al.] 2021). normalizing flow- based generative classifier, and the diffusion classifier (Li et al. 2023). For IBINN, we use the model trained with 3 1. For diffusion-
generative classifiess diffusion and rectified flow. In the diffusion case, we follow the setup of (Li et al. (023) (DC) and adopt DiT-XI/2 (Peebles & Xiel 2023) at resolution 256 the backbone. We report results under two settings: (i) ELBO estimation using 25 samples, and (ii) the two-stage approach pro- posed in the original paper, where 25 samples are used to select the top-5 candidates followed by 250 samples for refined prediction. For the rectified flow-based implementation, we report results using MeanFlow (Geng et al. 2025) (DC-MF) with a S backbone, which enables evaluating whether improved sampling effiiincy translates into better classification performance. In this case, the noise prediction error in Bis replaced with the velocity prediction error associated with the rectified flow formulation, and 25 samples are used for error estimation. For A-VARC+, we use the first stage,
examine two families:
VAR-d16 at resolution 256 as the backbone and adopt a three-stage procedure. use pe a (x111) to narrow down the candidates to the top 10. In the second stage, use pe (z yy) to select the top 3 candidates. In the final stage, we apply pa.a (x(1) with o = 0.1 for the final prediction. Please refer Sec. E in Appendix for more details.
Quantitative Results. Table I summarizes the comparison results. On ImageNet- 100, the proposed A-VARC improves both the accuracy and efficiency compared to the naive implementation VARC and achieves accuracy comparable to that of the 2-stage DiT based diffusion classifier, with less than 1 1% drop, while requiring 89x less computational cost. The efficiency gain primarily arises from the tractable likelihood and the candidate pruning strategy, whereas the enhanced accuracy can be attributed to likelihood smoothing and CCA finetuning. By contrast, IBINN attains the highest efficiency by modeling class- conditionll likelihoods with Gaussian Mixture Model, which enables fast classification via cluster distance computation but leads to substantially lower accuracy. It is worth noting that although rectified-flor -flow models such as MeanFlow exhibit superior sampling efficiency for image generation compared to diffusion models, this advantage does not translate to improved performance in rectified flow-based diffusion classifiers. When evaluated with the same number of samples for error estimation, DC-MF performs significantly worse than the DiT-based counterpart. Our analysis suggests that McanFlow suffers from higher prediction error compared to the DiT-based diffusion classifier. A possible explanation is that rectified- -etified-flow models are trained to approximate marginal velocity fields using supervision from conditional flows, as discussed in (Geng et al. 2025). This training mismatch may introduce additional noise into the error estimation, weakening the class-conditional signal and ultimately degrading classification performance.
In terms of robustness, consistent with the findings of (Li et al. 2023), generative classifiers exhibit improved robustness to adversarial shifts in ImageNet-A compared to ResNet-based models. How- ever, for other distribution-shin datasets, the VAR classifier does not demonstrate any noticeable advantage. This suggests that the robustness property reported in diffusion-basee methods (Jain] et al. 2023 Li et al.) 2024) does not generalize to VAR. Interestingly, the DiT-based diffusion classifier significantly outperforms all discriminative models except ViT-L/32 on ImageNet-Sketdel highlighting its robustness to shifts from natural images to sketches. Neither IBINN nor A- VARCC exhibits this behavior, which implies that the observed robustness likely originates from the denois- ing training paradigm of diffusion models rather than from the generative objective itself. Finally, on ImageNet and its variants variants-including ImageNet-V V2, ImageNet-R, RR, and ObjectNet Coreeteeee-iisciinnatiiiii models continue to achieve superior overall performance. This persistent gap highlights that gener-
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ative classifiers remain underexplored direction with considerable room for improvement. Nev- ertheless, given the rapid advances in generative modeling, we expect that generative classifiers will benefit from these developments and gradually narrow this gap in the near future.
Table 1: Comparison on ImageNet and across multiple distribution shifts.
ImageNet
Distribution shifts IN-Sketch
Method
Top-1
GFLOPs
IN-V2
IN-R
ObjectNet
IN-A
ResNet18
88.44
1.8
79.1
41.2
43.8
26.02
3,6
ResNet34
89.96
3.7
81.3
40.9
46.1
30.62
5.1
ResNet50
91.90
4.1
83.4
44.0
45.5
34.69
2.0
ResNet101
92.14
7.8
84.8
44.6
50.3
36.99
6.8
ViT-L/32
91.92
15.3
83.9
51.3
55.2
30.44
14.4
ViT-L/16
93.22
59.7
86.0
49.4
49.7
33,98
18.0
VIT-B/16
94.20
16.9
86.9
52.5
52.0
36.28
21.7
IBINN
51.12
9.2
40.9
13.2
14.6
3.98
3.2
DC-MF
50.30
296861.3
44.0
4.5
3.9
10.27
4.8
(25)
86.32
286287.6
75.8
33.1
51.6
22.65
13.7
DC
90.30
415056.0
80.6
38.3
53.7
29.38
16.2
VARC 83,30
14105.0
71.9
30.6
36.0
19.47
10.3
A-VARC+
89.32
4649.4
79.3
33.1
34.0
24.51
10.0
Ablation Study. Table presents the ablation study the two accuracy enhancement techniques adopted by A- A-VARC* likelihood smoothing and CCA finetuning. To focus on the analysis of accuracy gain, the partial-scale candidate pruning technique is not applied in this experiment. Likelihood smoothing is applied only to the top-10 candidate classes, selected based the class- conditional likelihood (2(()) from a standard forward pass. We use 10 samples for smoothing, additional samples yield diminishing returns. The results show that likelihood smoothing sistently improves the performance the baseline for all datasets, though at the cost of increased computation. In contrast, CCA finetuning enhances in-domain accuracy ImageNet and closely related datasets such as ImageNetV2, ImageNet-R, and ObjectNet, but slightly reduces performance on ImageNet-A and ImageNet-Sketch. This suggests that CCA finetuning encourages the model emphasize class-specific information, thereby improving discrimination within the training distribu- tion but reducing generalization larger distribution shifts.
