LDT: Layer-Decomposition Training Makes Networks More Generalizable
Authors: Zaizuo Tang
Zongqi Yang
Yu-Bin Yang
Venue: ICLR 2026 Poster (2026)
Keywords: Domain generalization
Primary area: transfer learning, meta learning, and lifelong learning
TL;DR: We propose the Layer-Decomposition Training (LDT) strategy, which effectively mitigates feature distribution perturbations caused by misclassified unstable layers in existing methods through layer-wise separation of stable and unstable layers.

Abstract: Domain generalization methods can effectively enhance network performance on test samples with unknown distributions by isolating gradients between unstable and stable parameters. However, existing methods employ relatively coarse-grained partitioning of stable versus unstable parameters, leading to misclassified unstable parameters that degrade network feature processing capabilities. We first provide a theoretical analysis of gradient perturbations caused by unstable parameters. Based on this foundation, we propose Layer-Decomposition Training (LDT), which conducts fine-grained layer-wise partitioning guided by parameter instability levels, substantially improving parameter update stability. Furthermore, to address gradient amplitude disparities within stable layers and unstable layers respectively, we introduce a Dynamic Parameter Update (DPU) strategy that adaptively determines layer-specific update coefficients according to gradient variations, optimizing feature learning efficiency. Extensive experiments across diverse tasks (super-resolution, classification, semantic segmentation) and architectures (Transformer, Mamba, CNN) demonstrate LDT's superior generalization capability. Our code is available at https://github.com/ZaizuoTang/LDT.

Published as a conference paper at ICLR 2026
LDT: LAVER-DECOMPOSITION TRAINING MAKES NETWORKS MORE GENERALIZABLE
Zai-Zuo Tang, Zong-Qi Yang, Yu-Bin Yang
State Key Laboratory for Novel Software Technology
Nanjing University
Nanjing, China
(tangzz, zongqi sma www.uuuuuuuuu.e... edu. cn, yangyubinenju.cm edu.
ABSTRACT
Domain generalization methods can effectivvly enhance network performance on test samples with unknown distributions by isolating gradients between unsta- ble and stable parameters. However, existing methods employ relatively coarse- grained partitioning of stable versus unstable parameters, leading to misclassified unstable parameters that degrade network feature processing capabilities. We first provide theoretical analysis of gradient perturbations caused by unstable pa- rameters. Based on this foundation, we propose Decoo-eccomooitioonn Training (LDT). which conducts fine-grained layer-wise partitioning guided by parame- ter instability levels, substantially improving parameter update stability. Further- more, to address gradient amplitude disparities within stable layers and unstable layers respectively, we introduce Dynamic Parameter Update (DPU) strategy that adaptively determines layer-specific update coefficients according to gradient variations, optimizing feature learning efficiency. Extensive experiments across diverse tasks (super-rexolution, classification, semantic segmentation) and archi- tectures (Transformer, Mamba, CNN) demonstrate LDT's superior generalization capability. Our code is available at https//gtthub.ccm///aaaaoTTTa/
INTRODUCTION
With advances in neural networks and computing hardware, neural network-bassed methods have be- come dominant across visual tasks, spanning both high-level vision (image classification(Lee et al,, 2025), , semantic segmentation (Zhang al. 2025). object detection(Chen
al. 2025), point cloud (zaioouuuutinoooouuuu (Zhou et al., 2.23), image generation Shi al. (024 However, their superior performance critically depends on the assumption that training and test data share similar distributions.
segmentation Signenntttioo(Tang/aaioooo et al.J (023)) and low-level vision (image super-resolution
In real-world scenarios, due variations illumination conditions and imaging devices, the test sample distribution (target domain) and training sample distribution (source domain) exhibit signif- icant differences, termed as domain shift. Domain shift causes networks that perform well on the source domain to suffer severe performance degradation on the target domain, which greatly lim- its their application high-coofidence-demand tasks (e.g., medical diagnosis, autonomous driving, etc.).
Consequently, domain generalization methods have emerged (Kumar et al.) 2022 Pahk al. 2025 Wang et al.) 2024b), which effectively enhance the generalization capability of networks through data augmentation strategies (Vaish et al., 2024b] Xu et al. 2025 Zheng et al. 2024) and explicit learning of Nomain-invarian features (Huang al. 2024 Li al. 2024b). Data augmentation- based domain generalization methods fall into two categories: (1) Input sample augmentation im- proves network robustness different degradation patterns through rotations, crops, and frequency- domain perturbations of input samples. (2) Network architecture augmentation improves parameter generalization via perturbations specific layers channels (e.g.. Dropout (Hinton et al., 2012))- Explicit domain-invariant feature learning methods enhance model robustness by decomposing input
"Corresponding author

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Published as a conference paper at ICLR 2026
Warm--up Stage
Finc-tuning Stage
Iackbere
Prodktion
held
201
LP-FT
DeFT
LDT
Figure 1: Comparison with existing methods. Existing methods consist two stages: a warm-up stage initializing the prediction head, and fine-tuning stage. During fine-tuning, LP-FT (Kumar et al. 2022) fine- tunes the entire network, while DeFT (Pahk et al., (2025) treats the backbone network as stable layers and the prediction head as unstable layers, constructing primary and auxiliary networks with cross-frreziing backbone stabilize gradient updates. Our proposed LDT method achieves finer-
and prediction head components grained hicrarchical separation of stable and unstable layers and incorporates the dynamic parameter update (DPU) strategy into the parameter update process. Notably, for low-level vision tasks such as super-resolution (SR), the network's prediction head replaced with upsampling module.
Sactibone
Siitants
3.50
Unstable tayer
Stable lyyer
Mugritude of standare deviations
sazs 6330
Unokaske Layars
Stable Layss
9999
(a)
(b)
Figure 2: Gradient stability analysis. (a) Gradient stability analysis for each layer both the backbone net- work and prediction head. We feed 600 samples through the network to collect layer-wise gradients without performing parameter updates, then compute the standard deviation of each layer's gradients across these sam- ples. (b) Gradient stability analysis for unstable layers. (c) Gradient stability analysis for stable layers. We statistically analyze gradient variations stable layers unstable layers partitioned by L.D,, respectively.
features into domain-invarian and domain-specific components, then selectively emphasis domain- invariant representations while suppressing domain-specific variations. (For related work on domain generalization methods, refer to Appendix A
Although existing domain generalization methods have extensively investigated input samples, net- work architectures, and intermediate features, their exploration of parameter correlations in domain generalization tasks remains limited.
Two recent works, LPFT (Kumar al. 2022) and Pehk al. 2025). have conducted pre- liminary exploration of contnneter-t--paaameee correlations in networks, focusing on network gen- eralization in fine-tuning scenarios. There exists randomly initialized prediction head and back- bone network pretrained with large-scale data (ImageNet (Deng et af.) 2009)), where the backbone possesses strong feature processing capability and generalization performance. They argue that dur- ing fine-tuning, the random parameter distribution in the uninitialized prediction head will perturb parameter updates in the backbone network, ultimately compromising the network's 's overall perfor- mance and generalization capability. As shown in Figure both LPFT and DeFT methods divide the training process into a warm-up stage and a fine-tuning stage. During the warm-up stage, they freeze the backbone network to prevent interference from the prediction head. In the fine-tuning stage, the LPFT method fine-tunes all network parameters to maximize feature learning efficiency. The DeFT method maintains isolation between the prediction head and backbone network during fine-tuning by constructing a branch architecture with cross-freett freezing of the backbone and prediction head, thereby preventing interference from the prediction head.
Gradients represent the change network parameters in response to current input samples. The network contains unstable parameters that exhibit extreme sensitivity input feature distributions minor variations can trigger severe fluctuations these parameters. These unstable parameters con- stitute the fundamental factor impairing network generalization performance. Through theoretical
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Identification of stable and unstable layers
Gradient isolation and stabilization
Inference
Wamn-up stage
Network training
Loss
Var
Weight
Var
DPU
Fioeea Weight
Layers
Thinlook
Stakle
Layes
Layers
Unsable
Layer selection stage
Apci Earry neturel
DPU
Layer
Figure 3: Overall framework.
analysis (Appendix B), we demonstrate these unstable parameters significantly influence parameter updates throughout network layers by interfering with gradient propagation.
Due to the stochastic nature of unstable parameter fluctuations, their gradient directions are typically more random, exhibiting higher variance. Therefore, as shown Figure we statistically analyze gradient variations of network parameters at the layer-wise level across different input samples, revealing the following two issues:
Insufficient granularity in partitioning stable and unstable parameters: existing methods such as LP-FT (Kumar et 2022) and DeFTT (Pahk et al. 2025) designate the backbone network as the stable layers and the prediction head as the unstable layers. However, as shown Figure 2a certain layers within the backbone network demonstrate higher gra- dient variance compared to the prediction head, indicating that some backbone layers are actually less stable than the head. These unstable parameters in the backbone substantially influence parameter updates across all other layers. As result, existing partitioning meth- ods based on the backbone-heal distinction operate an insufficient granularity, leading to misclassification issues.
