Searching Efficient Semantic Segmentation Architectures via Dynamic Path Selection
Authors: Yuxi Liu
Min Liu
Shuai Jiang
Yi Tang
Yaonan Wang
Venue: NeurIPS 2025 poster ()
Keywords: Neural Architecture Search
Semantic Segmentation
Dynamic Path Selection
Primary area: deep_learning

Abstract: Existing NAS methods for semantic segmentation typically apply uniform optimization to all candidate networks (paths) within a one-shot supernet. However, the concurrent existence of both promising and suboptimal paths often results in inefficient weight updates and gradient conflicts. This issue is particularly severe in semantic segmentation due to its complex multi-branch architectures and large search space, which further degrade the supernet's ability to accurately evaluate individual paths and identify high-quality candidates. To address this issue, we propose Dynamic Path Selection (DPS), a selective training strategy that leverages multiple performance proxies to guide path optimization. DPS follows a stage-wise paradigm, where each phase emphasizes a different objective: early stages prioritize convergence, the middle stage focuses on expressiveness, and the final stage emphasizes a balanced combination of expressiveness and generalization. At each stage, paths are selected based on these criteria, concentrating optimization efforts on promising paths, thus facilitating targeted and efficient model updates. Additionally, DPS integrates a dynamic stage scheduler and a diversity-driven exploration strategy, which jointly enable adaptive stage transitions and maintain structural diversity among selected paths. Extensive experiments demonstrate that, under the same search space, DPS can discover efficient models with strong generalization and superior performance.

Searching Efficient Semantic Segmentation Architectures via Dynamic Path Selection
Yuxi Liu 1.2 Min Liu Shuai Jiang Yi Tang Yaonan Wangl.2
School of Artificial Intelligence and Robotics, Hunan University 2 National Engineering Research Center of Robot Visual Perception and Control Technology 3 Department of Data and Systems Engineering, The University of Hong Kong yuxi liuchnu. edu. cn yiitangShku. hk
Abstract
Existing NAS methods for semantic segmentation typically apply uniform opti- mization all candidate networks (paths) within a one-shot supemet. However, the concurrent existence of both promising and suboptimal paths often results in inefficient weight updates and gradient conflicts. This issue is particularly severe in semantic segmentation due to its complex multi-branch architectures and large search space, which further degrade the supernet's ability to accurately evaluate individual paths and identify high-quality candidates. To address this issue, we propose Dynamic Path Selection (DPS), selective training strategy that leverages multiple performance proxies guide path optimization. DPS follows stage- wise paradigm, where each phase emphasizes a different objective: early stages prioritize convergence, the middle stage focuses expressiveness, and the final stage emphasizes balanced combination of expressiveness and generalization. At each stage, paths selected based these criteria, concentrating optimization efforts on promising paths, thus facilitating targeted and efficient model updates. Additionally, DPS integrates a dynamic stage scheduler and diversity-driver exploration strategy, which jointly enable adaptive stage transitions and maintain structural diversity among selected paths. Extensive experiments demonstrate that, under the same search space, DPS can discover efficient models with strong generalization and superior performance.
Introduction
Semantic segmentation is a fundamental task in computer vision which focuses on pixel-level classification of images. In recent years, deep neural networks have achieved impressive success in semantic segmentation [1, 2, 3]. critical factor behind this progress is the design of network architectures, which greatly impact overall performance. However, designing architectures that both accurate and efficient often require substantial expertise and iterative experimentation.
To address this challenge, neural architecture search (NAS) has been proposed to automatically discover optimal architectures and reduce the need for manual effort. Current NAS methods [4, 5, 6, 7, 8, 9] typically embed the search space into one-shot supernet and optimize it through weight sharing. Compared to traditional NAS methods, which usually require training thousands networks, one-shot NAS significantly reduces the computational cost while having superior performance.
Nevertheless, the search space in NAS enormous, especially for multi-branch segmentation networks (e.g., FasterSeg [8], with search space of 1095). Such space makes it difficult for the supernet's parameters to adapt to different subnets (paths) [10], leading to gradient oscillations [11].
Eqqual contribution. Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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0.030
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2.4
0.020
Valdagen
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60
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100
0
22
42
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Step
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(a) Validation loss
(b) Angle score
Figure 1: Comparison of different performance proxies on a fixed path during the initial stage of supernet training. (a) Validation loss. (b) Angle score.
Additionally, the simultaneous presence of suboptimal and promising paths in the supernet further complicates the optimization process, as weak paths interfere with the highly shared weights [12].
To avoid these issues, some methods [13, 14] introduce a multi-path sampler, which samples multiple paths simultaneously during supernet training and filters poor-pertie poor-performing ones, prioritizing training on paths with higher quality. These approaches solely rely on the validation loss to determine whether path is promising. For example, GreedyNAS [13] computes the loss on only 2% of the validation set and use it as an evaluation metric. However, the limited sample size leads to high variance in performance estimates, making this metric crreliable-especiallee during the early stages of training. As shown in Fig. la, we randomly sampled path during supernet training and observed significant fluctuations its validation loss in the early stage, pattern consistently found across other paths. While using a larger portion of the validation set could help reduce this variance, it would also incur substantial computational costs. Furthermore, relying solely on validation loss fails to capture a network's 's full potential from multiple perspectives. As suggested by recent study [15], the design of effective proxies should account for three key factors: expressiveness, generalization performance, and convergence.
In this paper, we have proposed corresponding proxies to quantify these three factors. For convergence, inspired by ease-of- the distance between initialized and trained weights, indicate the convergence behavior of a given path. As shown in Fig. 1b, this metric provides more stable signal than validation loss, exhibiting steady increase throughout training. For expressiveness estimation, an accuracy predictor is employed to generate relevant scores. Finally, leveraging insights from information bottleneck (IB) theory [19], we design a multi-scale IB (MS-IB) objective function tailored for segmentation models to assess their generalization capacities.
hypothesis", we adopt the angle score [17, 18], a metric that measures
However, all three proxies cannot be uniformly utilized throughout supernet training, their importance and liability evolve across different training phases. For instance, the angle score tends stabilizes in the mid-to-late stages, most paths have already converged by then, making it less discriminative for evaluating path quality. Similarly, the curacy predictor used for expressiveness estimation must be trained on representative path samples. Nevertheless, in early training stages, the supernet produces unstable and noisy paths, which lead unreliable training data for the predictor. As result, accurate expressiveness estimation only becomes feasible once the supernet has reached a more stable phase. (Experimental evidence can be found in Appendix).
KZ: XX, Z)
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Mutual Information
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9.1
0.0
59
1.00
150
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250
300
350
400
Epoch
Figure Changes mutual information during supernet training.
Moreover, according to the IB theory [19], well-generalized model should compress the input while preserving only the task-relevan features necessary for accurate prediction. To characterize this process, we monitor the mutual information between the input and intermediate representations (i.e., feature maps), denoted as [Xx; ; Z), and between the representations and the output, denoted
A strong correlation exists between rapidly converging architectures and high- performing ones [16].

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as YY) As shown in Fig. 2, [[X; 2) first increases and then declines during early training. suggesting that the model first retains imput information fit the data, and subsequently compresses imelevant features to improve generalization. At this point, the model has not started compressing redundant information, making MS-IB a relatively less accurate proxy for evaluating generalization.
Building on these empirical observations, we present a novel dynamic path selection (DPS) strategy that progressively shifts focus across these proxies throughout different stages of supernet training. Specifically, our method emphasizes convergence in the first stage, expressiveness in the middle stage, and balanced combination of expressiveness and generalization in the final stage. As an additional practical consideration, FLOPs is included as model complexity proxy across all training stages,
To realize this multi-stage, multi-objectio framework, DPS integrates three key components. First, path selection is based on Pareto optimality, identifying non-dominated paths that achieve favorable trade-offs among multiple objectives. Second, dynamic stage scheduler adaptively transitions between stages based on improvement trends and dynamic thresholds, eliminating the need for manual scheduling. Finally, prevent the path search from collapsing into narrow search space, we propose a diversity-driven exploration strategy that encourages sampling of structurally diverse paths.
Our main contributions are summarized as follows:
We propose DPS, a selective training strategy for one-shot NAS in semantic segmentation, which dynamically shifts the selection focus across convergence, expressiveness, and gener- alization throughout supernet training. This stage-wise design enables comprehensive path evaluation and focuses optimization on high-quality paths in an adaptive manner.
Three distinct proxies are proposed to evaluate path quality from multiple perspectives, aligning with the three stages of DPS. These metrics provide explicit feedback signals that guide the supernet training, helping to identify and optimize for promising paths during the search process.
Extensive experiments demonstrate that DPS achieves -fate-ofthhe-- results within the same search space. The architectures discovered by DPS exhibit strong generalization, deliver superior performance, and maintain high efficiency.
2 Related Work and Background
In one-shot NAS, the architecture search space A is encoded into an over-parameteriized supernet (AA, W), where W denotes the set of shared weights across all candidate paths. The supernet is trained using weight-sharing strategy and the corresponding training objective can be formulated as:
W = arg min Limin (N(A, W)),
W
where Lusin is the loss function on the training set. Once the supernet has converged, the optimal path a" can be obtained from the supernet:
a = arg max Acc,a wa)),
(2)
aeA
where w, is the weight inherits from WA and Acc,al denotes the performance evaluated the validation set.
As summarized in [20], one-shot NAS approaches mainly fall into two categories, distinguished by the way supernets are represented and optimized. The following provides an overview cach.
bradient-based. Gradient-based NAS methods, such as DARTS [4], perform joint optimization of supernet parameters and weights through gradient descent within differentiable search space. Due to their efficiency and end-to-ent nature, most NAS approaches for semantic segmentation [7, 21, 22, 9, 23] adopt this gradient- based paradigm. However, such joint optimization complicates the training of the supernet and inevitably introduces biases that may mislead the architecture search [6]. Moreover, these methods suffer from high GPU memory consumption [13] and struggle to incorporate different architecture constraints (e.g., FLOPSs, latency and energy consumption) during the search process [6]. Instead, they often turn to relaxed regularization terms [24, 5], which offer limited efficacy in ensuring strict compliance with various constraints.
3