Table 2: Ablation study on likelihood smoothing and CCA finetuning.
Smooth (S=10) CCA
ImageNet
Distribution shifts IN-Sketch
Top-1
GFLOPs
IN-V2
IN-R
ObjectNet
IN-A
83.30
14105.0
71.9
30.6
36.0
19.47
10.3
88.26
28210.0
77.1
33.6
40.4
24.78
11.0
88.68
14105.0
80.3
34.5
34.8
25.75
9.9
89.72
28210.0
81.2
33.9
36.0
26.73
10.9
INTRIGUING PROPERTIES
In this section, discuss the intriguing properties of the VAR-based classifier that distinguish from conventional discriminative classifiers.
6.1 VISUAL EXPLAINABILITY
The tractable likelihood of the VAR model inherently provides visual explainability. The concept of pointwise mutual information (PMI), defined as log p(zly) has been widely used NLP tasks (Church & Hanks 1990 Levy & Goldberg, 20144) to measure word associations. Here, we show that this concept can be naturally extended by the VAR-based classifier to provide visual explanations.
p(z)
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The goal of visual explanation is capture fine one-grained associations between local image regions and a target label y. thereby clarifying why the model makes a particular decision. Since autore- gressive models compute token-wiss wise likelihoods, can extend pointwise mutual information to token-wise mutual information (TMI, which measures the association between a token and a label as follows:
Po (t.5) log (r( (1(3) 71, ". rk-1)
11. ". ,k-1,9
(12)
(3,5)
(6.5) denotes the (ith) token of the k-th scale token map. This ratio can be obtained for each token with only two forward passes. Moreover, this concept be extended to contrastive explanations, which highlight why a prediction is made in favor of one class over another. Fig. 3 illustrates that token-wise mutual information effectively identifies regions strongly associated with the label "little blue heron", thereby revealing the basis of the model's prediction. It also provides contrastive evidence by explaining why the image is classified as "little blue heron" rather than "goose". This offers direct and interpretable insight into the decision-making process of the VAR classifier. Please refer Fig. 7 and Fig. 8 in the Appendix for more visualization results.
where re
Imput
log
Iime blun heroti
log
Porigaaee
PiS
log
nofitte bie hwreel peggeceo
Figure 3: Visual explanation of A-VARC+ using TMI. From left to right: the input image, TMI conditioned on the true label, TMI conditioned on the highest-ranked incorrect label, and the con- trastive explanation between them.
The token-wise mutual information can viewed as an attribution method that produces an attribu- tion score for each token. To evaluate attribution quality, adopt the insertion and deletion metrics introduced (Petsiuk et al.] 2018), which are widely used in the explainability literature. Tokens are first sorted according to their attribution scores; then, they are gradually inserted or removed to measure the change in the predicted probability of the ground-truth class. The area under the curve (AUC) is used for evaluation. Intuitively, for the insertion metrie, higher AUC is preferred, as it indicates that tokens with high attribution scores meaningfully support the ground-truth class. For the deletion metric, lower AUC is preferred, removing highly attributed tokens should de- crease the ground-trum -ruth probability. We additionally report LIME (Ribeiro et al. 2016) and SHAP (Lundberg & Lee 2017) as baselines. Please refer to Sec. C Appendix for more details.
Table 3 presents the quantitative analysis of the visual explanation methods. For A-VARC, LIME demonstrates the strongest overall performance, while TMI performs comparably to SHAP on the insertion metric and achieves the second-best score on the deletion metric. For A-VARC TMI outperforms all other methods on both insertion and deletion metrics. These results are consistent with the qualitative observations in Fig. d where TMI becomes more focused on class-relevant regions after finetuning, leading to improved explainability.
Table 3: Quantitative analysis of visual explanation methods. The reported metrics correspond to the average urea-under-the-curve (AUC) averaged across the ImageNet- 100 dataset.
Metric
LIME
SHAP
TMI (Ours)
Insertion (1)
0.979
0.853
0.845
A-VARC
Deletion (1)
0.192
0.432
0.346
Insertion (f)
0.939
0.902
0.944
A-VARC+
Deletion (L)
0.614
0.746
0.605

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6.2
LEARNING
Unlike discriminative classifiers, which rely on unified softmax layer across all classes to ob- tain logits for classification, generative classifiers base their predictions solely on class-conditional likelihoods. Since these likelihoods can be learned independently for each class, generative classi- fiers naturally adapt to class- classinncrementl learning without suffering from catastrophic forgetting, as noted in (Van De Ven et al.] (021). This provides distinct advantage over discriminative models, which are vulnerable to catastrophic forgetting and typically require storing a portion of past data as "rehearsal" to preserve performance.