Inadequate adaptability parameter updates: even within the unstable layers, significant disparities in gradient variance exist (with the ratio between the minimum and maximum standard deviations approaching 10x, as shown Figure 2b). Existing methods apply the same update strategies to all layers, despite their varying gradient standard deviations, which inevitably leads to underutilization of information. This issue is even more pro- nounced among stable layers, as illustrated in Figure 2c
To address the low partitioning granularity issue, we propose the Deyeo-Decooposition Training (LDT) strategy. LDT performs layer-wise partition of stable and unstable layers based gradient variance of parameters across network layers, and employs subsequent cross-freezing to prevent gra- dient interference from unstable layers to stable ones, effectively enhancing network generalization. For the parameter update adaptability problem, we further introduce the Dynamic Parameter Update (DPU) strategy within LDT framework. DPU projects gradient variance into parameter update efficients, enabling networks to adaf-idap determine update ranges, thereby further improving generalization performance.
Our main contributions can be summarized as follows:
We first provide theoretical analysis of perturbation effects from unstable parameters to stable parameters. Building on this foundation, we propose Layer-Decompostiee Training (LDT), which mitigates perturbations from unstable layers during training through explicit separation stable and unstable layers, effectively enhancing domain generalization.
We develop Dynamic Parameter Update (DPU) strategy that adapts update coefficients based on fluctuation amplitudes, demonstrating superior adaptability compared to conven- tional EMA methods.
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Table Symbol Definitions
Symbol
Meaning/ Descriptionn
Symbol
Mcaaing/Desccrippn
PM
Primary Network
AM
Auxiliary Network
PL
Primary Network Layer
AL
Auxiliary Network Layer
P0
Parameters of the Primary Network
A0
Parameters of the Auxiliary Network
Frozen Non-trainable
A
Gradient
W
Parameter Update Coefficient
Input Features
V.2
Prediction Result
Ground Truth Label
Primary
Auxiliary
.5
Stable
J
Unstable
,80
Source Domain
.T Target Domain
Our method is architecture-agnosii and task-agnostic, validated across diverse vision tasks (both high-level and low-level) and architectures (Transformer, Mamba, CNN), demon- strating LDT's general effectiveness.
METHOD
2.1
PROBLEM DEFINITION AND NOTATION
Given a source domain D (Df s.. } and a target domain D E (DT, ,..... DT }. which comprise no and n² sub-datasets respectively and are disjoint D n D = 9. We per- form supervised training of network M the source domain D to enhance its generalization performance, i.e., achieve satisfactory performance on target domain D that are unknown during trainin
n²
For clarity, Table provides a unified definition of the notations used throughout this paper.
2.2 OVERALL FRAMEWORK
As shown in Figure 3 the training procedure of LDT consists of two components: (1) Identifica- tion of stable and unstable layers, and (2) Gradient isolation and stabilization. The identification component contains two sub-stages: the Warm-Up stage initializes the prediction head, while the Layer Selection stage performs fine-grained layer-wise partitioning of stable and unstable layers. In the gradient isolation component, the network is duplicated into two copies (primary and auxiliary networks) with alternating layer freezing: unstable layers are frozen the primary network while stable layers remain trainable; conversely, stable layers are frozen in the auxiliary network while unstable layers are made trainable. During simultaneous training of both networks, the proposed Dynamic Parameter Update strategy (DPU, Section 2.4) adaptively adjusts the frozen parameters in both networks. After training completion, frozen layers from both networks are extracted and combined into composite network for test-time inference. (See Appendix E for the complete training pipeline pseudocode)
2.3
TRAINING (LDT)
2.3.1
MOTIVATION
The gradient represents the change in network parameters induced by current inputs, where larger gradients indicate more significant parameter modifications, When a sample is fed into the net- work, large gradients primarily arise from two scenarios: (1) parameters possess strong feature processing capabilities and exhibit stronger feedback to current inputs; (2) parameters are overly sensitive to input samples, where minor input variations cause large parameter fluctuations, thereby generating large gradients. In the second scenario, the parameters exhibit high sensitivity to do- main shift, severely degrading performance on target domains with unknown distributions. More critically, during network training, unstable parameters (second scenario) interfere with the gradients of stable parameters (see Appendix [B] for theoretical proof). Furthermore, through iterative training (forward propagation-gradiens computation-paranet updates), this interference

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becomes progressively amplified. Therefore, aim to decouple unstable parameters' interference with stable ones while stabilizing updates of unstable parameters.
2.3.2 IDENTIFICATION OF UNSTABLE AND STABLE LAYERS
Since the training sample distribution is relatively uniform, (as discussed in the preceding subsec- tion) the first case results in stable, directionally consistent parameter updates with low gradient variance. contrast, parameters in the second scenario exhibit random fluctuations with stochastic update directions and high variance. Therefore, ance, using layers as the partition unit treating layers with high gradient variance as unstable and those with low variance stable. Notably, to prevent interference from randomly initialized weights when obtaining gradient variance, a warm-up stage is introduced that initializes the network using a subset of the source domain.
propose to partition parameters based on vari-
The source domain samples are partitioned into two subsets D = {D D During the warm- up stage, one source domain subset D is used for network parameter initialization. After network initialization, the other source domain subset D fed into the network, where gradients at each layer are preserved without parameter updates. Subsequently, LDT calculates the variance of each layer's s gradients across samples from source domain subset D&= defining layers with high variance as unstable layers and those with low variance as stable layers,
Name =
Ratio U M),
(1)
Nam Name Name
where Alle is the set of all layer names in network M, Top_N selects the top-ranked layer names based on each layer's gradient variance to define unstable layers Name The stable layer name set Name is the complement of unstable layer Name name set within the full set N Aime R indicates the ratio of unstable layers.
2.3.3 GRADIENT ISOLATION AND STABILIZATION
As shown Figure stabilize parameter updates in unstable layers, inspired by (Kumar al. 2022 Pahk et al. 2025), the LDT method adopts a parallel dual-branch training strategy. It dupli- cates the inttialized network from the warm-up stage into two copies: the primary network PM and auxiliary network AM. Subsequently, LDT freezes the unstable layers in the primary network, pre- venting gradient updates via the loss function, while employing the Exponential Moving Average (EMA) algorithm to update primary network's frozen unstable layer parameters using those from auxiliary network's 's unfrozen unstable layers. This mechanism aims to stabilize the unstable lay- in primary network by leveraging multi-timestep parameters from auxiliary network's 's unstable layers,
Po w Po. (11 Wy) x A
where O0 denotes the parameters of the frozen unstable layers in the primary network, AO cor- responds to the parameters of the unfrozen unstable layers in the auxiliary network, and Wy is the parameter update coefficient. value of W, closer indicates that more timesteps the auxiliary network's unstable layer parameters A are required to induce changes in the primary network's unstable layer parameters P0"
To eliminate gradient interference from unstable layers to stable layers, the stable layers in the auxiliary network are frozen, allowing loss function gradients update only the unstable layers in auxiliary network. The frozen stable layer parameters in auxiliary network can only be updated via the unfrozen stable layers in the primary network. This method not only severs gradient interference from unstable to stable layers (via network freezing), but also enables stable layers to adapt to the stabilized parameter changes from unstable layers (via EMA)
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The forward propagation processes and loss computations for both primary and auxiliary networks are as follows:
v² = PM)x), where
PM Cat(PL ,LL
(4)
s AM(x), where AM Cat ALL)) A AP8 funcly w) AAD grad funcly ,g),
(5)
where PL and PL" represent the unfrozen stable layers and frozen unstable layers in the primary network PM respectively, while AL and AL correspond to the frozen stable layers and unfrozen unstable layers in the auxiliary network AM. Cat denotes the concatenation of layers from the collection. x; y", yd, and y denote the input features, predictions from primary and auxiliary networks, and the ground truth label, respectively. grad-fune is the gradient computation function that acquires the gradients of stable layer parameters in the primary network A PPS and the gradients of unstable layer parameters in the auxiliary network AO
In summary, the gradients from the loss function can only update the stable layers in the primary network and the unstable layers in the auxiliary network. The frozen unstable layers in primary network and the frozen stable layers in auxiliary network are respectively updated via the EMA algorithm using the unfrozen unstable layers in auxiliary network and the unfrozen stable layers in primary network.
PO P A APOS, A0 = AO -AAO",
A0 (+1 W Ad + (1 11--10 W,) x PO
(8)
Po = W x PO2 + (1-W)) x AO
where POS, P0. A0 and A represent the parameters of stable layers in the primary network before and after gradient updates, and the parameters of unstable layers in the auxiliary network be- fore and after gradient updates, respectively. P0 Pe AB, and AB +1 represent the parameters of frozen unstable layers in the primary network before and after EMA updates, and the parameters of frozen stable layers in the auxiliary network before and after EMA updates, respectively. W, the parameter update coefficient that controls the influence strength from corresponding layers in the another network on the current layer's parameter updates. A larger value of Wy indicates weaker influence the current layer, and it is typically set to 0.99.