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Sampling-basee bastdd Sampling- sanpling-based NAS typically search on discrete search space, and decouple the supernet training and architecture search into two separate stages. In the first stage. supemet was trained using different sampling strategies, such as uniform sampling [6, 25] or multi-path sampling [26, 27, 13, 14, 12] with different priorities. During this process, each sampled path is optimized individually, which alleviates the weight coupling issue observed in gradient- NAS. In the second stage, typical search algorithms (e.g., evolutionary algorithms) are applied to identify the optimal path. Unlike gradient-based NAS, where hard constraints are difficult to enforce, sampling-base NAS can flexibly incorporate various resource constraints into different search algorithms.
3 Method
In this section, we will present our sampling-based NAS method named DPS. First, we begin with introducing different proxies evaluating convergence, expressiveness, and generalization capacity of a candidate path. Then we discuss the path selection process in detail, which includes three main components: (1) a pareto optimality-basee selection strategy, (2) diversity-driven exploration mechanism, and (3) dynamic stage scheduling approach for supernet training. Finally, the search pipeline based on evolution algorithm will be explained in short.
3.1 Proxies for Path Evaluation
Angle Score. To ensure a steady path selection in the early stage of supernet training, inspired by the cuse-of-convergence hypothesis, we introduce an angle score [17, 18] to evaluate path convergence.
Specifically, we compute the angle between the initial weights wwo and the trained weights w for path
(V wo), V(a, w)) (a l22 [|V w) w))l ((t) na)
angle(a) (a) arccos
(3)
where V(a, wo) and Va, w) are the concatenated weight vectors, obtained by flattening and stacking all weights along the path from input to output. However, in the early stages of training, certain paths may be sampled more frequently due the stochastic nature of the search algorithm. This imbalance leads to overestimated angle scores for those paths- not because they converge better, but simply because they are updated more often. Such unfair comparisons can mislead the search process and favor paths with initialization advantages or those sampled more frequently due to randomness.
To mitigate this effect, we incorporate a sampling penalty term that decays with training steps, thereby reducing the impact of early-stage randomness and sampling imbalance on the convergence assessment. Here, no denotes the sum of sampling counts for all individual components (c.g., operations, connections) along path a, rather than the number of times the entire path is sampled- which is practically negligible due to the enormous search space (e.g.. 1068). The decay factor X(t) a function of training step f, and it follows a linear decay schedule. The parametera controls the overall penalty intensity.
Performance Predictor. To achieve accurate assessment of path expressiveness while avoiding the high computational cost of validation set evaluation, train performance pre-
1.2
Spoch 260 (C-6 S-d
Spoch 530 (ow0.2s Swo,00
0.8
dictor once the search process enters the second stage (c.g.. 200th epoch). The following are the training details: (1) we begin by randomly sampling 1000 paths and measuring
0.4
their actual accuracy through the current supernet. (2) we
then split these samples into training and test sets at an 8:2
ratio. (3) Finally, we train random forest regressor [28] on the training set and evaluate its performance on the test set. The regressor is configured with 100 decision trees and no maximum depth limitation.
Tue Values (nomallzed)
Figure 3: Rank correlation between the pre- dicted values and the true values.
To evaluate the reliability of the performance predictor, we report Kendall's and Spearman's computed on the test set. Furthermore, to ensure that the regressor maintains strong predictive capability in later stages, we randomly sample 200 paths at the 850th training epoch of the supernet

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and measure their actual accuracy. The previously trained regressor (from the 200th epoch) is then used to predict their performance. As shown Fig. the random forest regressor achieves high rank correlation in both early and late stages (e.g., K = 0.76 S = 0.92 at epoch 200; K = 0.73 S = 0.90 at epoch 850), indicating consistent and reliable performance throughout the search process.
Information Bottleneck. Generalization is a fundamental property of deep neural networks (DNNs), as it determines how well model performs on unseen data. According the IB theory [19], DNNs achieve strong generalization by progressively compressing the input information and discarding iusk-irrelevant redundancy. This compression encourages the learning of compact and informative representations, which are less prone to overfitting and more likely to generalize well. To explicitly model this trade-off between information compression and task relevance preservation, the IB framework introduces a formal objective function:
CiBip(z|z)] -
where [(X; Z) measures the mutual information between the input X and the learned representation Z, and Y( captures the informativeness of Z with respect to the target output Y. The parameter 3 controls the trade-off between compression and prediction. Mutual information I ((;;8 Y) can be defined as the Kullback-Leibler divergence between the joint distribution px,y and the product of marginal distributions px & py:
PX, (a. y) px 2 ppy (y)
T(X; Y) DE (Px,y |px pr) Exx. [log
(5)
This theoretical foundation motivates us to leverage the IB principle as a measure of generalization in our DPS framework. Specifically. by calculating the mutual information between input images X and feature maps Z, as well as between feature maps Z and output label Y, we construct an IB-based proxy that can select paths with better generalization capabilities. However, it is impossible to acquire exact distribution of X, Y and Z.
Inspired by [29, 30], we approximate [)B(p(=[x)] by estimating a lower bound of T T(X; Z) and an upper bound of / (2;Y) Y). This choice aligns with the IB objective: the lower bound on [(;; Z) avoids overestimating compression and losing informative input features, while the upper bound on [[Z; Y) prevents overestimating task relevance, ensuring sufficient predictive information is retained. As mentioned in [29], KL divergence has dual representation known the Donsker- Varadhan representation:
(PI()))Q) sup (Er(T) (og(Eole")) T-Q-
where T' is any function from the space Ω to R such that the expectations finite. To make the problem tractable, we parametrize the function T using a neural network, denoted as To. Thus, T(Z;Y) can be estimated as:
I(2;Y) 2 Epz.y [To) log(Epespy l (eT
The network parameter 9 are optimized by maximizing the objective function using gradient ascent (see Appendix A for details).
Having estimated the dependency between feature maps and labels, now turn the second term of the IB objective function. Since the direct computation of mutual information is generally intractable due the unknown joint distribution px. z. we adopt a variational approach known as variational Contrastive Log-ratio Upper Bound (vCLUB) [30], which provides a tractable upper bound of the mutual information. Specifically, it introduces variational distribution (g(=(rr) (z)) approximate the true conditional distribution p(2 2)), and derives the following inequality:
T(X; Z) Exx.2 [log {=(=[=r)] Epx Opz (=(=)))
(8)
In this formulation, the first expectation is taken over the true joint distribution px,z, while the second is over the product of marginals px pz. The variational estimator qs (z) is implemented as neural network parameterized by Φ. and is trained to tighten the bound by maximizing the right-hand side of Eq. 8 (see Appendix A for details).
Multi-Scale Information Bottleneck (MS-IB). Based on Eq. (7) and Eq. (8), we implement an effective estimation of the Information Bottleneck (IB) objective. Nevertheless, this estimation is performed on a single-scale feature representation, often extracted from the final layer before the segmentation head.
5

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For dense prediction tasks such as semantic segmentation, relying solely on the final representation Z may be suboptimal. This task requires capturing both fine-grained local details and high-level global context. Modern segmentation architectures [31, 32, 2, 33] typically exploit multi-scale feature maps extracted from different stages of the backbone network. Only using the final representation will neglects the potential redundancy or relevance encoded in other features with different scales.
To address this limitation, we propose the MS-IB, which extends the IB estimation to multiple stages of the segmentation network:
9 MS-I Σ
where S denotes the total number stages considered. This formulation enables a more comprehen- sive evaluation of the generalization capacity of different paths.
3.2 Dynamic Path Selection
Path Selection with Pareto Optimality. We formulate path selection as a multi-objective optimiza- tion problem and adopt Pareto optimality to identify non-dominated candidates. At every selection step (every & iterations), we randomly sample m candidate paths from the supernet and evaluate them across multiple snbectives-includinn expressiveness. convergence, and generalization ability- using proxies defined in 3.1. Computational cost (FLOPs) is also considered as an additional constraint. We subsequently determine the set of non-dominated paths among the candidates. A path a; is said to dominate another path aj if and only if:
Va C, C, Toz Fayz
(10)
3x C. Ta. Tayy
where c, e, 9. and f denote the proxies for convergence, expressiveness, generalization, and negative FLOPs, respectively. The set non-dominated paths forms the Pareto front, representing architectures that achieve optimal trade-offs among the competing objectives. Note that in Eq. (10), the metric is used consistently throughout the entire supernet training process, while the other metrics are activated only during their respective stages.
Our objective to select the top-k paths from the Pareto front. If the number of non-dominated candidates is fewer than k. we supplement the selection with additional high-quality paths, chosen based on a composite score calculated by summing their ranks across all proxies.
Diversity-driven Exploration. To prevent the path selection from collapsing into narrow and suboptimal regions of the search space, we introduce diversity-driven exploration strategy that promotes structural diversity and balances exploitation with exploration.
Let S = {ai, ....... } } be the set of m candidate paths sampled at each training iteration, and let h, denotes the discrete structural encoding of path a,, we define the pairwise structure distance between two paths and aj using weighted Hamming distance:
Dist a3)== = Σ Ihe + *n!91,
(11)
where L is the maximum encoding length (with padding applied as needed), wi is weight for the 1-th position and [[|] represent the indicator function. Then we estimate a diversity score of each path by computing its average pairwise structure distance within the sampled set:
K
1
diversity(a,)
Dist(s 8z),
(12)
Finally, the path with the highest diversity score is considered for replacement. If its score exceeds predefined threshold n, replaces the worst-performing path in the top-k set to maintain diversity while preserving performance.
Dynamic Stage Scheduling. To maximize the utility of each proxy and facilitate effective, adaptive path selection, we propose dynamic stage scheduling mechanism that prioritizes different proxy