To investigate whether recent generative classifiers exhibit similar behavior, we conduct a proof- of-concept class-increnen incremeaatt learning experiment the first 10 classes of ImageNet. The classes are partitioned into two tasks, each containing 5 classes. Discriminative classifiers are trained se- quentially, first Task 1 and then on Task For generative classifiers, two separate models independently trained on each task, following the setup in (Van De Ven et al. 202T). We evaluate both diffusion-basse basse and VAR-based generative classifiers. Specifically, we use ResNet-50 as the discriminative baseline, DiT-S/2 for the diffusion classifier, and VAR-d8 for the VAR classifier. All models are trained from scratch for 1,000 epochs, except D/T-S/2, which is trained for 2,000 epochs. Afterward, the VAR model is further finetuned with CCA for an additional 10 epochs.
As shown in Table 4 without rehearsal data, the discriminative model suffers severe catastrophic forgetting. Although methods such CWR (Lomonaco & Maltoni (017) can mitigate this issue, their reliance on fixed feature extractors limits performance on new tasks. contrast, the generative classifiers, trained to model (lass-condition
likelihoods independently, adapts naturally new tasks and achieves promising performance without requiring additional data or complex techniques. This provides promising solution for creating a unified classifier by simply merging classifiers trained on different datasets, making it capable recognizing an expanded set of classes without retraining. Compared the VAE used in previous work Van De Ven et al. (202T), the VAR model, along with the techniques of A- A-VARC+ provides powerful alternative for future research in class- incremental learning.
Table 4: Class-incremental learning experiment the first 10 classes of ImageNet.
None Task2
CWR
DC Task2
A-VARC*
Task1
Avg
Task1
Task2
Avg
Task
Avg
Task1
Task2
Avg
0.0
82.4
41.2
83.2
61.6
72.4
78.4
73.6
76.0
72.4
82.4
77.4
CONCLUSION
In this work, we investigate VAR-based generative classifiers and propose A- A-VARC+, which further improves both accuracy and efficiency, achieving performance comparable to DiT-based diffusion classifiers while requiring substantially less computational cost. Although analysis indicates that VAR-based classifiers do not inherit certain properties exhibited by diffusion- models, such robustness to distribution shift, uncover other notable characteristics, including visual explainability and natural adaptability to inaseiincremetall learning. These findings offer a deeper understanding of the strengths and limitations of generative classifiers and point toward promising directions for future research.
REPRODUCIBILITY STATEMENT
To ensure reproducibility, we release our implementation at https:://ithhb htt.gitttu.com// com/ along with detailed instructions in Sec. F of the Appendix for ob- taining the evaluation subsets used in our experiments. We also provide the pseudo-code A- VARC in Algorithm I For discriminative baselines, pretrained models publicly available in
Vi-Chung-Chen/A--Vas
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torchvisior] The code and pretrained weights for IBINN DC VAR and CCA are also accessible via their respective official repositories.
ACKNOWLEDGEMENTS
This work is supported in part by the US National Science Foundation under grants NSF IIS- 2141037 and IIS-2212097, the US Army Research Laboratory under award 191NNF-0000221 and the Office of Naval Research under contract N00014-23-C-1016 Any opinions, findings, and conclusions or recommendations expressed this material are those of the author(s) and do not necessarily reflect the views of the sponsors.
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org abs/ abs/aaab/1111 2110. 00473
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A USE OF LARGE LANGUAGE MODELS
In this work, we made limited use of large language models (LLMs) to assist with writing and ref- erence search. Specifically, LLMs were used polish the text for clarity and readability, and to conduct preliminary surveys for identifying relevant references. All generated content was carefully reviewed and edited by the authors to ensure that faithfully reflects the intended meaning. Like- wise, all references retrieved through LLM-assisted searches were manually verified for accuracy and alignment with the described content.
B ADDITIONAL ABLATION STUDY
B.I LIKELIHOOD SMOOTHING WITH VARYING NUMBERS OF SAMPLES
Following the setting in Table 2 we provide an additional ablation study using different values of S for likelihood smoothing Table 5 For A-VARC, the accuracy increases S grows and saturates at S = 16, yielding an overall improvement of 5.12%. For A-VARC+, saturation occurs earlier at 1.06%, indicating that the gain from smoothing is reduced when combined with CCA. In both cases, the results consistently show that applying likelihood smoothing improves classification accuracy.
= with a smaller improvement
Table 5: Ablation study of likelihood smoothing with varying numbers samples.
S=1
S=2
S=4
S=8
$=10
S=16
S=32
A-VARC
83.30
85.30
87.18
88.18
88.26
88.42
88.42
A- A-VARC*
88.68
89.42
89.30
89.74
89.72
89.54
89.72
B.2 VARIANCE FOR LIKELIHOOD SMOOTHING
Likelihood smoothing averages the likelihoods of neighboring samples in the latent space to pro- mote local smoothness. The neighborhood size is controlled by the variance parameter σ. Table reports an ablation study of A-VARC over different a values. A small a limits smoothing to nar- row neighborhood, while large may undesirably average semantically dissimilar samples. Empirically, we find that a 0.1 provides the most favorable results.
Table 6: Effect of varying the likelihood smoothing variance
0.01
0.05
0.1
0.5
1.0
Acc 85,50
87.72
88.26
84.38
39.54
B.3 PARTIAL-SCALE CANDIDATE PRUNING WITH LIKELIHOOD SMOOTHING
We explored using more samples during the candidate pruning stage by combining likelihood smoothing with partial-scale pruning. The results are provided in Fig. A It shows that although using more samples can slightly improve the top-10 10 accuracy, it substantially increases computa- tional cost. Therefore, recommend using S = at this stage to achieve better efficiency.