2.4 DYNAMIC PARAMETER UPDATE
The LDT method effectively avoids gradient interference from unstable to stable layers and stabi- lizes unstable layers' gradient updates through its cross- freezing of stable/unstable layers and EMA algorithm. However, shown in Figure 2b] and Figure 2c] there remains significant variation in fluctuation magnitudes between layers within the unstable and stable layer groups. If the same pa- rameter update coefficient W, is applied to all layers when EMA updating, would inevitably result in underutilization of information and counterintuitive behavior.
When layer exhibits higher variance (greater fluctuation magnitude), it should incorporate param- eters from more timesteps to stabilize its parameter updates. Conversely, when a layer demonstrates lower variance, indicating more stable gradient updates and stronger generalization capability, its feature learning capacity should be enhanced by strengthening its parameter update efficiency.
During the EMA-based parameter update process, where frozen parameters are updated using un- frozen parameters via the EMA algorithm, parameter update coefficient W, closer to 1 implics lower influence weights of unfrozen parameters on frozen parameters, requiring more timesteps of unfrozen parameters to induce updates to frozen parameters. Conversely, smaller W, corresponds to higher influence weights of unfrozen parameters, enabling substantial updates to frozen parame- ters with fewer timesteps of unfrozen parameters. Therefore, we propose to refine the EMA algo- rithm's 's parameter updates for frozen layers by assigning larger update coefficients to high-variance frozen layers (enabling reference to more timesteps of unfrozen parameters) while giving smaller coefficients to www-aarince frozen layers (allowing significant updates from fewer unfrozen param- eters), thereby enhancing overall update efficiency.

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SAFMN:31.7
31T:31.96
MambalR:32..0
panasonic 132.pmg
GT
+3DT:33.33 +LDT:33.33
+LDT:33.70
Visual comparison of LDT. The large image on the left is the LR image, and the sub-images on the right are LR. SAFMN, HAT, MambalR (first row), GT. SAFMN LDT, HAT LDT, MambalR + LDT (sccond row). The value following the name represents the PSNR metric of the current patch.
Figure 4:
The dynamic parameter update (DPU) strategy first sorts all stable and unstable layers in descending order of their variance magnitudes, respectively, and subsequently calculates each layer's relative ranking position.
Get Get.index (V arf, Var
Rank?
where i (1...NN),
NS
(9)
Rank! Get,inder (Var), Var")
where jE (I...NNN
NU
where Get inder(V ( Var denotes obtaining the rank order of the i th stable layer's vari- ance Vars among all stable layer variances V V and Get Get.index(V ary V Var") follows the same principle. N and N represent the quantities of stable layers and unstable layers respectively. Subsequently, DPU calculates the parameter update coefficient for the current layer based on its obtained ranking position:
S W Base + Rank RRnk
(10)
W = Bose + Rank Rank)
where W:S and 1 denote the parameter update coefficients for the th stable layer and j th unstable layer, respectively. Base and W Base are set to 0.99 and 0.999 respectively, while Rank and Rank are configured as 0.01 and 0.001.
Referring to Eq the frozen unstable layers in the primary network and the frozen stable layers in auxiliary network are updated using their corresponding parameter update coefficients,
Ao W x Ao, + (1 - WS) x POS
(11)
po WU x PO + (1 - w x A0
where 8 E (W 3 W E (W .... W No represent the parameter update coeffi- cients for stable layers in the primary network and unstable layers in the auxiliary network respec- tively.
2.5 TEST-TIME INFERENCE
After training the dual-branch network using LDT and DPU strategies, we extract (1) the frozen unstable layers from the primary network and (2) the frozen stable layers from the auxiliary network, then concatenate them to construct the composite network MC. During test-time inference, MC performs predictions on input samples. This workflow can be formally expressed as:
MC = Cat(AL PT.) } y = MC(x).
(12)
(13)
where x and y denote the input sample and its corresponding prediction, respectively.

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Table 2: Effectiveness validation of LDT. The samples the Olympus-camen camera branch are selected as source domain, while those from the remaining camera branches constitute the target domain. Performance is evaluated using PSNR and SSIM metries. The experiment was repeated three times, with results reported mean + standard deviation.
Method
Sony
Baseline
30.81/0.8688
30.81/0. 8850
30.22/0.8753
LDT LDT & DPU
31.20 = 0.08830.863 + 4.58c-4
31.25 +
0.2768/0.8746 = 4.79c-3
31.23 + 0.092.00.886 + 7.02c-4
31,36 +: 0.0469/0.80 8611 + 6.03e-4
32.15 +: 0. 1953/08..880 + 3.03e-3
31.51 + 0.1351/0.8865
+ 1.16e-3
Method
Canon
Baseline
30.17 = 31101AL8737
30,93/08811
LDT
+ 5.69c-4
32.33 +
0.2187/0.9236 + 1.23c-3
LDT & DPU
30.37 +: 0.1367/0.8705 + 3.61e-4
32.80 :: 0.256000.9246 + 1.51c-3
Table 3: Ablation experiments stable/unstable layer partitioning criteria
Method
Pan
Sony
DSC
IMG
Canon
Baseline
30.81/0.8688
30.81/0.8850
30.22/0.8753
30.01/0.8737
30.93/0.8617
Random
90.96/0.8619
30.88/0.8692
31.00/0.888
30.02/0.8733
32.05/0.9217
Mean
31.18/0.855 85188
11.86/0.8833
$1.28/0.8854
30.47/0.8706
32.39/0.9226
Var/Mcan
31.27/0.861 8615
31.89/0.888
31.37/0.8870
30.50/0.8717
32.59/0.9248
Var 31.36/0.8611
32.15/0.8880
31.51/0.8865
30.57/0.8705
32.80/0.9246
EXPERIMENTS
3.1 EXPERIMENTAL SETUP
Datasets: For image SR tasks, we employ the DRealSR dataset (Wei et al. 2020). which consists of images captured by multiple cameras (Olympus, Pan, Sony, DSC, IMG, Canon). Since each camera possesses distinct hardware parameters, samples collected by different cameras exhibit significant domain shift. During experiments, we select images from one or multiple cameras as the source domain, while using images from the remaining cameras the target domain. For image classi- fication tasks, we employ the VLCS dataset (Torralba & Efros 201T), which comprises the VOC, LabelMe, Caltech, and SUN datasets. The VOC dataset contains diverse daily-life scene images, the LabelMe dataset exhibits multi-scene characteristics, the Caltech dataset focuses on specific objects (e.g., vehicles), and the SUN dataset covers various indoor and outdoor scene images. We adopt the DomainBed-consiser training strategy, specifically cross-training validation. For semantic seg- mentation tasks, employ the Cityscapes (Cordts al. 2016). BDD100K (Yu al. (2018), and Mapillary (Neuhold et al. 2017) datasets, which contain diverse autonomous driving scenarios with distinct styles.
Network architecture: For SR tasks, we validate the effectiveness of our proposed method on networks based on CNN, Transformer, and the recently popular Mamba architectures, specifically SAFMN (Sun et al.] 2023). HAT (Chen et al. 2023), and MambalR (Guo et (0.4a) respec- tively. For the image classification task, we employ ResNet-18, ResNet-50((He et al., 2016a). ViT (Dosovisskiy et al. 2021), and Vision Mamba (Liu et al. (024) network architectures. For semantic segmentation tasks, we adopt an architecture consisting of a ResNet-50 He et al. (001bb backbone with DecpLabV3++ Chen et al. (2018) prediction head.
Implementation details:
The input patch sizes are 48 x 48 for SR tasks, 224 x 224 for classification tasks, and 512 x 512 for semantic segmentation tasks. We employ 4x V100 GPUs as training devices. It is worth noting that since SR data processing is relatively straightforward and constitutes pixel-level task, it is more susceptible to domain shift effects. Consequently, we conducted ablation experiments on the SR task branch.
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Table 4: Ablation experiments on training/inference efficiency. The task is image super-resolution, with training and inference patch sizes set to 48x48 and 200x200 pixels respectively. The network architecture based on MambalR.
Method
Training memory (GB)
Inf memory (GB)
Training time (S)
Inf time (s)
Baseline
15.27
2.7
0.6912
658.3287
DeFT Pahk et al. 2025 LDT
20.30
2.7
1,2566
653.9900
20.25
2.7
1.2608
643.6736
Table 5: Comparative experiments
Network
Pan
Sony
DSC
IMG
Canon
IODA (Tan & Yang 2024
30.97/0.8594
$1.3120.8807
31,05/088822
30.15/0.8728
31.87/0.9216
SRTTA (Deng al. 2025
29.88/0.8359
31.24/0.871
29.93/0.6639
29.78/0.8580
31.88/0.9146
Wang et al 2024a
$1.28/0.8626
31.53/0.8818
31.34/0,8875
30.42/0.8775
32.72/0.9269
DTAM HHung ct al. 2024
$1.23/0.8615
31.29/0.8773
31.29/0.8864
30.32/0.8747
32.65/0.9256
START
d 20201D
31.28/0.8609
31.41/0.8774
31.29/0.8862
30.33/0.8743
32.70/0.9261
MambalR + I.P-FI [Kmma ml.
2022
30.99/0.88 8621
30.97/0.8722
30.82/0.8827
29.93/0.8696
31.64/0.9212
MambulR + DeFT (Pank el al.