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metrics at different training stages, Specifically, the mechanism focuses on convergence in the first stage, expressiveness the middle stage, and both expressiveness and generalization in the final stage.
Unlike methods that enforce rigid transitions between training phases, our approach dynamically adjusts the selection focus based on learning dynamics. Stage transitions are determined by monitoring the improvement trend of the current main metric over sliding window of size twin. A transition triggered when the average improvement drops below a dynamic threshold:
T = E stddtoooyyyy--eeeeeoooorol tttwi :]),
(13)
and remains non- non-negative. Here, history(-furir :] denotes the most recent fuvi values of the current metric, and is sensitivity coefficient that controls the responsiveness of the threshold to historical variation. Note that, in stage II, the performance predictor is used solely for path ranking and does not provide estimates of relative improvement over time. Therefore, we instead use the training loss to assess the improvement trend and determine whether stage transition should occur.
This approach dynamically adjusts the threshold based on the volatility of the metric: high variance delays path switching to allow for further optimization, while low variance enables timely transitions when performance improvements plateau.
The details of overall dynamic path selection strategy is given in the Appendix B.
3.3 Evolution Search with Elite Population.
To better initialize the population in the evolutionary algorithm and improve search efficiency, we maintain an elite population M after entering final stage of the training process. Specifically, in each iteration, the top-k paths are added to this elite population. If the population exceeds the predefined size limit [M], the oldest individuals are removed to maintain fixed capacity. This strategy ensures that high-quality paths are preserved and reused during the search process, thereby accelerating the discovery of optimal paths. The algorithm details of evolution search refer to Appendix C.
4 Experiments
4.1 Experimental Settings
Datasets. We conduct experiments three widely used semantic segmentation datasets: Cityscapes[34]], CamVid [35], and BDD100K[36]. Cityscapes contains 2.975 training, 500 val- idation, and 1,525 test images with resolution of 1024 2048, annotated with 19 semantic classes. CamVid consists of 367 training, 101 validation, and 233 test images at 720 x 960 resolution, labeled with 11 categories. BDDI00K provides 7,000 training and 1,000 validation images (resolution: 720 x 1280), also annotated with 19 semantic classes. All models are trained without external data such as ImageNet [37].
Search Space. For a fair comparison, we adopt the same search space from SqueezeNAS [21] and FasterSeg [8]. The first search space use inverted bottleneck [38] as the basic building block and have five different factors, including kernel size, expand ratio, dilation ratio, groups and network depth. The second search space supports multi-branch architecture design. It is based
L-layer cell framework, where each branch operates at a distinct resolution scale. Within each branch, every cell has five different operation choices. The searchable downsample rates are allowing flexible control of input resolution and feature hierarchy.
E (8, 16, 32).
Implementation Details. The pipeline of our method is divided into three stages: (1) Supernet
training with dynamic path selection. (2) Evolution search with elite population. (3) Network retraining. In concrete, once the supernet is fully converged, we will perform evolution search on to obtain the optimal path. For more details on the training and search settings, please refer to Appendix C and D.
4.2 Ablation Studies
Searching on same search space. demonstrate the effectiveness of DPS, we first compared it with other one-shot NAS method using the same search space, including samlping-basee gradient-based
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Table 1: Comparison of one-shot NAS methods on Cityscapes validation set and Can Vid set, categorized into sampling- based (S), gradient-bers based (G), and training- feening-free (F) approaches; GFLOPs measured using an input size of 1024 x 2048.
mIoU (%)
Method
Type
GFlops
Cityscapes
CamVid
SqueezeNAS Search Space
Random Search
F
17.32
69.8
70.7
Uniform Sampling
S
17.10
71.6
71.8
Loss Based [13]
S
12.57
72.7
73.1
SqueezeNAS [21]
G 10.86
72.4
73.2
Ours
S 10.96
74.5
73,8
FasterSeg Search Space
Random Scarch
F
31.48
69.7
67.9
Uniform Sampling [6]
S
31.77
71.2
69.9
Loss Based [13]
S
30.64
70.9
71.8
FasterSeg [8]
G
28.20
73.1
SasWOT [39]
F 29.34
71.3
64.3
Ours
S
29.59
73.2
72.1
and training-frre methods. Note that 'Loss Based' refers to using the validation loss an evaluation metric to filter out suboptimal paths, as done GreedyNAS [13]. As shown in Table 1, our DPS achieves the best performance on both search spaces. On the SqueezeNAS search space, our method outperforms the original gradient-based SqueezeNAS by 2.1% 11 on Cityscapes and 0.6% on Cam Vid, while maintaining nearly the same computational cost (FLOPs). For the FasterSeg search space, [8] employed knowledge distillation to further boost accuracy, whereas our approach achieves slightly higher accuracy without relying on extra training strategies. These results demonstrate that DPS achieves an excellent trade-off between accuracy and computational efficiency, thereby showcasing its effectiveness in architecture search.
Effect Different Proxies. analyze the effect of different proxies, we conduct ablation studies on each component using various proxy combi- nations. The results are summarized in Table 2. observe that increasing the number of proxies significantly improves the network performance. Specifically, the combination involving all three proxies achieves the best result, yielding an mloU of 74.5%. In contrast, relying on a single proxy results in noticeably inferior performance. These findings highlight the importance of evaluating paths from multiple perspectives. By incorporat- ing more proxies into the path selection process,
Table 2: Comparison of different proxy combina-
tions Cityscapes dataset.
Proxies
Stage
GFlops
mloU (%)
14.33
72.8
2
13.82
72.6
3
11.91
72.8
1 1-+2
12.51
73.8
1-+3
12.12
73.4
2-43
11.28
73.1
1 1-+2-3
10.96
74.5
our method is able to capture complementary signals during training, enabling a more comprehensive assessment of path quality. This, in turn, enhances the effectiveness of the architecture search and leads to better-performing models.
Effect of Diversity Exploration and Dynamic Stage Scheduler. We further study the impact of our proposed diversity-driver exploration (DDE) and dynamic stage scheduler (DSS). As summarized in Table both strategies contribute to performance improvement, with DSS showing a more significant effect. This indicates that adaptive stage switching is critical for ensuring the reliability of the evaluation metrics. Using inappropriate fixed-stage switching strategies greatly reduce metric reliability, potentially leading to suboptimal path selection.
Effect of MS-IB. To investigate the impact of MS-IB on model generalization, we design two different experiments. First, we evaluate model's robustness against various common corruptions and perturbations following the experimental setup in [40]. Specifically, we construct benchmark containing 16 types of algorithmically generated corruptions across four categories: noise, blur, weather, and digital. Under this setting, we compare the model's performance using single-scale

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Table 3: Ablation study of diversity-driven exploration and dynamic stage scheduler.
Table Ablation study of different IB configura- tions in transfer learning.
DDE
DSS
GFlops
mloU
mloU (%)
Method
GFlops
10.82
72.8
CamVid
BDD100K
11.47
73.4
None
4.12
67.7
43.5
10.31
73.8
Single-scale
3.72
70.2
44.9
10.96
74.5
Multi-scale
3.61
69.7
45.2
Table 5: Comparison of model performance under various corruptions across IB configurations.
Blur
Noise
Digital
Weather
Method
Avg
Marion
Defoc
Glass
Gauss
Gauss
Impul
Shot
Speck
Bright
Contr
Satur
JPFG
Snow
Frost
3.4
34.7
15.6
Singlo-scale
63.3
58.8
66.5
4,9
7.4
5.0
58.3
50.8
53.9
38.8
149
504
31.5
18.4
37,9
Multi-scale
65.0
63.7
58.6
66.9
6.3
53,4
37.9
14.4
35.3
Table 6: Comparison of different NAS methods on Cityscapes. denotes the model is reduced for
acc-efficiency trade-offs, implemented by [41].
mloU (%)
Method
Flops
Resolution
Val
Test
Auto-Deeplen [7]
27.29
512 x 1024
71.2
HR-NAS-A [41]
1.91
512 1024
74.2
CAS [22]
768 x 1536
71.6
70.5
GAS [42]
769 x 1537
71.8
FasterSeg [8]
28.20
1024 x 2048
73.1
71.5
SqueezeNAS-Lace [21]
10.86
1024 x 2048
72.4
SasWOT [39]
29.34
1024 x 2048
71.3
69.8
Ours
10.96
1024 x 2048
74.5
73.5
IB, multi-scale IB, and without the IB module. As presented in Table 5, the multi-scale IB module outperforms the baseline in the majority of corruption types, yielding an average mloU gain of 2.5%. Meanwhile, the single-scale IB also demonstrates moderate improvements in robustness, with particularly strong performance observed on digital corruptions.
In the second experiment, we evaluate cross-dataset generalization by directly transferring Cityscapes- pretrained models to Vid and BDDI00K [36], fine-tuning only the segmentation head while keeping the backbone frozen. As shown in Table 4, both single-scale and multi-scale IB strategies improve generalization over the bascline. The single-scale IB achieves the highest mloU on Cam Vid (70.2%), slightly outperforming the multi-scale version (69.7%). This variant computes the IB based solely on the final feature before the segmentation head, whereas the multi-scale IB aggregates IB scores from multiple feature resolutions (e.g., 1/8, 1/16 of input size), aiming capture a broader information flow. However, the modest performance drop with multi-scale IB on Cam Vid suggests that not all feature levels contribute equally generalization. Since CamVid contains low-resolution images with simpler scenes, additional features may introduce redundancy rather than useful signal. These results indicate that future work could explore adaptive weighting of multi-scale features, emphasizing high-level ones near the output while maintaining lower-level regularization, to better balance informativeness and redundancy.
4.3 Comparison to state of the art methods.
In this experiment, we compare our method with existing ofate-of-the-art NAS approaches on the Cityscapes[34) datasets. As shown in Table 6, DPS achieves superior performance. Specifically, it obtains the highest mloU on the Cityscapes validation and test sets, reaching 74.5% and 73.5%, respectively.
Conclusion
propose DPS, dynamic path selection strategy for one-shot NAS in semantic segmentation. By integrating stage-wise evaluation based convergence, expressiveness, and generalization, DPS

---

adaptively focuses supernet training on high-quality paths, enabling targeted optimization. To align with these three stages, we introduce three complementary proxies that evaluate path quality from corresponding perspectives and guide selection through explicit feedback. Extensive experiments validate its effectiveness in identifying efficient models with strong generalization and performance.
Acknowledgments and Disclosure of Funding
This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFB330380 under Grant 2024JJ3013, in part by the National Natural Science Foundation of China under Grant 62425305, in part by the Science and Technology Innovation Program of Hunan Province under Grant 2023RC1048.
in part by the Natural Science Foundation of Hunan Province
References
[1] Liang-Chied Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proc. Eur. Conf. Comput. Vis. (ECCV), pages 801- September 2018.
[2] Enze Xie, Wenhai Wang. Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. In Proc. Adv. Neural Inf. Process. Syst., volume 34, pages 12077-12090, 2021.
[3] Jiacong Xu, Zixiang Xiong, and Shankar P. Bhattacharyya. Pidnet: A real-time semantic segmentation network inspired by pid controllers. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 19529-19539 June 2023.
[4] Hanxiao Liu, Karen Simonyan, and Yiming Yang. Darts: Differentiable architecture search. In Proc. Int. Conf. Learn. Represent., 2019.
[5] Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer. Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search. Proc. IEEE Conf. Comput. Vis. Puttern Recognit. (CVPR), pages 10734- 10742, June 2019.
[6] Zichao Guo, Xiangyu Zhang, Haoyuan Mu, Wen Heng, Zechun Liu, Yichen Wei, and Jian Sun. Single path one-shot neural architecture search with uniform sampling. In Proc. Eur. Conf. Comput. Vis. (ECCV), pages 544-560, 2020.
[7] Chenxi Liu, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Wei Hua, Alan L. Yuille, and Li Fei-Fei. Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 82 92, June 2019.
[8] Wuyang Chen, Xinyu Gong, Xianming Liu, Qian Zhang, Yuan Li, and Zhangyang Wang. Fasterseg: Searching for faster real-time semantic segmentation. In Proc. Int. Conf. Learn Reprexent., 2020.
[9] Xiong Zhang, Hongmin Xu, Hong Mo, Jianchao Tan, Cheng Yang, Lei Wang, and Wenqi Ren. Denas: Densely connected neural architecture search for semantic image segmentation. In Proc. IEEE Conf. Comput. Vis, Pattern Recognit. (CVPR), pages 13956-13367, June 2021.
[10] Haibin Wang, Ce Ge, Hesen Chen, and Xiuyu Sun. Prenas: Preferred one-shot learning towards efficient neural architecture search. In Proc. Int. Conf. Mach. Learn., pages 35642-35654, 2023.
[11] Chengyue Gong and Dilin Wang. Nasvit: Neural architecture search for efficient vision transformers with gradient conflict-aware supernet training. In Proc. Int. Conf. Mach. Learn., 2022.
[12] Tao Huang, Shan You, Fei Wang, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu. Gree- dynasv2: Greedier search with greedy path filter. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 11902-1 11911, June 2022.
[13] Shan You, Tao Huang. Mingmin Yang, Fei Wang. Chen Qian, and Changshui Zhang. Greedynas: Towards fast she- nas with greedy supernet. In Proc. IEEE Conf. Comput, Vis. Pattern Recognit. (CVPR), pages 1999-2008. June 2020.
10