C VISUAL EXPLAINABILITY
C.I IMPLEMENTATION DETAILS OF QUANTITATIVE ANALYSIS
To compute attribution scores, LIME Ribeiro al. 2016) and SHAP Lundbergg & Lee 2017) per- turb the features of interest and measure the corresponding variation in the target function. In our setting, we apply these explanation methods estimate an attribution score for each token, using the predicted probability of the ground-truth class as the target function. For each perturbation, binary mask vector M (mn()))) is used to select subset of tokens. The predicted probability of
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Computation Cost (GLLOPS)
500
Top-10 Accuracy (8))
400
80
320
70-
200
50
10
Number Scales
Number of Scales
(a) Top-10 10 accuracy vs, number scales
(b) Computation cost vs. number scales
Figure 4: Comparison of top-10 accuracy and computational across different numbers of scales for varying values of S.
the ground-truth class, (yge xx M), is computed as follows:
1o(x y, M) = Σ
mi (4.))
log po (6.5) 11,22, ". ,Tk-1,,)
(13)
(i.1.))
exp(la(x Yot M))
(y xx, M)
(14)
Σ. exp(1o(x yo M))
(1.1) E {0, is a binary indicator determining token inclusion, and lo(x y, M) denotes where my the mask-aware log- logeliielooodd of x. LIME then fits linear model on the perturbation outputs obtain attribution scores, whereas SHAP uses Kernel SHAP to approximate Shapley values. For both methods, we use 5,000 perturbations during evaluation. The resulting attribution scores are then evaluated using the insertion and deletion metrics. An example AUC curve for these metrics is shown in Fig.
Note that we precompute the token-wiss wise log-lik logelikelshoods and apply the masking strategy in Eq. I3 approximate the effect of token insertion or deletion. This allows efficient computation of token attributions while avoiding potential out-of-distribution artifacts that may arise from directly altering sequence.
1.0
1.0
Ground Thats Class Tribabito
Groued Truts Clane Pictebilty
Ground Trulh Claa
8.4
0.2
0.370
0.4222
0.4976
0Bn
00g
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1.0
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0.
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AUC = 0.9794
AUC 0.7675
060
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000
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Permestage of Touess (TMI)
Figure 5: Insertion/delltion analysis of explanation methods. The first row shows insertion results,
and the second row shows deletion results. Methods from left to right are LIME, SHAP, and TMI.
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C.2 VISUALIZATION RESULTS
In this section, we provide additional visual explanations of the VAR-based classifier. Fig. 7 and Fig. @illustrate that the classification results are indeed driven by regions corresponding to the fore- ground object. For example, in the second row of Fig. 7 the example is correctly classified as a green mamba, not because of the background green leaf, but because the model focuses on the snake's head.
We also analyze failure cases in Fig. The most common type of error occurs when the model fails to distinguish between visually similar classes, such as hen vs. cock or chihuahua Even when the model correctly identifies important regions for each class, it may still produce an incorrect final prediction. Another common error arises in scenarios where multiple candidate objects appear in the same image. For instance, the ground-trume label of the third-row example 'modem', but the presence of a laptop in the same image misleads the classifier into predicting 'laptop'. A similar issue is observed in the last example, where the co-occurrence of a tabby cat and a bassinet results in an incorrect prediction.
toy terrier.
These examples demonstrate that visual explanations can provide valuable insights into the decision- making process of the VAR-based classifier, enabling developers to better understand model behay- ior and make informed adjustments during development.
D TRADE-OFF BETWEEN GENERATIVE AND DISCRIMINATIVE PERFORMANCE.
The trade-off between generative and discriminative performance in conditional generative clas- sifiers has been discussed in prior work (Fetaya et al., 2020). While this phenomenon is less pronounced in the VAR-d16 model, it becomes increasingly evident as model size grows. Ta- ble repoorss the performance of VAR classifiers with different model sizes, including accuracy on ImageNet- 100 and the FID) reported by Chen et al. (202bb) (without likelihood smoothing or can- didate pruning). As model size increases, generative performance improves, as reflected by lower FID, but classification accuracy ImageNet- 100 drops substantially.
This degradation can be attributed to the dilution class-conditional information by structural in- formation. Specifically, the class-conditional likelihood of each token depends on both class and structural information. Larger models, with stronger generative capacity, are able to more accu- rately infer tokens at subsequent scales when conditioned an incorrect class label. This is evidenced by the simultancous increase in both p(zly) and p(r "es) as model size grows. As a result, the contribution of class information to likelihood estimation diminishes, leading to weaker discriminative ability. As illustrated in Fig. TO larger models increasingly fail to distinguish between visually similar classes, such as cock vs. hen or great white shark vs. tiger shark.
To address this issue, one promising direction is to improve the training objective so that it more effectively preserves class information. While recent advances such CCA represent meaningful step in this direction, our results indicate that CCA alone is insufficient to fully resolve the problem. Another complementary direction is to disentangle class information from structural information, thereby preventing the dilution effect associated with increased likelihood. We leave the exploration of these directions to future work.
Table 7: Trade-off between generative and discriminative performance of the VAR classifier across different model sizes.
dl6 VAR
d20
d24
d30 VAR
CCA
VAR
CCA
VAR
CCA
CCA
Accuracy
83.30
88.68
80.80
88.90
75.68
84.92
64.96
71.64
FID
12.00
4.03
8.48
3.02
6.20
2.63
5.26
2.54
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Inpust
Hajendi 2011
log
Melgelaoon 2011
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Figure 6: Impact of CCA finetuning. From left to right: the input image, TMI conditioned on the true label, TMI conditioned the highest-ranked incorrect label, and the contrastive explanation between them.