20233
31.27/0.8632
31.61/0. 8801
31.34/0.8875
30.31/0.8726
$2.40/0.9247
MambalK LDT
31.36/0.8611
32.15/0.8880
31.51/0,8865
30.57/0.8705
32.80/0.9246
3.2
ABLATION EXPERIMENTS
3.2.1
ABLATION EXPERIMENTS FOR EACH COMPONENT OF LDT
To validate the effectiveness of our proposed method, we conduct ablation studies for each module. The performance metrics of models fine-tuned on the source domain and evaluated on the target domain serve as baseline results.
We first evaluate the network performance with only the Layer-Decomposition Training (LDT) strat- egy implemented (without DPU). As shown in Table. [ LDT improves the SR network's perfor- mance across all target-domain camera branches, with the most significant PSNR gain of 1.4 dB observed on the Canon data branch. By isolating gradients between stable and unstable layers, the LDT strategy prevents perturbations from large parameter fluctuations in unstable layers during sta- ble layer updates, effectively enhancing parameter update stability. Subsequently, we incorporate the proposed Dynamic Parameter Update (DPU) strategy with LDT, yielding further performance improvements on the target domain notably a 0.4dB PSNR increase on the Sony branch. Through finer-grained processing of gradient amplitude variations within both unstable and stable layers, DPU enhances update adaptability and further boosts the network's 's generalization capability.
3.2.2 ABLATION EXPERIMENTS ON STABLE/UNSTABLE LAYER PARTITIONING CRITERIA
To verify the impact of stable/unstable layer partitioning criteria network generalization perfor- mance, we conducted the following experiments: (1) Random partition, where layers were randomly assigned as stable unstable. (2) Gradient magnitude-base based partition, where layers were sorted by their mean gradient magnitudes across input samples, with layers exhibiting larger mean gradients designated unstable. (3) Variance-based partition, where layers were sorted by gradient variance across input samples, assigning those with higher variance as unstable. (4) Normalized gradient vari- ance partition. Under identical fluctuation amplitudes, layers with higher gradients exhibit greater variance than those with lower gradients. To eliminate this bias, we implement normalized gradient variance partitioning (Var/Mean).
As shown in Table 3 random partitioning of stable and unstable layers provides only marginally improvements in network generalization performance. While the fual-branch training strategy en- hances parameter update stability via EMA, misclassification between unstable and stable layers re- duces gradient interference isolation efficiency, ultimately limiting generalization gains. Using mean gradient magnitude for stable/unstable layer partitioning yields modest generalization improve- ments, though underperforms LDT's variance-based method. Large gradients primarily emerge from two scenarios: (1) the network encountering new distribution samples requiring adaptation, and (2) certain layers exhibiting excessive sensitivity to input variations. Employing only gradient averages for layer separation would incorrectly categorize the first scenario's layers unstable (sub- sequently frozen during fine-tune tuning), thereby impairing the network's 's feature learning capacity. As mentioned in Section 2.3.2 the first scenario produces more coherent parameter updates with lower

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gradient variance, owing to the aligned distribution of training samples, whereas the second see- nario demonstrates more randomized update directions and larger gradient variance. Using variance the metric distinguish stable and unstable layers effectively distinguishes between these two scenarios, achieving strong performance across all four camera branches in the target domain. While normalized variance outperforms variance in certain camera branches, the LDT method adopts vari- ance as the layer partition metric to preserve methodological simplicity.
3.3 ABLATION EXPERIMENTS ON TRAINING/INFERENCE EFFICIENCY
To validate the method's ss impact computational efficiency, we systematically evaluate GPU mem- ory consumption during both the training and inference stages for: (1) the baseline method, (2) DeFT [32], and (3) our proposed LDT, as quantified in Table | We further measure the training time per image and inference time across the entire Olympus-camera branch dataset for all com- pared methods.
Although DeFT and LDT introduce an auxiliary network, their additional parameters remain frozen (excluding them from gradient computation), resulting in limited memory overhead. Crucially, dur- ing inference, LDT maintains identical memory consumption to the baseline since only a single network processes input images.
3.4 COMPARATIVE EXPERIMENTS
As shown in Table we compare our proposed LDT method with other domain generalization methods, including Pahk et al. 2025),
methods LP-FT (Kumar et al. (022) and DeFT based domain generalization methods START (Guo al., 2024b), DTAMiHHaan et al. 2024), and Wang et al. (2024a), as well as domain adaptation meth- ods TODA Tang Yang 2024) and SRTTA (Deng et al. (023) trained on both source and target domains. For instance, compared to DeFT, LDT obtains PSNR improvement of 0.54 dB on the Sony data branch. Visual comparisons in Appendix Figure F further confirm that the super-resolved images produced by LDT exhibit clearer and more accurate texture details.
CONCLUSION
this paper, we propose the Layer-Decomposition Training (LDT) strategy, which effectively mit- igates feature distribution perturbations caused by misclassified unstable layers in existing methods through layer-wise separation stable and unstable layers. Furthermore, the proposed Dynamic Pa- rameter Update (DPU) strategy enhances the network's adaptability amplitude variations within both stable and unstable layers by adaptively determining update coefficients based on gradient oscil- lation levels, thereby improving generalization performance. Extensive experiments across diverse tasks and architectures demonstrate LDT's effectiveness and universality.
ACKNOWLEDGMENTS
This work supported the Natural Science Foundation of China (Grant 62176119). Addi- tionally, we would like to thank Wei-Wei Zhao for conducting the LDT experiments in the NLP classification tasks.
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A RELATED WORK
A.1 DATA AUGMENTATION-BASS DOMAIN GENERALIZATION METHODS
Data augmentation strategies Vaish et al. 2024a Cao et al. 2022 Wang et al. 2021 Castellini
al. 2023: Xu et al. 2022) aimed reduce neural networks overfitting problems through data perturbation and increasing sample feature diversity, thereby improving the networks' generalization performance.
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For image classification tasks, the Mixup (Zhan et al. 2017) data augmentation method randomly mixed two images while proportionally transforming the class labels according to the mixing ratio, effectively increasing the diversity of original samples. CutMix (Yun et 2019) randomly cropped partial regions of images and covered the cropped regions with processed crops from other images. Liu et al. (2022) designed token-level Mix strategy specifically for Transformer networks. [slam et al.] (2024) argued that randomly mixing two images may not only omit important portions of the input images but also introduce label ambiguities. Therefore, they employed a Diffusion architecture for image generation, avoiding the label ambiguity problem. |Fan et al. (2024) designed a data aug- mentation strategy for instance segmentation tasks, effectively expanding the diversity of training samples. Wann et al. (2024c) proposed a foreground- -araunndddaakkoundddo separation-based data augmenta- tion strategy, where they effectively enhanced training sample diversity by combining foreground features with different background features. FFnn et al. (2019) addressed the overfitting problem of SR models with small data samples by proposing a Mixup data augmentation strategy, which directly merged two LR images at random ratios, effectively increasing training sample diversity. Yoo et al. (2020) argued that existing data augmentation strategies like CutMix could destroy spa- tial relationships between image pixels, harming SR task performance. Therefore, they adopted cross-augmentation method (CutBlur) that pasted HR images onto upsampled LR images and added upsampled LR images to HR images. They claimed that compared to existing data augmentation methods, CutBlur not only taught the network how to perform SR but also taught it which regions to super-resolve. preventing the network from producing overly sharp images. [iioo et al. (2023) first proposed a data augmentation strategy specifically for light field image SR tasks. They randomly augmented images using CutBlur on light field images' unique multi-view images, effectively im- proving light field SR performance. Chao et al. (2024) further enhanced light field SR performance by proposing a dual spatial-angular data augmentation strategy based on CutMix. The CutBlur (Mi) & Yang. 2025) method achieved sample diversity expansion by mutually pasting and covering LR and HR images. However, the ADD method (Zeyu & Yubin (025) discovered that CutBlur could cover high- inth--iffmmaaonn regions in images, causing information loss. To address this, they introduced an attribution algorithm to guide the pasting process, ensuring only low-information regions were covered each time, effectively preserving information richness.
A.2 DOMAIN-INVARIANT FEATURE LEARNING-BASE DOMAIN GENERALIZATION METHODS
Domain generalization methods targeting domain-invariant features typically decomposed input fea- tures into Nomain--nvariann and domain-specific components. By preserving learning capacity for domain-invariant features while reducing sensitivity to domain-specific variations, these methods effectively enhanced model generalization performance.