---

[14] Dilin Wang, Meng Li. Chengyue Gong, and Vikas Chandra, Attentivenas: Improving neural architecture search via attentive sampling. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 6418- 6427, June 2021.
[15] Guihong Li, Due Hoang, Kartikeya Bhardwaj. Ming Lin, Zhangyang Wang, and Radu Marculescu. Zero- shot neural architecture search: Challenges, solutions, and opportunities. IEFE Trans. Pattern Anal. Mach. Intell., 46(12)77618876665 2024.
[16] Yao Shu, Wei Wang, and Shaofeng Cai. Understanding architectures learnt by cell-based neural architecture search. In Proc. Int. Conf. Learn. Represent., 2020.
[17] Yiming Hu, Yuding Liang. Zichao Guo, Ruosi Wan, Xiangyu Zhang, Yichen Wei, Qingyi Gu, and Jian Sun. Angle-hased search space shrinking for neural architecture search. In Proc. Fur. Conf. Comput. Vis. (ECCV), pages 119-134, 2020.
[18] Xuanyang Zhang, Pengfei Hou, Xiangyu Zhang, and Jian Sun. Neural architecture search with random labels. Proc. IEEE Conf. Comput. Vis. Pattern Recognit, (CVPR), pages 10907-10916, June 2021.
[19] Tishby, Naftali and Zaslavsky, Noga. Deep learning and the information bottleneck principle. Proc. IEEE Inf. Theory Workshop, pages 1-5, 2015.
[20] Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. Neural architecture search: survey. Mach. Learn. Res., 20(55):1-21, 2019.
[21] Albert Shaw, Daniel Hunter, Forrest landola, and Sammy Sidhu. SqueezeNAS: Fast neural architecture search for faster semantic segmentation. In Proc. IEEE Int. Conf. Comput. Vis. Neural Architects Workshop. 2019.
[22] Yiheng Zhang, Zhaofan Qiu, Jingen Liu, Ting Yao, Dong Liu, and Tao Mei. Customizable architecture search for semantic segmentation. In Proc. IEFE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 11641-11650, June 2019.
[23] Yanyu Li, Changdi Yang, Pu Zhao, Geng Yuan, Wei Niu, Jiexiong Guan, Hao Tang. Minghai Qin. Qing Jin, Bin Rea, Xue Lin, and Yanzhi Wang. Towards real-time segmentation on the edge. In Proc. AAAI Conf. Artif. Intell., volume 37, pages 1468-14777 1476, Jun. 2023.
[24] Han Cai, Ligeng Zhu, and Song Han. ProxylessNASS: Direct neural architecture search on target task and hardware. In Proc. Int. Conf. Learn. Represent., 2019.
[25] Minghao Chen, Houwen Peng, Jianlong Fu, and Haibin Ling. Autoformer: Searching transformers for visual recognition. In Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pages 12270-12280. October 2021.
[26] Xiangxiang Chu, Bo Zhang, and Ruijun Xu. Faimas: Rethinking evaluation fairness of weight sharing neural architccture search. In Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pages 12239-12245 October 2021.
[27] Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas Huang, Xiaodan Song, Ruoming Pang, and Quoc Le. Bignas: Scaling up neural architecture search with big single-stage models. In Proc. Eur. Conf. Comput. Vis. (ECCV), pages 702-717, 2020.
[28] Leo Breiman. Random forests. Machine learning, 45:5-32, 2001.
[29] Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm. Mutual information neural estimation. In Proc. Int Conf. Mach. Learn., pages 531-540, 2018
[30] Pengyu Cheng, Weituo Hao, Shuyang Dai. Jiachang Liu, Zhe Gan, and Lawrence Carin. Club: A contrastive log-ratio upper bound of mutual information. Proc. Ins. Conf. Mach. Learn., pages 1779-1788, 2020.
[31] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 2881-2890, July 2017.
[32] Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and Jian Sun. Unified perceptual parsing for scene understanding. In Proc. Eur. Conf. Comput. Vis. (ECCV), pages 418-434, September 2018.
[33] Meng- Meng-Hao Guo, Cheng Ze Lu, Qibin Hou, Zhengning Liu, Ming-Minn Cheng, and Shi-min Hu. Segnext: Rethinking convolutional attention design for semantic segmentation. In Proc. Adv. Newral Inf. Process. Syst. volume 35, pages 1140 1156, 2022.
11

---

[34] Marius Cordts, Mohamed Omran. Sebastian Ramos, Timo Rehfeld. Markus Enzweiler. Rodrigo Benenson. Uwe Franke, Stefan Roth, and Bemt Schiele. The cityscupes dataset for semantic urban understanding. In Proc. IEEE Conf. Comput. Vis. Puttern Recognit. (CVPR), pages 3213-3223, June 2016.
[35] Gabriel J. Brostow. Jamie Shotton, Julien Fauqueur, and Roberto Cipolla. Segmentation and recognition using structure from motion point clouds. David Forsyth, Philip Torr, Andrew Zisserman, editors, Proc. Eur. Conf. Comput. Vis. (ECCV), pages 44-57, 2008
[36] Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangehen Liu, Vashisht Madhavan, and Trevor Darrell. Bdd100k: A diverse driving dataset for heterogeneous multitask learning. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 2636 2645, June 2020.
[37] Jia Deng. Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image databasc. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 248-255, 2009.
[38] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang Inverted residuals and linear bottlenecks. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 45 4510-4520, June 2018.
Liang-Chieh Chich Chen. Mobilenetv2:
[39] Chendi Zhu, Lujun Li, Yuli Wu, and Zhengxing Sun. Saswot: Real-time semantic segmentation architecture search without training. In Proc. AAAI Conf. Artif. Intell., volume 38, pages
Mar. 2024.
[40] Christoph Kamann and Carsten Rother. Benchmarking the robustness of semantic segmentation models. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 02-483, June 2020.
[41] Mingyu Ding, Xiaochen Lian, Linjie Yang. Peng Wang. Xiaojie Jin. Zhiwu Lu, and Ping Luo. Hr-nas: Searching efficient ressutesotution neural architectures with lightweight transformers. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages
June 2021.
[42] Peiwen Lin, Peng Sun, Guangliang Cheng, Sirui Xie, Xi Li, and Jianping Shi. Graph-guided architecture search for real-time semantic segmentation. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pages 4203 4212, 2020.
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A Training Details of Mutual Information Estimator
Before entering the final stage of supernet training, we optimize two mutual information estimators to separately compute the two terms in MS-IB, which are used for evaluating path generalization. Once training reaches the final stage, these estimators are fixed and switched to evaluation mode. The detailed training procedures are shown in Algorithm and Algorithm 2.
Algorithm The optimization algorithm Tg.
Input: Neural network Te, training set Dtrain {a11, v1), (#2, U2). (I br)) number of iterations T' required enter Stage III
Output: Optimized neural network To for 1 to T do
2: Sample mini-batee batch B from Dtrain:
3: B {(111 3/), (az. y2)..... (2b. 36)]
4: Compute Z by the sampled path at
5:
21, #1,.1 26 = a(x1 T2, 22,,...,,,,
6:
Sample Z from marginal distribution of Z:
7:
21. 2.... 26 ~ Pz
8:
Compute the value YZ by Eq.7:
9:
V = 1/2 I [T 34) - log( { Σ i= (F..)))))
Update the parameters 0 using gradient ascent:
8 x A + ay
11:
12: end for
Algorithm 2 The optimization algorithm of 1s.
Input: Variational distribution qa (2(2),, training set Dirain { 81); (x2, v2), (.))...... (I (Fn.yn)} yn) number of iterations 7 required to enter Stage III
Output: Optimized neural network go
1: for t = 1 to T do
2: Sample a mini-batch B from Dirain:
3: B = {(x1,, y1) (I2, 12),..., (Ib, {b)}
4: Compute Z by the sampled path a:
5: ø1, .2.... ................................ 2b
a(21, 22, ....,))))
6:
Sample Z from marginal distribution of Z:
7:
21, 22, ; 26 pz
8:
Compute the positive term:
9:
Topos= = 1/2 Σh log (0,(21))))))
10:
Compute the negative term:
11:
Lneg 12 Σb
Σ マ== log 4 (3)(=4)
12:
Compute the vCLUB estimate:
I.CLBB Ippus Tiney
14:
Update the parameters & using gradient ascent:
al,cua ① + &
15:
end for 06
16:
13