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Input
log
Pl
log BOYEE BOKEY
log Atei
log #ichaahhspert
log Pitason TurDOa
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log
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Figure 7: Visual explanation of A-VARC+ using TML. From left to right: the input image, TMI conditioned on the true label, TMI conditioned on the highest-ranked incorrect label, and the con- trastive explanation between them.
E PSUEDO CODE
Algorithm D outlines the classification procedure of the proposed Adaptive VAR Classifier (A- VARC). The likelihood estimation strategy is composed of three forms of likelihood estimation:
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Input
log Alcchccatee OUCH
PM
log
log
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Figure 8: Visual explanation of A- A-VARC+ using TML. From left to right: the input image, TMI conditioned on the true label, TMI conditioned on the highest-ranked incorrect label, and the con- trastive explanation between them.
(o(v))) (Eq. (xly) (Eq. 9 and (x (Eq. 10). This flexible design enables wide
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Input
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Ro
Multicle Objects
Figure 9: Visual explanation for failure cases. From left right: the input image, TMI conditioned on the true label, TMI conditioned on the highest-ranked incorrect label, and the contrastive expla- nation between them.
range of combinations, allowing users to balance accuracy and efficiency according to their quirements. For the experiments reported in T we adopt a three-stage configuration with Nstage 3, IC (10,3, 1) and M (fo.a(>ly),, Po (xrl)) (o.3(<l))) Note that, while the al- gorithm is presented using likelihoods for clarity, in practice, we compute log-likelihoods to ensure numerical stability.
F IMPLEMENTATION DETAILS
To enhance reproducibility, we provide details of the subsets used in Table [ For ImageNet, ImageNet-V2, V2, and Imagehet-Sketch, we adopt the same set of classes provided by Tian et al. (2020), as listed in Table Since ImageNet-A and ImageNet-R do not include all classes from ImageNet-100, select the overlapping classes and list them in Table 9 and Table respectively. For ObjectNet, we use all overlapping classes reported in Barbu et al. (2019), with implementation support from the diffusion classifier's s (Li et al. 2023) repository
To reduce evaluation cost on distribution-shi datasets, we further subsample 10 samples per class. For most datasets, we sort file names alphabetically and select the first 10 samples per class. For
httpp:: http:s:://iittubbooom com cttp://iosfuuiii-ccclaassss httr//ifffaaa on- assi fieifier
21

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Published as a conference paper at ICLR 2026
Confusion Matrices VAR Model Size Impact-
Dataset: imagenet 10 Setup: standard
VAR-d16 (Base)
VAR-d20
Accuracy. 0.882
Accurocy: 0.862
tanch: 0.56 0.00 0.20
0,00
0.00 0.0 0.00 0.00 0.00 0.00
tench 0.95 0,00
0.00
0.00 0.00 0.00 0.00 0.02 0.00 0.00
colditsin 0.00 0:96
0,00
0.00
0.00 0.00 0.02 0.00002000000000 0.00
goldfish -0.00 0.05
0.00
0.00
0.00 0.00 0.00 0.00
0.8
white k 0.00
0.00
0.000
0.66
0.04
0.00
0.00
Bioo shark 0.00
0.00
tier shark 0.000
0.82
Than Label
hammert ead - 0.00 0.02
0.060.88
08.000000
0.02
0.00
0.00
Thue Label
hammerheed -0 0.00 0.04
0.50
0.00
0.02
0.00
00.00.0.00
0.00
0.6
electricc ray - 00 0.02 0.30
0.04
0.02
0.90
0.02
0.00
0.00
0.00
electric ray -0,00 6.02 0.00
0.06
0.00
0:70
0.16
0,00
0.00
0.4
stingray 0.02
0.00
0.00
0.02
0.2
0.2
hen co 3.00 0.00 0.00 0.00 0.00 0.12
0.80
0.00
hen -0 0000 0.00 0.00 0.00
0.22
0.002
ostmic -0 Co 0.00 0,00 0.00 0.00 0.00 0.00 0.00 0.00
1:00
octrich -0,00 0o 0.00 e 00 0.00 0.00 0.00
0.02
0.900
0.0
0.0
terret
great white staan
gatoffon
tiger sharr
hamnerteed
electric ray
stingr by
herr
comoo
tareet
great white shark
gondtist
tiger shark
fammer electricc
stnngray
cook
Ner
ostrch
Predicted
VAR
VAR-d30
Accuracy: 0.
Accuracy: 0.774
1.0
1.0
teach 0.50
0.00
0.00
tench 0.94
0.00
goldiish -0.00
1:00
0.20
0,00
0.00
0.00
0.00
0.00 0.00 0.00
goldfiih -0.02 0.84
0.04
0.00
0.02 0.04
0.04 0.00 0.00 0.00
great white sha lk 0.00
0.76
0.20
0.02
0.00
0.00
0.00
0.02 0.00
white shark -0.00 6.00 0.76
010 0.04
0.02
0.00 0.00 0.00
shark
0.00
0.00
Thue label
hammmoth
0.80
0.00
0.6
True Labol
hammerhead -0.30
0.6
elecuric ray 00.00 0.04 0.30
0.02
0.02
0.84
0.08
0.00
0.00 0.00
electric ray - 0.02 0.06
0.08
0.16
0.00
0.00
0.02
stingray - 00 0.00 0.34 0.04
0.02
0:12
0.76
0.02
0.00 0.00
stingray 0.02 0.00 0.04 0.06 0.04
0:34
0:70
0.00 0.0 0.00
0...0...22222 0.00
0.00
0.000 0.00
0.04
0.2
0.2
ostrich -0.00 0.00 0,30 0.00 0. 0.00 0.000000000000 0.00 0.02
astrich -0.00 0.00 0.00 o 00 0.00 0.00 0.00 0.00 0.00
1.00
fendh
great white shark
guieriin
tiger stark
stingray
cookk
Hum
great white sherk
goldfish
tiger Shart
harmanhhaad
stlggray
clork
Hem
Getree
eleartic
electric
Predicted Label
Predicted Label
Figure 10: Confusion matrices of the VAR classifier evaluated on the first 10 classes of ImageNet.