Previous domain generalization methods primarily focused on processing either high-level or low- level features. DomainDrop (Guo et al.] 2003), however, operated along the channel dimension by identifying and suppressing channels containing domain-specific features through channel activa- tion values. The Dropout method (Hinton et al., 2012) improved network robustness by randomly dropping connections between layers. However, Wang et al. (2024a) argued that using Dropout in low-level vision tasks could lead to loss of feature diversity, thereby degrading network performance. Given this limitation, they proposed degradation consistency loss that enforced consistent predic- tions across differently degraded images, thus enhancing network robustness. Ahn et al. (2024) maintained that image augmentation should not alter the relationships between objects in images. They constrained objects in augmented images using covariance and employed contrastive learning to enhance feature discriminability while preserving generalization performance. Chattopadhyay jet al. (2023) investigated domain generalization from virtual to real scenes, noting that "synthetic images have less variance in high-frequency components of the amplitude spectra compared real images." Based on this assumption, PASTA perturbed the amplitude spectra of synthetic images in the fourier domain to generate augmented views. DGMamba (Long et al. 2024) conducted domain generalization research on the recently popular Mamba network architecture (Gu & Dao). They addressed the issue of accumulated domain shift caused by iterative hidden state updates in Mamba networks by proposing hidden state random shuffling data augmentation strategy. The START method (Guo et al. 2024b) further refined input feature processing by dividing features into fore- ground features (affecting predictions) and domain-speciffe background features based activation values. START improved background robustness through style swapping between background fea- tures and randomly generated features. Huang et al. (2024) improved the foreground/hackgrommn
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feature partitioning strategy. They argued that foreground features determining object predictions should exhibit high correlation with other patch features. Thus, they identified patches with high co- variance values foreground features and others as background. [ et al. (2024a) introduced CLIP to domain generalization tasks, using text descriptions with CL.IP's text encoder to guide feature learning, and implemented domain-specifie feature filtering through generated channel and spatial masks, effeetively improving network robustness. [Cheng et al.] (2024) leveraged large language models reason about domain-specific and domain-invariant features, constructing memory bank from domain- -omain--pecific features to guide subsequent inference. Zhao et al. (2022) proposed a test- time domain generalization method, hypothesizing that amplitude images from fourier transforms contained feature intensity information (considered as style information and domain-specifie). They therefore augmented amplitude images to enhance robustness and reduced feature distance between test and source domain samples by incorporating source domain features during testing. Parr et al.) (2023) further categorized test samples, retaining those similar to source domain features while applying style transfer to samples with large distribution gaps. Yu & Hwang (2024) added noise prompts around input images to reduce distribution distance between test and source domain sam- ples.
Existing domain generalization methods primarily investigated three directions: input samples, in- termediate features, and network architectures, while largely neglecting parameter correlation anal- ysis. Due to gradient backpropagation through layers, parameter updates exhibited strong interde- pendencies, where fluctuations in individual parameters induced network-wide perturbations that significantly degraded generalization performance.
Kumar al. (2022) discovered that linear probing demonstrated superior performance compared to full fine-tuning when handling samples with large domain shifts, while full fine-tuning outper- formed linear probing on data with smaller distribution shifts. Therefore, they divided the training process into two stages: first initializing the prediction head using linear probing, then adjusting the entire network through full fine-tuning, effectively enhancing network robustness. Pahk et al. (2055) observed that backbone networks pre-trained on large datasets demonstrated superior feature extraction capabilities compared to randomly initialized prediction heads. They demonstrated that joint fine-tuning of both components enabled the prediction head to perturb backbone features, con- sequently degrading its representational capacity. To mitigate this, they decoupled the fine-tuning processes of the backbone and prediction head, while introducing parallel auxiliary network to stabilize parameter updates both components.
The method of distinguishing stable and unstable parameters simply by separating backbone net- work and prediction head was naive. As shown in Figure during network training, certain layers in the backbone network exhibited greater instability compared to the prediction head, which could impair the backbone's 's feature processing capability. Furthermore, existing parameter update strate- gies applied identical weight coefficients layers with different stability levels, showing limited adaptability. To address these issues, we proposed Layer-decomposition Training (LDT), which further reduced feature corruption from unstable layers through finer-grained layer decomposition. We also introduced the Dynamic Parameter Update (DPU) strategy that adaptively adjusted update weights according to each layer's stability characteristics, achieving superior adaptability.
B THEORETICAL PROOFS
Following (Kumar et al. 2022 Pahk et al. 2025), for clarity of explanation, we simplify the network into two modules, S and U. where 5 denotes stable layers, which exhibit more stable parameter updates (lower gradient variance), and U denotes Unstable layers, which exhibit unstable parameter updates (higher gradient variance).
Theorem 1 A network comprises one stable layer S and one unstable layer U. At time step + 1 of network parameter updates, the gradient ÁS of stable layers S shows strong correlation with the gradient AU, of unstable layers U at time step L. This can be expressed as AS,411 = (AS, Z),,),, where AS, denotes the gradient of stable layers S at step f, and represents the input data.
Proof. At time step f 1, the network's prediction can be expressed as:
Do+ (S. AU. AU))..
(14)
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When the loss function is the L1 loss adopted in super-resolution tasks, i.e., Loss y y, where y denotes the ground truth label. At time step ++ the gradient of module S can expressed
OAS: ASH (1 as.
UAU, OAS, (U, AU)) + (S. A..)( OAS, as,
(15)
From [5 it can be observed that at time step t + 1, the gradient of module S is influenced by the gradient AU, of module U at time Consequently, the instability characteristies in unstable layers ultimately affect parameter updates in stable layers, thereby interfering with the overall network's predictive performance. Eq can be simplified A = f 2),
Assuming the loss function is the L2 loss commonly employed in high-level vision tasks, i.e., L0881.2 (y++1 y)², the gradient of module 5 at time step + can be expressed as:
ASH ++1 = 2[(S, AS,) AU)) 전 f AU,,),
(16)
where f (AS,, AU;, .) represents the gradient update magnitude time step under the loss.
From Eq. T6 it can similarly be observed that at time step + 1, the gradient update of module remains correlated with the gradient update of module U at time t.
When the loss function the cross-entropy employed for classification tasks, i.e., LossCross Flog(y), the gradient of module S at time step + can be expressed as:
(LS, AU,,) (S.
AS F+1 = -s - AS, - AU;))
(17)
From Eq. 17 can similarly be observed that in classification tasks, the gradient update of module at time step + 1 is also influenced by the gradient of module U at time t.
Through Eq. 15 16 and 17 it be observed that the gradient updates of unstable layers U significantly influence subsequent gradient updates of stable layers S parameters. Therefore. the gradient of stable layers S time step t + can be expressed AS f(As,, AU,, x), where AS; denotes the gradient of stable layers S at step f, and represents the input data.
Extension to Multi-Layer Network
X
u
m'
y
(a)
X
m
m
y
(b)
X
m n-1
m"
Figure 5: Network architectures under different scenarios. u denotes an unstable layer, while m represents an unspecified layer that could be cither stable unstable. and the network input and output, respectively.
Theorem 2 For network comprising one U layer and k unspecified layers {m'..., each m could be either stable or unstable, the gradients of all k unspecified layers m are influenced by the gradients of the unstable U layer, expressed by Amm-m² m² f (AU,...
,..., "e',...,''' m where
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Proof. (We proceed by mathematical induction on k.)
1. Base Case (k = 1):
As show in Figure 5a when the network consists of an unstable layer U and unspecified layer m', the gradient of m at time + 1, denoted as Am is influenced by the gradient of U at time AU,. (This relationship is analogous to Theorem 1, where the stable layer S is replaced by the unspecified layer m This relationship is expressed as Am = (AU...... It should be noted that, unlike in Theorem 1, the function f()) in this context represents relationship between two variables, with the former being influenced by the latter.
2. Inductive Hypothesis (k-
1):
As shown in Figure 5b assume that for k = n- 1, U forms a combined unit with n 1 unspecified layers (ml ... m The gradient of the combined unit m mm? ...m"- depends the gradient of U', satisfying A (mm*r) ......m=)) = ("U,)AU,...)
3. Inductive Step (k: n)):
As shown in Figure 5c for the case of k: = n, the network comprising one U layer and n unspecified layers (m- ....y m is constructed by adding m. layer to the architecture with t n 1. The network output at time t + 1 can be expressed as:
Mt++ (Um'm mm ... m A(Umm m² ... ...." mm-"))))(m Am"))
(18)
The gradient of m can be expressed as:
Ami =(- AA(Um-me mof...m" m-1
)mm Amm))
Om"
(19)
0Am7
+(Um m² ... m n-
AlUm m??...m"!))a
Omn )x
It can be abbreviated as:
Ami1 f= (emmmm....))))))) m. -,--"=². xm)
(20)
Given that A(m' m² mmmmmmm"m m²-)) = fr (AU, it follows that A(Um'm² (0mmm²..mm==)) mm=1) pUn-1 (AU,.... Substituting this into Eq. 20 we can further deduce that Am depends on AU. Consequently, the gradient of the combined unit m'm m'm? ... .......mmpppp m- also depends on the gradient of u, as expressed by A Amm-m²2 ...") ("UU,,...)
Conclusion:
For network comprising one U layer and & unspecified layers {m-,... ...., m²), we conclude that for any & 2 1, the gradients of all unspecified layers in such network are influenced by the gradients of the unstable layer U, as expressed by A(m-m² ...mm) f(AU...).
C THE SINGLE-BRANCE VERSION OF LDT (LDT-S)
C.I METHOD
The LDT method, which computes gradients for both the primary and auxiliary networks, involves notable computational overhead. To improve its efficiency, we introduce LDT-S, streamlined single-branch version. LDT-S achieves lower computational cost by alternately freezing parts of the single network across timesteps. maintaining the requisite isolation between stable and unstable layers while ensuring the stabilization the unstable ones.