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B Dynamic Path Selection
Details of dynamic path selection are presented below. We summarize the key settings as follows: twin 20, m = 10, k = 5, 2e 5, C 4c - 5.
Algorithm 3 Dynamic Path Selection (DPS)
Input:
Supernet V with weights W, training set brain performance predictor fpred: max iteration
T. window size twin. diversity threshold 7), sensitivity coefficients 62
Initialize stage counters: =
2: Track historical performance: history <-
3: for 1 to T do
4: i mod 5 == 1 then
5: Initialize score list: Scores + [
6: Sample paths from search space: P = {ai,,.., ... a10}
7:
for each path a, E P do
8:
Compute FLOPs score: Scorey(a:) -FLOPs(a,)
9:
ifs = then
10:
Compute convergence score: Score.(a:) AngleScorc(a.)
11:
else s = 2 then
12:
Compute expressiveness score: Score, spred(a:)
13:
else
14:
Compute expressiveness score: Score, fared(a;)
15:
Compute generalization score: Score, = MS-IB(a:) end if
17:
Scores +- Scores U {(a,, Scorey, Scorec/e/g)
end for
19:
Props + NonDoominttedSor(SSorres. k)
if |Poopl then
21:
Supplement Peop, with paths having lowest aggregated rank across all metrics end if
23:
Apply diversity-driven exploration:
for each a; EP Props do if diversity > 6 then
25:
26:
Identify aworst = arg max.ee RankSum(a)
Replace awarst in Prope with a; break end if
28:
30:
end for
31:
Train selected paths Props
32:
Update history:
33:
if then
34:
history + history U
P Eajer (a)))))
35:
else if = 2 then
history <- history
Propyl En, EPoov
TrainLoss(a))
37:
end if
38:
Update stage based on learning dynamics:
39:
Extract recent window: W t- history[ twri
40:
Compute average improvement: A Twsn
win -1 (Wii Wi 1))
41:
A < then
42:
8 8
43:
end if
44:
end if
45:
end for
14

---

C
Evolution Search with Elite Population
Details of the evolutionary search presented below. We summarize the key settings as follows:
M 1, 500, G = 20, N = 50, Pm = 0.2, ps = 0.1, = 10.
Algorithm Evolution Search
Input:
Trained superet search space A, clite population M number of generations G. popula-
tion size N. mutation probability Pm. crossover probability Pe, validation set Dval Output: Optimal Architecture a* Initialize population: P t- RandomSample(
for 1 to G do
P Piarent &- Select top l subnets from P by mloU
P child 50
if Randomo) < Pe. then
Pohild < Perosal Crossover(P parent)
end if
if Randomm) < Pm then
Pohild < Pehitau Mutation(P (P) Pprrent)
end if
if |Petttal < N then Sample -Peaaal subnets from M and add to Pebild
end if
P t Pohild
end for
a" <- arg max.ep (l) Dval)
return a'
D Implementation Details
Supernet Training. In this stage, we train the supernet on Cityscapes[34] dataset. We employ the stochastic gradient descent (SGD) optimizer with initial learning rate of 0.01, momentum of 0.9, and weight decay of 0.0005. The learning rate is adjusted using polynomial decay policy with power of 0.9. During training, we apply standard data augmentation techniques including random cropping, random scaling, and horizontal flipping. All models are trained for 850 epochs with total batch size of 12 across two RTX 3090 GPUs. addition, the OHEM loss is adopted enhance model performance.
Network Retraining, For network retraining, adopt the same training strategy used for the supernet. In the case of transfer learning, the learning rate is set to 0.007, and the models are trained for 500 epochs on Cam Vid [35] and 200 epochs BDD100K [36], with all other settings remaining unchanged.
E Experiments on Proxy Analysis
To examine the behavior the convergence proxy, we tracked the angle score averaged over 10 randomly sampled paths at different training epochs. As reported in Table 7, the score increases rapidly the early stage (0.65 at epoch 100) and gradually saturates after epoch 400 (around 1.23 at epoch 800). This confirms that the angle score becomes less discriminative in the mid-to-late stages.
To assess the reliability of the performance predictor across training. we measured its correlation with the ground-truth accuracy at different epochs. Table 8 reports Kendall's 7 and Spearman's correlations. The results indicate that the predictor is relatively noisy the early stage (e.g., 7=0.53 at epoch 50), but its accuracy improves steadily and stabilizes after around 200 epochs (r=0.76, p=0.92). This trend supports our design choice of activating the predictor only from Stage onward, when the supernet has reached more stable phase.
15

---

Table 7: Angle score across epochs.
Table 8: Rank correlations between predicted and true accuracies across epochs.
Epoch
Angle Score (avg. 10 paths)
Epoch
(Kendall)
(Spearman)
100
0.65
200
0,99
50
0.53
0.72
400
1.20
100
0,63
0.82
600
1.22
200
0.76
0,92
800
1.23
400
0.75
0.92
F
Searched Architectures
Table 9: Searched architecture using DPS on the SqueezeNAS search space. k, denote kernel size, groups, expansion ratio, and dilation ratio, respectively.
Block
Operator
Cout
Down
k3 g2 el dl
16
2
2
k5_gl_ _3
24
4
3
k5_g2_el_di
4
k5_gl_c3_dl
4
5
k3_gl_el_dl
24
4
6
k5_gl_e3_dl
32
8
k3_gl_e3_d2
8
8
k3_gl_el_dl
32
8
9
k5_gl_c3_dl
32
8
10
k5_gl_o6_dl
16
11
k5_g1_e6_dl
64
16
12
g5_g__e3_dl
64
16
13
Identity
16
14
k3_gl_c6_dl
96
16
15
k3_g2_el_dl
96
16
16
k3_g1_e3_d1
96
16
17
k3_gl_e3_d2
16
18
k3_gl_el_dl
160
16
19
k3_gl_el_d2
160
16
20
k5_gl_e3_d1
16
21
g2
160
16
22
k3 g3_____
160
16
Table 10: Searched architecture using DPS on the FasterSeg search space. Left: cells for branch with final downsample rate of 16. Right: cells for branch with final downsample rate of 32.
Operator
Exp.
Cou
Down
Cell
Operator
Exp.
C
Down
zoomed conv.
96
zoomed conv.
96
zoomed conv.
48
6
96
16
zoomed conv.
10
80
8
3
zoomed x2
4
128
32
conv.
4
32
8
4
zoomed conv. x2
4
128
32
zoomed conv.
4
64
16
5
zoomed conv.
12
384
32
zoomed conv.
4
64
16
zoomed conv.
4
128
32
zoomed cony.
8
128
16
16