Algorithm Adaptive VAR Classifier (A- (A-VARC)
Require: Test image x; Initial set of candidate labels y = {yr > Number of stages Nstages;
Sequence of candidate counts IC- (k), KNang):: Likelihood estimation strategy M
(m...... :............p. MNg... ).
1: Initialize candidate set Co + )
2: Initialize a map I to store likelihood scores
3: for stage i = 1. ..... Nstuges do
4: for each candidate yj E C2-1 do
5: L(y;) t- ComputeLikclihoodlxxx yΙ method = m;))
P> e.g., computing pe, be. (x w)
6: end for
7: Let C²=, be the set C;-1 sorted by descending scores L(-)
8: C. + the first h; elements of C/
Prune candidates with the lowest scores
9: end for
10: return arg max max, NECngs I(y)
ImageNet-R, which includes multiple styles (e.g., art, cartoon, deviantart), we first sort styles al- phabetically and then apply a round-robin ordering across styles (e.g., art 0.jp., cartoon aajpp de- viantart 0.jpg....) ensuring that as many styles as possible are represented.
22

---

Published as a conference paper at ICLR 2026
Table 8: List of ImageNet 100 classes used in our experiments, identified by their WordNet IDs (n-numbers).
List of ImageNet 100 classes
n02869837
n01749939
n02488291
n02107142
n1 133377006
n02091831
n04517823
n04589890
n03062245
n01773797
n0173515 189
n07831 146
n07753275
n03085013
n04485082
n02105505
n01983481
n02788148
n03530642
n04435653
n02086910 10
n02859443
n13040303
u03594734
a02085620
a02099849
n01558993
n04493381
n02 109009947
104111531
n02877765
n04429376
n02009229
n01978455
n02 106500
n01820546
n01692333
n07714571
n02974003
n02114855
n03785016
n03764736
n03775546
n02087046
n07836838
n04099969
104592741
n03891251
n02701002
n03379051
n02259212
n07715103
n03947888
n04026417
n02326432
n03637318
101980166
n02113799
n02086240
n03903868
n02483362
n04127249
n02089973
n0301 7168
n02093428
n02804414
n02396427
n04418357
n02172182
nO1 729322
n021 I 13978
n03787032
n02089867
n021 19022
n03777754
n04238763
n02231487
n03032252
n02138441
102104029
n03837869
n03494278
n04136333
n03794056
n03492542
n02018207
nD4067472
n03930630
n03584829
n02123045
n04229816
n02 100583
n03642806
n04336792
n03259280
n02116738
n02108089
n03424325
n01855672
nO2090622
Table 9: List of ImageNet-A 100 classes used in our experiments, identified by their WordNet IDs (n-numbers).
List of Image/Net-A 100 classes
101531178
n01580077
n01616318
n01631663
n01641577
n01669191
n01677366
n01687978
n01694178
n01774750
n01820546
a01833805
n01843383
n01847000
n01855672
n01910747
n01924916
n01944390
n01986214
n02051845
n02077923
n02099601
n02106661
n02110955
n021 19022
m02133161
n02137549
n02165456
n02174001
602190166
n02206856
n02219486
m02236044
n02259212
n02268443
n02279972
n02280649
n02325366
n02445715
m02454379
n02504458
n02655020
n02730930
n02782093
n02802426
n02814866
a02879718
n02883205
n02895154
n02906734
n02948072
102951358
n02999410
n03014705
n03026506
n03223299
n03250847
n03255030
n03355925
a03444034
103452741
n03483316
n03590841
n03594945
n03617480
n03666591
nO3720891
n03721384
n03788195
n03888257
n04033901
n04099969
104318538
nO4133789
n04146614
n04147183
n04179913
n04252077
nO4252225
n04317175
nO4366367
n04376876
n04399382
n04442312
n04456115
n04507155
n04509417
no4591713
n07583066
n07697313
n07697537
107714990
n07718472
n07734744
n07768694
n07831144 146
n09229709
n1 1879895
n12144580
n12267677
Table 10: List of ImageNet-R 100 classes used in our experiments, identified by their WordNet IDs (n-numbers).