As shown in Figure 6 the intermediate 2 x timesteps of LDT-S training This example consists of two stages: the unstable layers training stage and the stable layers training stage, with each stage spanning timesteps. The entire LDT-S training process employs only single network and one weight buffer. This buffer, located in the CPU, is used to store the network weights from the immediately preceding timestep only.
example.
The unstable layers training stage is initiated by loading the network weights from the preceding timestep stored in the weight buffer. To prevent the unstable layers from interfering with the training
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Unstable layers training stage
Stable layers training stage
XSS
XSS
Switch the training stages
Load wight
DPU
Load wight
DPU
Weight buffer
[|ui]
Trainable weight
Frozen weight
Stable layer
Unstable
layer
The overall framework of LDT-S. is the side-switching interval, determining the number of training timesteps allocated to each stage.
Figure 6:
of the stable layers, the parameters of the stable layers are frozen in this stage, and gradients computed and used to update only the unstable layers. During this stage, the unstable layer param- eters the network are used update their counterparts in the weight buffer via the DPU method, thereby stabilizing the unstable layer parameters stored in the weight buffer. After this stage ex- ecutes for 8s timesteps, a training stage switching occurs, transitioning the process into the stable layers training stage.
The stable layers training stage analogously begins by loading the weights from the previous timestep from the weight buffer and proceeds freeze the unstable layers. This stage utilizes the DPU method to update the stable layer parameters stored in the weight buffer, enabling them to adapt the newly stabilized unstable layers. After timesteps, training stage switching is executed again, transitioning back to the unstable layers training stage. Finally, upon the completion of network training, the final weights are retrieved from the weight buffer for inference and testing.
The pseudocode for the overall training procedure of LDTSS is provided in Algorithm 3
C.2 EXPERIMENTS ON PERFORMANCE AND EFFICIENCY
As shown in Table 6 owing its single-branch design, LDT-S exhibits superior computational ef- ficiency compared to LDT, with an efficiency nearly on par with the single-networg baseline model. Furthermore, LDT-S achieves competitive performance against the ble 7 while also attaining superior computational efficiency.
method DeFT (Ta-
Table 6: Ablation experiments on training/inference efficiency. The task is image super-resolution, with training and inference patch sizes set to 48 48 and 200 200 pixels respectively. The network architecture is based on MambalR.
Method
Training memory (GB)
Inference memory (GB)
Training time (s)
Inference time (s)
Baseline
15.27
2.7
0.6912
658.3287
DeFT
20.30
2.7
1.2566
653.9900
LDT-S
15.21
2.7
0.6920
649.5462
LDT
20.25
2.7
1.2608
643.6736
Table 7: Performance comparison
Method
Pan
Sony
DSC
IMG
Canon
Baseline
30.81/0.8666 868/0
30.81/0.8850
30.22/0.8753
30.01/0.8737
30.93/0.8617
DeFT
31.27/0.863.
31.61/0.8801
31.34/0.8875
30.31/0.8726
32.40/0.9247
LDT-S
31.30/0.860
31.46/0.877
$1.37/0.8878
30.37/0.8722
32.49/0.9228
LDT
31.36/0.8611
32.15/0.8880
31.51/0.8865
30.57/0.8705
32.80/0.9246
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D ADDITIONAL ABLATION EXPERIMENTS
D.1 ABLATION EXPERIMENTS ON LAYER PARTITIONING STRATEGIES
As shown in Table 8 we performed an ablation study on layer partitioning strategies, comparing static strategy, which maintains the initial layer assignment throughout training, with dynamic strategy that reassigns stable and unstable layers every 5,000 steps. The dynamic partitioning strat- egy achieves higher PSNR and SSIM scores than the static strategy across the Pan, DSC, and Canon data branches. This improvement stems from its ability to better adapt to the training process by dy- namically identifying stable and unstable layers based on evolving parameter states, demonstrating superior adaptability.
Table 8: Ablation experiments on layer partitioning strategies
Method
Pan
Sony
DSC
IMG
Canon
Baseline
30.81/0.8688
30.81/0. 8850
30.22/0.8753
30.01/0.8737
30.93/0.8617
Static 31.36/0.861
32.15/0.8880
31.51/0.8861
30.57/0.8703
32.80/0.9246
Dynamic
31.41/0.8636
31.85/0.8826
31.52/0.8892
30.49/0.8729
32.76/0.9250
D.2 ABLATION EXPERIMENTS ON DIFFERENT WEIGHT COFFFICIENT W PROJECTION METHODS
As shown in Table compared two strategies for generating weight coefficients W: di- rectly projects normalized variance into weight coefficients W, and the other generates W based on variance ranking. Generating weights directly from normalized variance requires careful design of the projection function to ensure stable performance. In contrast, the ranking-based method for setting weight coefficients is more straightforward. As visualized in Figure 7 present plot of the variance against the corresponding weight coefficients generated by the manking-based method.
Table 9: Ablation Study Different Weight Coefficient W Projection Methods
Method
Pan
Sony
DSC
IMG
Canon
Baseline
30.81/0,8850
30.22/0.8753
30.01/0.8737
30.93/0.8617
Normalized variance
31.09/0.8624 31.36/0.8611
31.01/0.8702
30.05/0.877
31.07/0.8855 30.57/0.8705
32.31/0.9227
Ours
32.15/0.8880
31.51/0.888 ...55
32.80/0.9246
Var
Ver
Figure 7: Correspondence between Weight Coefficients and Variance. (a) For stable layers. (b) For unstable layers.
D.3 ABLATION EXPERIMENTS ON DIFFERENT SOURCE DOMAIN SAMPLE DISTRIBUTIONS
As shown in Table 10 to validate the robustness of the LDT method across varying source domain sample distributions, individually designated the Olympus and Pan camera branches the source domain. Additionally, we conducted experiments with multiple source domains by forming pairwise combinations of out of the three data branches (Olympus, Pan and Sony) the source domain.
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Table 10: Ablation experiments on different source domain sample distributions. We employ the abbreviations O for Olympus, Pa for Pan, S for Sony, D for DSC, for IMG, and C for Canon. Olympus + FT, S + FT, and Pa + FT denote the performance of the network on the target-domain camera branches after naive fine-tuning on the Olympus, Sony, and Pan camera branches respec- tively, which we use as baselines.
Source
T1
T2
T3
T4
T5
Olympus+ FT
Pa:30.81A0.660
8:30887008830
D:300.220.8755
E310007008737
C:30.93008611
Olympus + LDT
Pa:31 .36/0.8611
S:32.15/0 8880
D:31.51/0.888 88655/
1:30.57/0.877 8705
C:32.800.99
Pa+ FT
Olympus: (0mppss:0.6655 6570.8596
S:31.59/0.883
D:31,5000.8900
L33004/0.8775
C:32.5000
Pa PT
Olympus: 30 65/0. 8569
S:31.76/0.888
D:31.57/0.
1:30.2000.800 8693
C:32.51/0.2338
Olympus FT
Pa:30.8 10.8888
8:30.8170.8850
D::00.220.8755
E30.0170.8337
C:30.93/088617
Olympus + Pu LDT
$:31.95/0.886
1131.63/0..915
1:30.38/0. 380.33/0.8737
C:32.92/0.9288
Olympus S LDT
Pa:31 Pa:314110.86644 41/0.8648
D:31.32/0.88 8875
1:30.41/0.88 8751
C:32.40/0.2222
S + FT
Olympas: 11.6900.852
Pa:31 72:31.2770.86211
D:3 1.3./008844
1:30.0000.772 8720
(:32..8/0.9222
Pa
Olympus:30 30.81/0.8874
D:31.55/0.887
1:30.35/0.870
C:32.65/0.9239
D.4 ABLATION EXPERIMENTS ON DIFFERENT NETWORK ARCHITECTURES
To validate the robustness of the LDT method across different network architectures, we conduct performance evaluations on three distinct frameworks: the CNN-based SAFMN (Sun et al., 2023), the Transformer-base HAT (Chen al., 2023), and the recently popular Mamba-based MambalR (Guo et al. (024a) networks. As demonstrated in Table [1] LDT achieves consistent generalization improvements across all architectures, with the most significant performance gain observed on the
Canon camera branch of MambalR network, where the PSNR metric improves by 1.61dB.
Table 11: Ablation experiments on different network architectures
Network
Pan
Sony
DSC
IMG
Canon
HAT [Chen ct al.