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Reviews:
[{"review_id": "TfvDbCFIIF", "reviewer": "Reviewer_oCPq", "summary": "The paper proposes a Dynamic Path Selection (DPS) strategy for semantic segmentation tasks, aiming to improve the efficiency and generalization ability of one-shot NAS methods under large-scale, multi-branch architectures. The approach systematically integrates multiple performance proxies-convergence, expressiveness, and generalization-along with a multi-stage dynamic scheduling mechanism to address issues such as gradient conflicts and interference among subnetworks during supernet training.", "questions": "Q1:The proposed DSS strategy relies on hyperparameters such as window size (t_win) and sensitivity coefficients (ε), which appear to be set empirically. The robustness of the method to these parameters is unclear, raising concerns about generalizability.\n\nQ2:The performance predictor is trained on only 1,000 sampled paths, while the overall search space is extremely large. It remains unclear whether such a sample size is sufficiently representative. Please provide additional experiments showing how the predictor's performance varies under different sampling scales.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1.The method combines multiple performance proxies, including Angle Score, a performance predictor, and Multi-Scale Information Bottleneck (MS-IB), enabling a systematic, multi-perspective evaluation of path quality.\n\n2.A dynamic stage scheduling mechanism is introduced, which adaptively adjusts the training stages based on metric trends, effectively avoiding manual hyperparameter tuning.\n\n3.A diversity-driven exploration strategy is designed to mitigate search space collapse and promote structural diversity during the search process.\n\nWeaknesses:\n\n1.Although MS-IB effectively improves generalization, its computational complexity is relatively high, and the additional cost for practical deployment has not been thoroughly discussed.\n\n2.The performance of the dynamic stage scheduling mechanism is sensitive to hyperparameters such as ε and window size, yet systematic sensitivity analyses are lacking.\n\n3.The core innovation primarily integrates and optimizes existing NAS and Information Bottleneck (IB) theories, with limited theoretical novelty and a lack of formal mathematical reasoning or convergence guarantees.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for your insightful comments, please check our answers below.\n\n---\n***Answers to Weakness 1:***\n\nAlthough MS-IB introduces additional computation, this overhead is practically acceptable given the overall efficiency and the excellent performance of our framework. We elaborate on these two aspects below:\n\n(1) **Efficiency: The computational overhead of MS-IB is limited and practical.**\n\nThe additional computation introduced by MS-IB occurs only during the **supernet training stage** and does not affect the subsequent architecture search or retraining phases. On **2 × RTX 3090 GPUs**, supernet training takes ~9 hours without MS-IB and ~14 hours with it. Despite this increase, the total computational cost of our method remains significantly lower than that of existing NAS approaches for semantic segmentation (see table below). \n\n| Method           | GPU Hours ( Total Training Time × Number of GPUs) |\n| ---------------- | ------------------------------------------------- |\n| Auto-DeepLab [1] | 72                                                |\n| GAS [2]          | 160                                               |\n| CAS [3]          | 200                                               |\n| Ours             | **28 (14 x 2)**                                   |\n\nThis is because, unlike most NAS approaches for semantic segmentation rely on gradient-based optimization, we adopt a sampled-based strategy, which avoids expensive end-to-end optimization and enables more efficient supernet training.\n\n(2) **Performance: Consistent improvements in generalization and robustness.**\n\nMore importantly, MS-IB leads to significant performance gains. As shown in Tables 4 and 5 (main paper), MS-IB delivers consistent improvements in generalization and robustness across multiple benchmarks. Thus, we consider the additional training time a reasonable trade-off for practical NAS deployment.\n\n---\n***Answers to Weakness 2:***\n\nWe thank the reviewer for highlighting the importance of sensitivity analysis in the dynamic stage scheduling (DSS) mechanism. Our overall design is robust in practice, and we provide supporting analysis from two perspectives:\n\n(1) **Empirical Robustness Across Thresholds**\n\nTo address your concern, we first independently varied each threshold while fixing the other, and observed only minor fluctuations in final mIoU performance:\n\n(a) Varying $\\varepsilon_1$ with $\\varepsilon_2 = 4e\\text{-}5$:\n\n| $\\varepsilon_1$ | mIoU (%) |\n| --------------- | -------- |\n| 1e-5            | 74.2     |\n| 2e-5            | 74.5     |\n| 4e-5            | 74.2     |\n\n(b) Varying $\\varepsilon_2$ with $\\varepsilon_1 = 2e\\text{-}5$:\n\n| $\\varepsilon_2$ | mIoU (%) |\n| --------------- | -------- |\n| 2e-5            | 74.4     |\n| 4e-5            | 74.5     |\n| 6e-5            | 74.1     |\n\nWe also examined the effect of the smoothing window size $t_{win}$ and found that, while it may affect the exact transition points, the final performance remains stable as long as $t_{win}$ is chosen within a practical range (e.g., 10–30).\n\n(2) **Design Rationale for Stability**\n\nOur overall design is inherently robust. This robustness comes from our scale-invariant thresholding strategy: instead of relying on fixed absolute values, each $\\varepsilon$ is defined as a small proportion (e.g., 3–5%) of the early-phase slope statistics of its corresponding proxy metric (e.g., angle score or training loss). This normalizes across value scales and ensures transitions are triggered by meaningful trend shifts rather than noise.\n\nImportantly, as shown in Table 3 of the main paper, replacing DSS with hard-coded epoch transitions leads to a **significant performance drop of 1.1% mIoU**, underscoring the necessity of adaptive scheduling.\n\nIn summary, DSS achieves strong performance without requiring precise hyperparameter tuning, and we will include the above analysis in the future version.\n\n---\n***Answers to Weakness 3:***\n\nWe thank the reviewer for the insightful comments. We address the concerns regarding theoretical novelty and convergence guarantees in two parts:\n\n(1) **On Theoretical Innovation**\n\nOur work makes multiple principled and technically non-trivial contributions to the NAS literature:\n1. **Theory-driven dynamic proxy scheduling framework**: we propose the first NAS strategy that dynamically aligns path selection with theoretical objectives specific to each training phase: convergence, expressiveness, and generalization. Each objective is modeled by a dedicated proxy metric derived from optimization theory and information theory. Unlike prior works that treat all metrics uniformly or statically, our framework introduces a stage-aware scheduling mechanism that adapts selection criteria based on observed learning dynamics.\n\n2. **Complementary performance proxies**:\n\t- **Convergence**: We introduce a **sampling-aware variant** of angle score (Eq. 3), which penalizes paths frequently selected in early training. Unlike prior angle-based metrics [4, 5], this avoids bias toward high-sampling paths and enables more reliable convergence estimation in large search spaces.\n\t- **Generalization**: We **extend and modify the IB principle [6] to segmentation tasks** via Multi-Scale IB (MS-IB). MS-IB captures information retention across multiple feature levels (e.g., 1/8, 1/16, 1/32), aligning with multi-resolution backbones and improving cross-domain robustness (Tables 4–5).\n\t- **Expressiveness**: We model architectural expressiveness using a performance predictor trained on uniformly sampled architectures, where each path is encoded as a structural vector capturing its layer-wise configuration. The predictor achieves high rank correlation (Kendall’s τ and Spearman’s ρ) and enables reliable ranking of candidate paths.\n\n3. **Pareto-based multi-objective optimization with structural diversity:** Our method formulates NAS as a multi-objective selection problem, balancing convergence, expressiveness, and generalization via Pareto optimality (Eq. 10), further enhanced by a diversity-driven exploration module (Eq. 12) that ensures structural heterogeneity and avoids premature convergence.\n\n\n(2) **On Convergence Guarantees**\n\nOur pipeline builds on theoretically grounded components, with careful adaptation for effective integration:\n- For the **IB-guided proxy metrics**, we adopt established MI estimators [7, 8] with known convergence properties. Crucially, their application to architecture evaluation in NAS is novel, and their use in stage-specific objectives is guided by information-theoretic principles without altering training dynamics.\n- For the **supernet training process**, we follow standard sampling-based frameworks [9–12], but enhance them with our stage-aware scheduling (DSS) and dynamic path selection. These design choices help decouple learning phases and maintain stable optimization.\n\nImportantly, the two components do not interfere with each other. As a result, the integration does not introduce instability or optimization conflict. Taken together, these observations imply that our training process remains stable and convergent throughout.\n\n---\n***Answers to Question 1:***\n\nPlease refer to `Answers to Weakness 2`\n\n---\n***Answers to Question 2:***\n\nWe thank the reviewer for raising this important question. To evaluate whether 1,000 sampled paths are sufficiently representative for training the performance predictor, we conducted additional experiments across different sampling sizes.\n\nWe trained the accuracy predictor using 500, 1,000, and 2,000 uniformly sampled paths from the same supernet state, with a fixed 8:2 train-validation split. We report Kendall’s τ and Spearman’s ρ to measure ranking consistency.\n\n| **# Sampled Paths** | **Kendall’s τ** | **Spearman’s ρ** |\n| ------------------- | --------------- | ---------------- |\n| 500                 | 0.73            | 0.90             |\n| 1,000               | 0.76            | 0.92             |\n| 2,000               | 0.77            | 0.92             |\n\nThe results show that the predictor achieves strong ranking correlation even with 500 samples. Increasing to 1,000 yields a noticeable improvement, while further increasing to 2,000 brings only marginal gains (τ increases by 0.01, ρ remains unchanged), indicating diminishing returns beyond 1,000 samples. Thus, 1,000 sampled paths strike an effective balance between computational efficiency and statistical representativeness, providing sufficient signal for reliable performance prediction in large-scale NAS.\n\n---\n\nWe hope the above answers have addressed your concerns and we are happy to provide additional details if needed.\n\nReference:\n\n[1] Chenxi Liu, et al. Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation. In CVPR, 2019.\n\n[2] Peiwen Lin, et al. Graph-guided architecture search for real-time semantic segmentation. In CVPR, 2020.\n\n[3] Yiheng Zhang, et al. Customizable architecture search for semantic segmentation. In CVPR, 2019.\n\n[4] Yiming Hu, et al. Angle-based search space shrinking for neural architecture search. In ECCV, 2020.\n\n[5] Xuanyang Zhang, et al. Neural architecture search with random labels. In CVPR, 2021.\n\n[6] Tishby, Naftali, et al. Deep learning and the information bottleneck principle. In IEEE Inf. Theory Workshop, 2015.\n\n[7] Mohamed Ishmael Belghazi, et al. Mutual information neural estimation. In ICML, 2018.\n\n[8] Pengyu Cheng, et al. Club: A contrastive log-ratio upper bound of mutual information. In ICML, 2020.\n\n[9] Zichao Guo, et al. Single path one-shot neural architecture search with uniform sampling. In ECCV, 2020.\n\n[10] Minghao Chen, et al. AutoFormer: Searching Transformers for Visual Recognition. In ICCV, 2021.\n\n[11] Tao Huang, et al. Greedynasv2: Greedier search with a greedy path filter. In CVPR, 2022.\n\n[12] Yu J, et al. Bignas: Scaling up neural architecture search with big single-stage models. In ECCV, 2020."}, {"review_id": "dNEbj5EVBz", "reviewer": "Reviewer_sMdg", "summary": "This submission introduces ​Dynamic Path Selection (DPS)​, an improved one-shot neural architecture search (NAS) method for semantic segmentation. DPS employs a ​stage-wise strategy​ to dynamically prioritize high-potential network paths: early stages optimize for convergence using an ​angle score​ metric, middle stages evaluate expressiveness via a ​random forest predictor, and final stages balance expressiveness and generalization using a novel ​Multi-Scale Information Bottleneck (MS-IB)​. By selectively training paths based on these proxies and incorporating ​diversity-driven exploration​ and ​adaptive stage scheduling, DPS reduces gradient conflicts and improves supernet reliability. This method improves over SOTAs with a not small margin.", "questions": "I only have one question: in table 4, the performance of this method on CamVid drops when using multi-scale IB, comparing to only using single-scale IB. The authors claimed that it's because CamVid's resolution is low so that the additional features may only introduce redundancies. I doubt about this hypothesis here. Do you have any evidence to support this claim? For example, visualize such redundancies? In addition, why this learning will result in some kinds of redundancy?", "limitations": "yes.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The innovations include\nDynamic Stage Scheduler: Auto-transitions stages based on improvement trends (e.g., switches when metric plateaus).\n​Diversity-Driven Exploration: Maintains structural diversity using weighted Hamming distance to avoid search collapse.\n​Pareto-Optimal Path Selection: Selects paths balancing FLOPs, convergence, expressiveness, and generalization.\n​Elite Population in Evolution Search: Preserves top paths to accelerate optimal architecture discovery.\n\nWeakness:\nThe three-stage proxy system (angle score, predictor, MS-IB) may introduce significant computational overhead. Training and maintaining these proxies—especially the mutual information estimators for MS-IB—demands extra resources beyond standard supernet training.", "was_revised": true, "final_justification": "I had read the rebuttals and further discussions; I will not change my rating.", "rebuttal": "We greatly appreciate the positive comments and insightful suggestions from the reviewer, please check our answers below.\n\n---\n***Answers to Weakness 1:***\n\nWe appreciate the reviewer’s concern regarding the potential computational overhead introduced by our three-stage proxy system. We clarify that:\n- The **angle score** is computed from weight updates that are already available during standard supernet training, requiring only lightweight vector operations with negligible computational overhead.\n- The **random forest predictor** is trained once at stage II using only ~1000 sampled paths, and subsequently queried via a single forward pass without retraining. \n- Only **MS-IB** introduces notable computational overhead. We profiled the training process on **2×RTX 3090 GPUs**: supernet training without MS-IB takes approximately **9 hours**, while enabling MS-IB increases this to around **14 hours**. This overhead is confined to the supernet training phase only, with the search and retraining stages remaining unaffected. Given the consistent generalization improvements shown in Tables 4 and 5, we consider this trade-off both reasonable and worthwhile for a NAS pipeline.\n\nIn addition, as shown in the table below, our total computational cost is much lower than that of other NAS methods.\n\n| Method           | GPU Hours ( Total Training Time × Number of GPUs) |\n| ---------------- | ------------------------------------------------- |\n| Auto-DeepLab [1] | 72                                                |\n| GAS [2]          | 160                                               |\n| CAS [3]          | 200                                               |\n| Ours             | **28 (14 x 2)**                                   |\n\nThis is because, unlike most NAS approaches for semantic segmentation rely on gradient-based optimization, we adopt a sampled-based strategy, which avoids expensive end-to-end optimization and enables more efficient supernet training.\n\nTo sum up, the additional cost is modest and justified by clear performance benefits, ensuring that DPS remains an efficient and practical NAS solution.\n\n---\n***Answers to Question 1:***\n\nWe thank the reviewer for raising this important question. We provide the following clarifications and new supporting evidence regarding the drop in performance observed when using the multi-scale IB (MS-IB) proxy on the CamVid dataset.\n\n**1. Why MS-IB May Be Less Effective on CamVid**\n\nThe performance gap between single-scale and multi-scale IB on CamVid is attributed to high similarity across feature scales in low-resolution and structurally simpler datasets. The MS-IB proxy computes a uniform aggregation of information bottleneck estimates from multiple feature stages. However, when the intermediate features capture largely overlapping or redundant information, equal aggregation can reduce the distinctiveness of the proxy signal.\n\nBecause MS-IB treats all feature levels with equal importance, the presence of uninformative or repetitive layers can dilute the contribution of more discriminative ones. As a result, its ability to accurately rank candidate paths is weakened. In contrast, single-scale IB, which focuses solely on deeper, semantically rich features, can be more robust and effective in such scenarios.\n\n**2. Centered Kernel Alignment (CKA) analysis**\n\nTo further support our explanation for the reduced performance of MS-IB on CamVid, we conducted a Centered Kernel Alignment (CKA) analysis [4] to measure the similarity between deep and shallow multi-scale features. CKA is a robust and widely used measure of representation similarity in deep networks, capturing structural alignment between entire feature spaces.\n\nIn our analysis, we computed both **linear CKA** and **RBF CKA** between the deepest feature $z_3$​ (1/32 scale, used in single-scale IB) and shallower features $z_1$ (1/8) and $z_2$​ (1/16). \n\n|Dataset|CKA Type|z₁ (1/8) – z₃ (1/32)|z₂ (1/16) – z₃ (1/32)|\n|---|---|---|---|\n|**CamVid**|Linear|**0.6214**|**0.7222**|\n||RBF|**0.6245**|**0.7255**|\n|**Cityscapes**|Linear|0.5324|0.4929|\n||RBF|0.5441|0.5180|\n\nThe results in Table X reveal a clear contrast between datasets:\n- On **CamVid**, all CKA scores are **consistently high (up to 0.72)**, indicating that the added shallow features are **highly similar** to the deep one and contribute limited new information.\n- On **Cityscapes**, CKA scores are **noticeably lower (∼0.49–0.53)**, suggesting **greater diversity and complementarity** across feature scales.\n\nThese findings quantitatively support our hypothesis: **in CamVid, multi-scale inputs offer minimal additional variation**, limiting the utility of MS-IB. In contrast, **Cityscapes benefits more from multi-scale modeling**, due to greater intra-network feature diversity.\n\nThus, the performance gap is not due to a training failure of MS-IB, but reflects the dataset-dependent effectiveness of multi-scale supervision. In low-resolution datasets with more redundant internal features like CamVid, **simpler single-scale IB proves more effective**.\n\nWe hope the above analysis and experiment have addressed your concerns, and we are happy to provide additional details if needed.\n\n---\nReference:\n\n[1] Chenxi Liu, et al. Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation. In CVPR, 2019.\n\n[2] Peiwen Lin, et al. Graph-guided architecture search for real-time semantic segmentation. In CVPR, 2020.