List of ImageNet-R 100 classes
n01484850
nO1514859
n01531178
nO1534433
n01614925
n01616318
n01632777
n01774750
n01820546
n01833805
n01843383
n01847000
n01855672
n0186018'
n01882714
n01944390
n01983481
n02007558
n02056570
n02066245
n02086240
n02088094
n02088238
n02096585
n02097298
m02098286
n02102318
n02106166
n02106550
n02106662
n02108089
R02108915
103110341
n02113624
n02113799
n0211711 17135
19022
n02128757
n02129165
n02130308
n02190166
n02206856
n02236044
n02268443
nU2279972
n02317335
n02325366
n02346627
n02356798
n02363005
n02364673
n02395406
n02398521
n02410509
nO2423022
n02486410
n02510455
n02749479
n02793495
m02797295
m02808440
n02814860
n02883205
n02939188 185
n02950826
n02966193
m02980441
n03124170
n03372029
n03424325
n03452741
n0348 133481172 172
n03495258
n03630383
n03676483
103710193
n03773504
n03775071
n03930630
n041 18538
a04254680
n04266014
n04310018
n04347754
n04389033
n04522168
n04536866
n07693725
a07697313
n07697537
n07714571
n077 14990
n07720875
n07745940
n07749582
n07753275
n07753592
n09835506
110565667
n12267677
23


Reviews:
[{"review_id": "dIQlC86HBS", "reviewer": "Reviewer_d2kX", "summary": "Given the existing diffusion classifiers, Adaptive VAR Classifier$^+$ is proposed in this paper as a new variant of generative classifiers. It is claimed to be  efficient, visually explainable and robust to distribution shifts. Experiments are conducted accordingly.", "questions": "1. For likelihood smoothing, why are Gaussian noises used to avoid the sensitivity to the small perturbations introduced by adding Gaussian noises? Could the authors provide an intuitive explanation? Compared to $\\epsilon$ in Equation (8), is $\\epsilon_i$ in Equation (9) scaled? In numerical experiments, could the authors provide an ablation study on $S$?\n\n2. Why is the novelty to apply CCA in the proposed Adaptive VAR Classifier$^+$? It seems that CCA is already proposed and published in Toward Guidance-Free AR Visual Generation via Condition Contrastive Alignment [1] which is also for autoregressive models.\n\n[1] Huayu Chen, Hang Su, Peize Sun, and Jun Zhu. Toward guidance-free ar visual generation via condition contrastive alignment. arXiv preprint arXiv:2410.09347, 2024b.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. **Clarity**: This work is clearly presented with the linear logic. The core motivation and contributions of Adaptive VAR Classifier$^+$ is articulated with examples and comprehensive comparisons to existing generative classifiers. The mechanism of the model is also coherently explained.\n\n2. **Originality**: It is not previously proposed and studied on VAR classifiers. In past research, VAR and its variants are workhorses for generative tasks. To the best of my knowledge, this work is from the original attempts and findings of VAR as classifiers.", "weaknesses": "1. The core novelty of this work should be improved. The idea to propose VAR as a generative classifier follow the same logic as substituting a module in an existing model with a more recent one, in specific, replacing diffusion models in diffusion classifiers with VAR. In Abstract, it should be articulated clearly on what the pressing problem is in classification for this era of large generative models and why proposing VAR as a generative classifier can solve this problem to highlight the significance.\n\n2. From results in Table 1, it seems that for the robustness against distribution shifts, Adaptive VAR Classifier$^+$ does not demonstrate clear advantage compared to diffusion classifiers and traditional classifiers such as ResNets. The claimed contributions on robustness to distribution shifts are not sufficiently achieved in this case.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "EoeukhYLWo", "reviewer": "Reviewer_XVX3", "summary": "This paper considers the problem of generative classification with an autoregressive model. Traditional discriminative classifiers directly model $p(y|x)$, while a generative model can be turned into a classifier via $\\text{argmax} p(x|y)$. This paper proposes a pipeline that has 3 steps: 1) likelihood smoothing (add gaussian noise to the latent features), 2) partial-scale pruning (use first few scales to give rough likelihood estimates for a few classes), and 3) CCA  coupled with a multi-scale autoregressive image model to perform classification. More specifically, to turn an autoregressive image model into a classifier, for each class, we then compute the class conditional log likelihood using the already trained model, and then pick the class with the highest likelihood. The paper shows that this pipeline is resistant to catastrophic forgetting, and that brings a bonus of interpretability.", "questions": "What is the discriminative finetuning method doing? To be more concrete, I think we can interpret this loss function (equation 11) as a contrastive discriminative objective. If we define the score: $s_\\theta(x, y) = \\log p_\\theta(x|y)$, then under uniform priors, the posterior can be expressed as a softmax over scores: \n$$ p_\\theta(y|x) = \\frac{e^{s_\\theta(x, y)} }{\\sum_y's_\\theta(x, y')}.$$ \nWe can then say that the CCA enforces: $-\\log  \\sigma( \\beta[ s_{\\theta(x, y)} - s_{\\theta(x, y_{\\text{neg}})} ])$. This is equivalent to the binary logistic loss used in discriminative contrastive learning setting. To clarify in words, it might be the case that: CCA is discriminative training on log-likelihood ratios, using the generator’s likelihood as the scoring function. This means: CCA finetuning is functionally identical to discriminative contrastive learning, but applied to generative likelihoods rather than logits. \n\nQuestions: \n- Wouldn't this bias the samples from the generative models towards class discrimination rather than sample realism?