2023
$0.45/0.8448 31.43/0.8751
30.63/0.8725
29.99 29.99/0.859
31.86/0.9146
HDT+LDDT
$1.34/0.86.0
31.39/0.8771
31.47/0.8896
30.29/0.8713
32.32/0. 9208
SAFMN (Sun et al. SAFMN+LD
2023
30.46/0. 8449
31.44/0.8753
30.64/0.8729
29.99/0.8597
31.86/0.9148
11.03/0.8585
30,92/0.8712
31.06/0.8835
29.99/0.8727
31.88/0.9209
MambalR (uo al. Mambaik +LD1
2024a
30.81/0,8660
30.81/0.88
30.22/0.8753
30.01/0.8737
30,93/0. 86117
31.36/0.8611
32.15/0.8880
31.51/0.8865
30.57/0.8705
32.80/0.9246
D.5 PERFORMANCE COMPARISON ON DIFFERENT DATA DISTRIBUTIONS FOR IMAGE SUPER-RESOLUTION TASK
As shown in Table 12. to validate the robustness of the LDT method against varying data distribu- tions in the super-resolution task, we trained the MambalR network using the Pan data branch as the source domain and evaluated on B100, Mangal09, Set5, Sct14, and Urban100. The results demonstrate the strong generalization capability of the LDT method across different data distribu- tions.
Table 12: Performance Comparison on Different Data Distributions for Image Super-
Method B100
Manga
Set5
Set14
Urban
LPFT
24.13/0.6551
23.79/0.8135
13.19/0.7713
23.58/0.6735
21.77/0.6717
DeFT LDT
25.14/0.6885
24.53/0.8306
26.29/0.8001
24.94/0.7157
22.91/0.7113
25.38/0.6938
24.70/0.8467
26.44/0.8100
25.03/0.7214
23.03/0.7261
D.6 PERFORMANCE COMPARISON ON DIFFERENT DATA DISTRIBUTIONS FOR NLP TASK
As shown in Table S to verify LDT's effectiveness NLP tasks, we utilized the Amazon review dataset specifically designed for NLP domain generalization research. It contains four data branches: DVD, Kitchen, Electronics, and Books. We used the first three as source domains and the last as the target domain. LDT demonstrates clear performance improvements compared to the DeFT method.
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D.7 PERFORMANCE COMPARISON ON DIFFERENT DATA DISTRIBUTIONS FOR IMAGE CLASSIFICATION TASK
To verify the effectiveness of LDT high-level vision tasks, we evaluate LDT's performance image classification. As shown in Ta LDT demonstrates consistent performance improve- ments across different data branches. The most significant improvement occurs in the branch, where LDT achieves 5.19% accuracy gain over baseline methods when using ResNet- 18 as the backbone network.
D.8 PERFORMANCE COMPARISON ON DIFFERENT DATA DISTRIBUTIONS FOR SEMANTIC SEGMENTATION TASK
As shown Table 15 we evaluate the performance of the LDT method on semantic segmentation
tasks. LDT demonstrates consistent performance improvements across different data branches.
D.8.1 ABLATION EXPERIMENTS ON UNSTABLE LAYER PARTITIONING RATIOS
To investigate how the unstable layer partitioning ratio Ratio affects network generalization, sample values between 0.1 and 0.9 at intervals of 0.2 (with finer 0.1 intervals near the optimal ratio), using these sampled values the partitioning thresholds. As shown in Tables 16 and 17, the network achieves optimal generalization performance at selection ratios of 0.4 or 0.5 for image super-resolution, while ratio of 0.7 yields the best performance for the semantic segmentation task.
Table 13: DG for NLP tasks.
Method
Source Domain
Target Domain: Book
FT
0.8725
DeFT
DVD, Kitchen, Flectronics
0.8800
LDT
0.9000
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Table 14: DG for image classification.
Network
C
I.
S
Mean
Resnet18 FT
0,9929
0.7363
0.6341
0.7941
0.7894
Resnet18 + DeFT PPahk et al
2025
0.9965
0.7439
0.6433
0.8133
0.7992
Resnet18 + LD1
0.9965
0.7514
0,6860
0,8281
0.8155
Resnet50 +FT
0.9929
0.7345
0.6371
0.8148
0.7949
Resner50 + DeFT (Pahk
2025
0.9929
0.7345
0.6418
0.8222
0.7978
Resnet50 + LD1
1.0000
0.7684
0,7058
0.8415
0.8289
Vit +FT
0.9965
0.7797
0,6570
0.8207
0.8135
Vit DeFT PPaik al.
2025
0.9929
0.7589
0.6433
0.7807
0.7940
+ LDT
1.0000
0.7589
0.6951
0.8400
0.8235
Vision Mamba + FT
1.0000
0.7684
0,6600
0,7956
0.8060
Vision Mamba + DeFT (Pahk al.
2025
1.0000
0.7759
0.6768
0.8430
0.8239
Vision Mamba LDT
1.0000
0.7928
0.7043
0.8326
0.8324
Table 15: DG for semantic segmentation.
Method
Source domain
Target domain BDD100K: 3088881
Target domain
FT
Mapillary:34.99 Mapillary:48.35 38.25 Mapillary:51 1.6658 16688
DeFT Pahk et al.
2025
Cityscapes
BDDI :40::00400400
LDI
BDD100K:43,677
Table 16: Ablation experiments on stable/unstable layer partitioning criteria for Super-resolution
Ratio
Pan
Sony
DSC
IMG
Canon
0.1
30.73/0.8506
31.79/0.888
30.89/0.8777 8877
30.19/0.8633
8633
32.10 /0.9184
0.3
31.28/0.8558 85589
31.86/0.850
51.41/0.8855
30.36/0.868
32.44/0.9191
0.4
31.36/0. 8611
32.15/0.8880
31.51/0.8865
30.57/0.8705
32.80/0.9246
0.5
31.37/0.8633
31.69/0. 8805
31.43/0.8883
30.36/0.8724
32.54/0.9233
0.6
31.25/0.8632
31.32/0.8755
31.24/0.88% 8877
30.21/0.8731
32.40/0.9229
0.7
31.28/0. 86288
31.24/0.8744
11.26/0.8875
30.20/0.8735
32.44/0.9237
0.9
31.04/0.8625
31.00/0.8712
31.04/0.8858
29.95/0.8723
$2.07/0.9209
Table 17: Ablation Study on the Impact of Layer Partitioning Ratios for Semantic Segmentation
task
Ratio
Target Domain (mlOU)
Target Domain (mlOU)
0.1
38.155
43.852
0.2
38.452
42.797
0.3
42.587
50.547
0.4
44.851
53.075
0.5
44.766
53.647
0.6
43.284
53.687
0.7
45.420
55.733
0.8
44.445
54.448
0.9
45.386
55.444
E PSEUDOCODE OF OVERALL TRAINING PIPLINE
We formalize LDT's complete training procedure through pseudocode: Algorithm D presents the identification of stable and unstable layers, while Algorithm 2 details the gradient isolation and stabilization mechanism. Algorithm presents the overall training procedure of LDT-S.
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Input:
The source domain subset o contains input samples and ground-truth labels y, network M, ratio of unstable layers Ralio
Output: Initialized network M, name set of stable layers N Name name set of unstable layers Name";
// Warm-up up stage
M Frozen by name M, Backbone name) // Freeze the backbone network
for i +- 1 to Nwarm-up up do
y (M);
Loss = funcl. 7): // func is Ll loss for SR tasks, cross eooss-yytroyy or L2 loss for high-level tasks
Lossbbackward); // Update prediction head end
// Layer selection stage
M Unfreeze(M);;
Grad = Collect network gradients(M); // Collect gradients over all
samples.
for i +- to (e)(M) do
// Compute the gradient variance for each layer.
Var, Comput.var(Gradl
end
Name Top_N(Var, Ratio" .M); Name Name - Name" Name name set of all layers
return M, Name NameU;
Algorithm 1: Identification of stable and unstable layers
Input: Another source domain subset D contains input samples x and ground labels T. network M, name set of stable layers Name name set of unstable layers Name"; Output: Trained network MC;
for 4- 1 to Nirainng do
AM, AM = Copy(M):
PM = Frozen by name (PM, NM,eme")); // where PM = (PLS, PL
AM Frozenbby AM, Name // where AM {AT. ,T.U)
y (x)(
A AM(x);
Loss P Suncly y):
Loss funcly A T);
Loss P backward(); / Update unfrozen stable layers PL in the primary network.
Loss backward(); / Update unfrozen unstable layers AL in the auxiliary network.
10
// DPU
WS WU = Get Cicieeosseeiiiieaaaaira V // Refer to Eq. and 10.
11
Update EMA(PL" V
A "w"): Update frozen unstable layers PL in the primary network. Refer to Eq 11
12
Update_EMM AMA( PLS wS) Update frozen stable layers AL in the auxiliary network.
14 end
15 MC = CAL PL // Concatenate frozen stable layers from auxiliary network and unstable layers from primary network.
16 return MC:
Algorithm 2: Gradient isolation and stabilization
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Input: Another source domain subset D contains input samples and ground-truft labels y. network M, name set of stable layers Name name set of unstable layers N Name side-switching interval;
Output: Weight buffer W B:
// where M ( L"}, WB (WBLS WBL")
WB B Copy(M) / Weight buffer
Flag Stable;
M Frozen_by.name(MM, Name))
W W = Get pdate Coef ficientsvs or Var"); Refer to and 10
for i +-1 to Neraining do
y = -M(x);
Loss funcly, D);
boss.backward); // Update unfrozen layers.
Flag Stable then
U'puateEEAAWBBL' L //) Update weight buffer. end 11
else AMA (,,
14
end
// Switch the training sides.