\n\n[3] Yiheng Zhang, et al. Customizable architecture search for semantic segmentation. In CVPR, 2019.\n\n[4] Kornblith S, et al. Similarity of neural network representations revisited. In ICML, 2019"}, {"review_id": "kYrLMJmBes", "reviewer": "Reviewer_Vnny", "summary": "This paper presents a dynamic path selection method for architecture search of semantic segmentation. In particular, the proposed DPS leverages multiple performance proxies to guide path optimization. In addition, a dynamic stage scheduler and a diversity-driven exploration strategy are used to enable adaptive stage transition and maintain path diversity. Experimental results proved the effectiveness of the proposed method.", "questions": "No.", "limitations": "No.", "rating": 2, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 1, "originality": 1, "strengths_and_weaknesses": "Strengths:\n(1) The motivation is well presented of considering dynamic path selection for architecture search.\n\n(2) The explanations and illustrations are mostly clear and intuitive of the multiple performance proxies and the dynamic stage scheduler.\n\nWeaknesses:\n(1) As a sampling-based NAS method, this paper provides no novel ideas to the field but a simple combination of typical optimization techniques such as angle score, accuracy predictor and information bottleneck.\n\n(2) The motivation of dynamically choosing loss terms for different training stages is not theoretically proved or investigated. The paper only presents empirical results that are also problematic.\n\n(3) The overall design of the dynamic path selection seems to have nothing specific to the semantic segmentation.\n\n(4) On Page 2, Line 42-43, the authors use a specific path during supernet training to get the results in Figure 1. There are no details on the experimental setting. In addition, if the observed path is not representative and the experiment is not conducted multiple runs, the results show no statistical signification. Besides, the validation loss could fluctuate due to the randomness of gradient-based optimization during initial stage of training.\n\n(5) On Page 3, Line 74, the authors claim a diversity-driven exploration strategy but present no specific explanation on it.", "was_revised": true, "final_justification": "The concerns on novelty and motivation are not well addressed. The experimental setting is still unclear. Based on the above consideration, I keep the original rating.", "rebuttal": "We sincerely thank the reviewer for your insightful comments, please check our answers below.\n\n---\n***Answers to Weakness 1:***\n\nWe respectfully disagree. Rather than merely combining prior techniques, we present a structured, dynamic framework for **path evaluation and selection** based on multiple complementary perspectives.\n\nOur work explores a largely overlooked aspect of sampling-based NAS—**the temporally adaptive and multi-perspective evaluation of candidate paths during supernet training**. To address this issue, we have made following innovations:\n1. **Theory-driven dynamic proxy scheduling framework**: we propose the first NAS strategy that dynamically aligns path selection with theoretical objectives specific to each training phase: convergence, expressiveness, and generalization. Each objective is modeled by a dedicated proxy metric derived from optimization theory and information theory. Unlike prior works that treat all metrics uniformly or statically, our framework introduces a stage-aware scheduling mechanism that adapts selection criteria based on training dynamics.\n2. **Pareto-based multi-objective optimization with structural diversity:** Our method formulates NAS as a **multi-objective selection problem**, balancing different proxies via Pareto optimality (Eq. 10), further enhanced by a diversity-driven exploration module (Eq. 12) that ensures structural heterogeneity and avoids premature convergence.\n3. **Innovations in Proxy Design:**\n\t- **Sampling-Aware Angle Score**: We introduce a sampling-aware variant of angle score (Eq. 3), which penalizes paths frequently selected in early training. Unlike prior angle-based metrics [1, 2], this avoids bias toward high-sampling paths and enables more reliable convergence estimation in large search spaces.\n\t- **Multi-Scale Information Bottleneck**: We **extend and modify the IB principle to segmentation tasks** via Multi-Scale IB (MS-IB). MS-IB captures information retention across multiple feature levels (e.g., 1/8, 1/16, 1/32), aligning with multi-resolution backbones and improving cross-domain robustness (Tables 4–5).\n\nWe believe our proposed framework offers a principled and practically effective approach to improving path selection in NAS, and we appreciate the reviewer’s feedback in helping us better articulate this contribution.\n\n---\n***Answers to Weakness 2:***\n\nWe respectfully disagree with the concern that our stage-wise proxy scheduling lacks theoretical grounding or investigation. We would to clarify from two aspects. \n\n(1) **Lack theoretical grounding**\n\n1. Our priority-based sampling mechanism is built upon theoretical foundations established in prior work [3, 4], including sampling distribution convergence and reward estimation consistency. We build upon these analyses without repeating their full derivations, allowing us to focus on our contributions.\n2. Our scheduling strategy is supported by a series of quantifiable and reproducible training observations, which are standard analytical tools in NAS literature for understanding supernet behavior [5, 6, 7].\n\nBesides, we would like to clarify a misunderstanding: our method does **not involve** dynamically adjusting **loss terms**. Instead, we dynamically select proxy metrics across different training stages to guide path sampling.\n\n(2) **Lack investigation**\n\nWe address the concern regarding insufficient investigation by highlighting the motivation, design rationale, and supporting evidence for our stage-wise proxy scheduling.\n\nOur method tackles a key limitation in existing NAS approaches—the lack of comprehensive evaluation criteria for candidate paths. To this end, we employ three complementary proxies to assess convergence, expressiveness, and generalization. Importantly, we observe that these signals **exhibit distinct reliability patterns throughout training**, motivating our scheduling design to selectively activate each proxy at its most informative stage.\n\nBelow, we reiterate the key findings already presented in our paper, and supplement them with additional supporting evidence.\n\n1. The angle score tends stabilizes in the mid-to-late stages, as most paths have already converged by then, making angle score less discriminative for evaluating path quality in the later stage. \n\n| Epoch | Angle Score (average over 10 paths) |     |\n| ----- | ----------------------------------- | --- |\n| 100   | 0.65                                |     |\n| 200   | 0.99                                |     |\n| 400   | 1.20                                |     |\n| 600   | 1.22                                |     |\n| 800   | 1.23                                |     |\n\n2. The accuracy predictor trained in the early phase is unreliable and only becomes trustworthy once training enters a relatively stable stage.\n\n| Epoch | τ (Kendall) | ρ (Spearman) |\n| ----- | ----------- | ------------ |\n| 50    | 0.53        | 0.72         |\n| 100   | 0.63        | 0.82         |\n| 200   | 0.76        | 0.92         |\n| 400   | 0.75        | 0.92         |\n\n3. Fig. 2 in our paper shows the mutual information begins to compress later in training, making generalization proxies (MS-IB) more reliable only in the final stage.\n\nThese observations form the empirical and conceptual basis of our stage-wise proxy scheduling strategy. Such design is **systematic and grounded in reproducible training behaviors**. Our **ablation studies in Table 2** further demonstrate that this scheduling yields consistent performance gains.  \n\n---\n***Answers to Weakness 3:***\n\nWe respectfully disagree with the claim that our dynamic path selection (DPS) framework lacks task specificity. In fact, it is particularly well-suited to **semantic segmentation**, for the following reasons:\n\n1. **Search space complexity**:  \n    Our search space covers both the encoder and the **decoder level** , where paths correspond to different multi-scale, multi-branch aggregation strategies. This leads to a **vastly larger and more entangled space** compared to typical classification NAS. This amplifies path interference and proxy instability, making a stable, proxy-guided dynamic selection strategy essential. DPS directly addresses this by adaptively choosing meaningful proxies across training stages and enforcing structural diversity.  \n2. **Segmentation-specific proxy**:  \n    Our **Multi-Scale Information Bottleneck** is specific designed for dense prediction setting. Unlike classification, segmentation requires preserving both low-level spatial detail and high-level contextual information. MS-IB evaluates the information compression **at multiple decoder resolutions** (e.g., 1/8, 1/16, 1/32), aligning well with the demands of pixel-level prediction. \n\nIn sum, DPS is a framework designed to overcome the unique challenges of semantic segmentation search: high-dimensional path space, strong inter-path coupling, and task-specific proxy requirements. \n\nThe consistent performance improvements in Tables 1, 2, 4 and 5 confirm its effectiveness under dense prediction settings.\n\n---\n***Answers to Weakness 4:***\n\nWe thank the reviewer for the comment. Figure 1 is intended as an illustrative instance, which is chosen to help readers understand the typical training dynamics of a sampled path.\n\nTo clarify:\n- The path in Fig. 1 was **not cherry-picked**. It was drawn from a set of randomly sampled, fixed paths that we tracked throughout supernet training.\n- We repeated this observation across **multiple runs**, and consistently found that validation loss fluctuates significantly, the angle score converges earlier and more steadily, making it a more stable indicator for early-stage selection.\n\n**For your second concern**, \"*validation loss could fluctuate due to the randomness of gradient-based optimization during initial stage of training*.\" This observation **precisely reinforces our design motivation**: such fluctuations are especially severe in supernet training due to parameter sharing and stochastic sampling. It is exactly this instability that makes validation loss unreliable as an early-stage proxy.  Therefore, we **intentionally propose** using angle score, which is robust to supernet noise.\n\nTo provide additional insight into proxy behavior, we track 10 sampled paths and compute **Spearman’s rank correlation coefficient (ρ)** and **deviation from monotonicity (DFM)** at training epochs 10-50.\n- Spearman’s ρ reflects whether a metric consistently increases or decreases with training steps.\n- Deviation from monotonicity is computed as the normalized number of violations from a strictly monotonic trend.\n\n| Metric      | Trend | ρ     | DFM  |     |\n| ----------- | ----- | ----- | ---- | --- |\n| Val Loss    | ↓     | -0.60 | 0.60 |     |\n| Angle Score | ↑     | +1.00 | 0.00 |     |\n\nAs shown in the table, **angle score exhibits perfect monotonic increase (ρ = +1.00)** with zero deviation, indicating high stability across paths. In contrast, **validation loss fluctuates more severely (ρ = –0.60)**, making it a less reliable early-stage signal.\n\n---\n***Answers to Weakness 5:***\n\nWe respectfully clarify that this strategy is **explicitly explained and defined** in Line **214-225** & 73-74. Its effectiveness is validated in Table 3.\n\n---\n\nReference：\n\n[1] Yiming Hu, et al. Angle-based search space shrinking for neural architecture search. In ECCV, 2020.\n\n[2] Xuanyang Zhang, et al. Neural architecture search with random labels. In CVPR, 2021.\n\n[3] Tao Huang, et al. Greedynasv2: Greedier search with a greedy path filter. In CVPR, 2022.\n\n[4] Su X, et al. Prioritized architecture sampling with monto-carlo tree search. In CVPR, 2021.\n\n[5] Haibin Wang, et al. Prenas: Preferred one-shot learning towards efficient neural architecture search. In ICML, 2023.\n\n[6] Zhou, Dongzhan, et al. EcoNAS: Finding Proxies for Economical Neural Architecture Search. In CVPR, 2020.\n\n[7] Minghao Chen, et al. AutoFormer: Searching Transformers for Visual Recognition. In ICCV, 2021."}, {"review_id": "oftZkNyoSS", "reviewer": "Reviewer_mYEj", "summary": "The work introduces Dynamic Path Selection (DPS), a novel strategy for one-shot neural architecture search (NAS) in semantic segmentation that addresses inefficiencies in training large supernets with mixed-quality paths. DPS adopts a stage-wise training paradigm that dynamically prioritizes different path quality proxies—convergence (via angle score), expressiveness (via a learned performance predictor), and generalization (via a multi-scale information bottleneck metric)—to focus optimization on promising architectures throughout training. The approach includes a Pareto-optimal path selection mechanism, diversity-driven exploration to maintain structural variety, and an adaptive stage scheduler for effective proxy utilization. Extensive experiments on benchmarks like Cityscapes and CamVid demonstrate that DPS discovers architectures with better performances.", "questions": "NA", "limitations": "NA", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n\nAddresses a critical limitation in one-shot NAS: the inefficient training and gradient conflict caused by simultaneous optimization of good and poor candidate paths within large search spaces.\n\n\nImplements a stage-wise optimization strategy that aligns training effort with proxy reliability: convergence (angle score), expressiveness (performance predictor), and generalization (MS-IB)\n\nCombines Pareto optimality-based path selection, diversity-driven exploration, and dynamic stage scheduling, enabling focused and adaptive path evaluation.\n\nThe experimental performances achieved a balance between accuracy and computational efficiency, outperforming prior NAS baselines within the same search space.\n\nDemonstrates consistent gains across datasets and robustness scenarios without external data or extra training tricks (e.g., knowledge distillation).\n\n\nWeaknesses:\n\nThe problem framing centers mainly on NAS inefficiencies, which is a well-recognized issue; the paper does not offer theoretical analysis of gradient conflicts or path interference, limiting deeper insight.\n\nThe performance predictor assumes path accuracy estimates from the supernet are reliable enough to train regressors, which may not hold early in training or with larger search spaces.\n\nNo error bars or statistical significance tests are reported, which limits confidence in the generalization of results.\n\nSome of descriptions are not fluent or precise.", "was_revised": true, "final_justification": "Thank you for the response. Increased rating.", "rebuttal": "We greatly appreciate the positive comments and insightful suggestions from the reviewer, please check our answers below.\n\n---\n***Answers to Weakness 1:***\n\nWe thank the reviewer for this thoughtful comment. Our discussion of gradient conflicts and path interference is informed by established theoretical work. We build upon these analyses without repeating their full derivations, allowing us to focus on our methodological contributions.\n\nTo clarify: \n- the discussion of gradient conflicts and path interference in our paper is based on established empirical and theoretical observations in prior work, such as [1, 2, 3], which we explicitly cite to motivate the design of our method. For clarity, we summarize some of the works here:  NASViT [1] formalizes gradient conflict via gradient cosine similarity, showing that negatively aligned gradients between sampled paths disrupt supernet optimization. And in GreedyNASv2 [2], it claims that \"*Equally treating different paths and uniformly sampling them from the supernet could lead to inappropriate training of the supernet, as the weak paths would disturb the highly-shared weights*\".\n- Our **priority-based sampling strategy** directly leverages above theoretical foundation. By prioritizing paths with consistent progress signals, our method **implicitly optimizes for gradient alignment**, reducing destructive interference across paths. Our ablation results (Table I in main paper) confirm the intended effect: priority-based sampling significantly stabilizes supernet training and shows clear advantages over uniform sampling.\n\nTo further address your concern, we are happy to add a supplementary background section summarizing these theoretical results (with explicit citations) in the next version. \n\nWe believe the strength of our work is precisely in how it **connects theory and practice**: extending the conceptual understanding of gradient conflicts and path interference into a concrete, empirically validated solution for NAS inefficiencies.\n\n---\n***Answers to Weakness 2:***\n\nThank you for this insightful observation. We fully agree that performance predictors can be unreliable during early training. In fact, this limitation is **explicitly acknowledged and addressed** in our design. As described in **Section 3.1**, we **intentionally delay the use of the predictor** until Stage II of training (after the model has reached basic convergence).\n\n(1) To further address your concern regarding the reliability of the performance predictor during training, we provide the following table summarizing the Kendall’s τ and Spearman’s ρ correlations at different epochs. The results show that prediction accuracy improves steadily and stabilizes by epoch 200, supporting our design choice to delay predictor activation until Stage II (Under the influence of our dynamic stage scheduler, the training of the supernet typically enters Stage II around 200 epochs).\n\n| Epoch | τ (Kendall) | ρ (Spearman) |\n| ----- | ----------- | ------------ |\n| 50    | 0.53        | 0.72         |\n| 100   | 0.63        | 0.82         |\n| 200   | 0.76        | 0.92         |\n| 400   | 0.75        | 0.92         |\n\n(2) Regarding the concern that **predictor reliability may degrade in large search spaces**, we would like to clarify that, despite this scale, the performance predictor remains highly effective:  \n- as shown in **Figure 3 (main paper)**, the predictor achieves strong rank correlation (e.g., Kendall's τ = 0.76 / Spearman's ρ = 0.92 at epoch 200) with ground truth performance in our massive search speace (up to **10⁴⁸ possible paths**).\n \nThese results demonstrate that our predictor remains **reliable**, even under large-scale search scenarios.\n\n---\n***Answers to Weakness 3:***\n\nWe appreciate the reviewer’s valuable suggestion regarding the reporting of error bars. In response, we have conducted **three additional independent runs** using different random seeds for our main experiments on Cityscapes. These runs follow the same training and architecture search settings as in the original submission. The results are summarized below:\n\n| Method | Seach Space | mIoU (%) |     |\n| ------ | ----------- | -------- | --- |\n| DPS    | SqueezeNAS  | 74.3±0.3 |     |\n| DPS    | FasterSeg   | 73.1±0.2 |     |\n\nAs shown, the low variance confirms the stability of our method across training runs and search spaces.\n\n---\n***Answers to Weakness 4:***\n\nWe appreciate the reviewer pointing this out. We will carefully revise the writing to improve clarity and technical precision.\n\n---\n\nWe thank the reviewer for the valuable feedback and hope our responses have clarified the concerns. We are happy to provide additional details if needed.\n\nReference：\n\n[1] Chengyue Gong, et al. Nasvit: Neural architecture search for efficient vision transformers with gradient conflict-aware supernet training. In ICLR, 2022.\n\n[2] Tao Huang, et al. Greedynasv2: Greedier search with a greedy path filter. In CVPR, 2022.\n\n[3] Haibin Wang, et al. Prenas: Preferred one-shot learning towards efficient neural architecture search. In ICML, 2023."}]