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The key inspiration behind this paper is the need to move away from a diffusion-centric approach that has limited efficiency. Here are concrete strengths of this work: \n\n- The paper evaluates comprehensively on the ImageNet dataset testing ImageNetV2, R, etc. More concretely, the method proposed here actually is close in top-1 accuracy to baseline methods but almost 2 orders of magnitude faster. \n- The pipeline proposed here is quite simple and easy to put together for common autoregressive models. The insight that using an autoregressive model allows for a factorization of the exact likelihood that is a nice one, and the use of the next-scale prediction approach from Tian et. al. is critical for this work. \n- The interpretability benefits from this work also are impressive. Because VAR models have tractable likelihoods, the paper define token-wise pointwise mutual information (PMI) to quantify how much each token (or image patch) contributes to the predicted class. This is an interesting side benefit of this approach. \n- Finally, the paper is quite well written. In particular, the introduction and the related work guide the reader through the motivation for this work.", "weaknesses": "The weaknesses are provided below: \n- The focus of the evaluation is primarily on the Imagenet dataset using 50 images per class. Is this standard in this literature? I wonder whether more extensive evaluations can be performed in other settings. \n- The interpretability claims are interesting, but would need more than qualitative images to thoroughly validate. \n- CCA Fine-tuning might be doing discriminative training in disguise. See the question section for more details on this.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zgziMO33ES", "reviewer": "Reviewer_kzRp", "summary": "The paper proposes a VAR-based generative classifier (A-VARC+) that adds likelihood smoothing, partial-scale candidate pruning, and CCA finetuning. It argues for better efficiency than diffusion-based generative classifiers and offers token-level PMI explanations and a small class-incremental learning demo.", "questions": "see weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Clear motivation for exploring autoregressive generative classifiers.\n\n2. Techniques are simple, practical, and likely easy to reproduce.\n\n3. Tractable likelihood enables straightforward token-wise visual explanations.\n\n4. Broad empirical sweep (in-distribution + several shift datasets).", "weaknesses": "1. Technical novelty feels incremental; mainly a combination/adaptation of known ideas.\n\n2. Accuracy advantages are limited; strong discriminative baselines still perform better.\n\n3. Robustness/CL claims are weakly evidenced (small-scale setups, mostly qualitative).\n\n4. Limited analysis of trade-offs (e.g., compute/memory vs. accuracy) and few quantitative explainability metrics.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2PVF2VF32m", "reviewer": "Reviewer_1Nsc", "summary": "This paper leverages Vector Auto Regressive (VAR) models, a class of generative models to the classification task by applying Bayes rule.", "questions": "Please refer to weakness section.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Strengths of var:\n\n- Faster (81x) and better quality compared to diffusion models  [Tian paper ]\n- AR models have tractable likelihood\n\nThis paper, puts these strengths of the VAR models to use in the task of explainability and classification.", "weaknesses": "### There are multiple claims in the paper that are not validated.\n\n**Superior Tradeoff / Pareto Dominance**\nLet $a, b \\in \\mathbb{R}^2$ with objective values\n$f(a) = (f_1(a), f_2(a))$ and $f(b) = (f_1(b), f_2(b))$.\nWe say that $a$ has a superior tradeoff to $b$\n(or $a$ Pareto-dominates $b$), denoted $a \\prec b$, if\n$f_1(a) \\le f_1(b), \\quad f_2(a) \\le f_2(b), \\quad \\text{and} \\quad \\exists j \\in \\{1,2\\}: f_j(a) < f_j(b).$\n\nHowever on the accuracy front VAR does not dominate. so at best you can say tradeoff and lies on Pareto front but cannot say Superior Tradeoff.\n\n**Ablation on other candidate pruning**\n\nCandidate pruning is a popular technique in diffusion classifier. There are multiple ways to prune, and more the pruning Figure 4.[https://arxiv.org/pdf/2303.16203] and more samples in the pruning process better the performance. You will get the same Figure 2 if you do uniform scale, the performance improves with number of selected scales. Can you provide experiments on various pruning techniques to validate the partial-scale hypothesis discussed in Section 4.2\n\n**Ablation on Smoothness**\n\nI agree with the authors that the adversarial noise, causes token drift. Is the model evaluated on Adversarial noise? No, So this section does not contribute to any new information. Do Authors perform ablation on S= [0, ….. N]? No. So How can authors conclude adding smoothness lead to improved performance?\n\n**Unfair Comparison**\n\n1. Class incremental learning can be seen in other generative models as well\n    \n    Class incremental learning is not just related VAR, but also observed in other types of generative models.  Consider the control net architecture in diffusion models, where you can add additional classes without even adding other model complexity. Can authors put to comparison how VAR model fares on class incremental learning in comparison to ControlNet? \n    \n2. DiT vs VAR\n    1. Over the years there are multiple improvement to Diffusion classifier to address inference speed \n        1. Rectified flow, few steps to clean image ( Instead of sampling 25 noise scales, you can get away with 5 noise scales. )\n        2. Parallelisation, speed of  Diffusion classifier can be significantly improved by Parallelisation.  Pruning to 25 noise scales to an image and for an 100 class classification, you can pass everything in a 25 * 100 times batch and obtain the results quickly. ( time will be much faster ). \n    \n    I agree that even with improvements it is very difficult to beat VAR with respect to inference time, it would also be helpful to mention flops, should be a better metric.\n\n\n**Strong Claims**\n\nI am not sure what does “novel” generative classifier mean? All the works, prior to this method, such as diffusion classifier does not call them as a novel classifier. It is applying Bayes’ rule to generative models to perform classification task.\n\nI don’t agree generative classifiers, particularly diffusion classifiers are not popular they are very popular on zero shot image classification tasks. In addition, diffusion models achieve better compositional generalisation [ https://arxiv.org/abs/2501.05707, https://arxiv.org/pdf/2503.04687]. However, using as a pure classifier, low adoption of generative classifiers can be attributed to the linear scalability with respect to number of classes, and not accurately modelling of $P(x|y)$", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)