Author rebuttal:

Meta-review: The paper proposes Dynamic Path Selection (DPS), a one-shot NAS strategy for semantic segmentation. DPS dynamically shifts training focus across convergence, expressiveness, and generalization using dedicated proxies (angle score, performance predictor, multi-scale information bottleneck). It integrates Pareto-based path selection, a dynamic stage scheduler, and diversity-driven exploration. Experiments on Cityscapes, CamVid, and BDD100K show DPS achieves state-of-the-art accuracy while maintaining efficiency, outperforming prior sampling- and gradient-based NAS methods.

Reviewers acknowledge the paper’s solid technical design, strong experimental results, and practical improvements in NAS for segmentation. Strengths include integration of multiple proxies, adaptive stage scheduling, and structural diversity maintenance. Weaknesses raised include limited theoretical novelty, computational overhead (especially from MS-IB), and sensitivity to hyperparameters. Some reviewers felt the contributions were more of a well-engineered integration than a fundamentally novel idea. Overall, three out of four reviewers leaned toward (borderline) acceptance due to empirical strength despite theoretical concerns.

Therefore, strong empirical results and well-motivated design outweigh concerns about novelty and overhead; the work might provide meaningful practical advances in NAS for semantic segmentation.
As the AC, I judge that this paper could be accepted.
Decision: Accept (poster)