15
1%ss = then
Moda_weiggttMM /B Load the weights from EM into M. Flag == Stable then
17
18
M = Unwfrozen.b.naaaeeee by_name Name>);
Flag Unstable;
M = www.eeeenb..ameee... MM Name");
29
21
end
else
M = MUnnfrozen.Jb /rr///ooeyyyaannnn Name.);
24
Flag = Stable;
M = Frozen.bynnmm((NM M, (M, Name");
26
end
27
end
28
end
return WB; B;
Algorithm 3: Gradient isolation and stabilization with single active network
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F VISUALIZATION RESULTS
As shown in Figure 8 we compared the proposed LDT with other domain generalization methods. LDT demonstrated superior detail restoration and noise suppression.
LR
ZSSR: 29.60
IODA: 29.90
SRTTA; 29.42
Navi: 30.02
K
ak
START 28.75
FT: 28.92
DeFT: 30.45
LDT: 30.76
Sony 53.png
LR
7SSR: 35.96
IODA: 34.29
SRTTA: 35.89
Navi: 35.07
GT
START: 33.02
LP-FT: 33.65
DeFT: 35.01
LDT: 35.97
Sony_53.pm 53.png
Figure 8: Visual comparison. The large image on the left is the LR image, and the sub-images on the right are LR, ZSSR (Shocher et al., 2018), IODA (Tang & Yang 2024p. SRTTA (Deng al. Wang et al. (Navi(ffss row), GT, START (Guo et al. 2024b). L.--T (Kumar et al., 2022 DeFT (Pahk et al. 2025), LDT (second row). The value following the name represents the PSNR metric of the current patch. Please zoom-in screen.
2023
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G STATEMENT ON LLM USAGE
We used a Large Language Model (LLM), specifically ChatGPT, solely for language polishing and improving the readability of the manuscript. The LLM was used to generate ideas, conduct experiments, analyze results, or contribute to the research methodology. All scientific content, in- cluding the conceptualization, design, implementation, and validation of the work, was entirely carried out by the authors.
27


Reviews:
[{"review_id": "NzrYQboLX6", "reviewer": "Reviewer_xH6P", "summary": "This paper attempts to mitigate the influence of unstable parameters on stable parameters for domain generalization. The authors proposed a fine-grained layer-wise partitioning strategy to improve parameter update stability, named Layer-Decomposition Training (LDT). LDT separates and cross-froze the stable and unstable layers during training, thereby mitigating the perturbations from unstable layers. Besides, the authors propose Dynamic Parameter Update (DPU) strategy to adjust the update coefficients of diverse layers according to their fluctuation amplitudes. Experiments on super-resolution, classification, and semantic segmentation demonstrate that LDT could enhance the generalization performance.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "[+] The paper is easy to follow. \\\n[+] The detail of the method and experiment is well described. \\\n[+] The related work is detailed.", "weaknesses": "Major weakness:\n\n[-] The explanations for the experimental results are insufficient. This is particularly evident in Figure 2, Table 4, and Appendix Figure 5.\n\n[-] The authors claim that \"the same parameter update coefficient would inevitably result in information loss and counterintuitive behavior.\" Please clarify what is meant by \"information loss\" and provide evidence to verify this phenomenon.\n\n[-] The number of benchmarks used across different tasks is limited. Expanding the benchmark set would help better validate the effectiveness of the proposed approach.\n\n[-] Regarding Table 2:\n(1) The performance differences among the methods—particularly between Mean, Var/Mean, and Var—are not significant. Please explain the possible reasons for this.\n(2) Why does the Var/Mean method, which is supposed to better reflect parameter fluctuations without magnitude interference, underperform compared to Var?\n\n[-] In Appendix Table 7, the average performance of ResNet50 + DeFT (0.7978) is lower than that of ResNet18 + DeFT (0.7992). Please explain this counterintuitive result.\n\n[-] The authors selected layer partitioning ratios of 0.4 or 0.5 for unstable layers. Is this ratio also optimal for other tasks, such as classification and semantic segmentation?\n\nMinor weakness:\n\n[-] Typographical errors should be corrected to ensure clarity. For instance, \"0.8746 pm 4.79...\" in Table 1 should be revised to \"0.8746 ± 4.79...\".", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "etRPiyCJoW", "reviewer": "Reviewer_GLbA", "summary": "This paper proposes Layer Decomposition Training (LDT) for domain generalization by detecting “unstable” layers identified via high gradient variance and separating their influence from “stable” layers. LDT trains two parallel copies of the network with cross-freezing and an EMA pathway to prevent interference, and introduces a Dynamic Parameter Update that adapts each frozen layer’s EMA coefficient according to its variance rank. The method is architecture and task agnostic, delivering gains on super-resolution, classification, and segmentation without any added inference overhead.", "questions": "- Can you extend the evaluation to larger-scale and non-vision settings (e.g., ImageNet classification and an NLP benchmark such as WikiText-103), and outline any adaptations needed to apply LDT in these regimes?\n- Can you expand the comparisons with other domain generalization methods on datasets beyond DRealSR benchmark?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 1, "strengths": "- The paper methodology of using the Top-n layers with highest gradient variance as the unstable layer is easy to follow and easy to implement in practice.", "weaknesses": "- The first concern is novelty. Dual-branch training already exists [1]; the difference here is a layer-wise variant that selects “unstable” layers via a top-n gradient-variance threshold, which feels ad-hoc.\n- The experimental evidence is limited to medium-scale vision benchmarks, with no large-scale tasks (e.g., ImageNet classification), making it hard to assess scalability and practical impact.\n- The method is presented as task-agnostic, yet there are no natural-language experiments (e.g., WikiText-103 language modeling), which would help demonstrate broader applicability.\n- Reported gains over DeFT [1] are modest (e.g., Table 4), suggesting limited improvement relative to prior dual-branch approaches.\n- Sections 2.2.3 and 2.4 are unnecessarily long relative to their content, which impairs readability. Greater emphasis should be placed on more baseline comparisons against other domain generalization methods.\n\nReferences:\n\n[1] Jaehyun Pahk, Donghyeon Kwon, Seong Joon Oh, and Suha Kwak. Decoupled finetuning for domain generalizable semantic segmentation. ICLR, 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KKVJtXRRVD", "reviewer": "Reviewer_e1yc", "summary": "This paper presents Layer Decomposition Training (LDT), a new domain generalization framework that improves network robustness by performing fine grained layer- wise separation of stable and unstable parameters. Existing works such as LP-FT and DeFT only distinguish between backbone and prediction head, which the authors argue is too coarse and leads to misclassification of unstable parameters.\n LDT first identifies unstable layers by measuring gradient variance across training samples and then adopts a dual branch cross freezing mechanism to isolate gradients between stable and unstable layers. To further enhance adaptability, a Dynamic Parameter Update (DPU) strategy is introduced, which adaptively adjusts EMA coefficients per layer based on gradient fluctuation magnitude.\n Extensive experiments are conducted across multiple domains including super resolution, classification, and semantic segmentation and across architectures such as CNNs, Transformers, and Mamba models. Results show consistent generalization improvements over state-of-the-art methods like DeFT and START.", "questions": "DPU assigns update coefficients based on sorted variance rankings. Would a continuous mapping (e.g., normalization based scaling instead of discrete ranking) further improve stability? and could the authors explore a single network variant that integrates DPU like adaptation without duplicating the model? This might broaden applicability to resource constrained environments.\n\nIn today's era of large models, can this method be applied to large model architectures?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "The idea of layer wise decomposition of parameters guided by gradient variance is both conceptually novel and practically meaningful. Prior works (LP-FT, DeFT) only perform module level partitioning, whereas this work introduces a more granular and data driven approach. The Dynamic Parameter Update (DPU) is an innovative extension to EMA based stabilization, turning the update coefficient into a layer adaptive parameter, which enhances stability and learning efficiency. Theoretical motivation is well supported: the authors provide a gradient correlation analysis between stable and unstable layers and formal proofs (Appendix B) showing how unstable gradients propagate perturbations.", "weaknesses": "Although the paper provides a theoretical derivation of gradient interference, the proof is limited to a simplified two module case (stable vs. unstable). Extending this to multi layer interactions or real architectures would make the analysis more convincing. The dual network training doubles the model instances during training, increasing memory from 15.27 GB to 20.25 GB (Table 3). Although inference cost is unaffected, a deeper analysis on scalability to larger networks (e.g., ViT-L or Swin-L) is missing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uOrUhHTEQ8", "reviewer": "Reviewer_cWBr", "summary": "This paper proposes a new training strategy, LDT (Layer-Decomposition Training), to improve domain generalization performance. It critiques existing methods for their coarse-grained partitioning of networks into 'stable' backbones and 'unstable' heads. LDT, in contrast, identifies stable/unstable layers at a fine-grained, individual layer level based on gradient variance. LDT uses a dual-branch (primary/auxiliary) network structure with a cross-freezing mechanism to isolate gradient interference from unstable layers, preventing it from disrupting the updates of stable layers. Furthermore, it introduces the DPU (Dynamic Parameter Update) strategy. Instead of using a fixed coefficient for parameter updates (EMA), DPU dynamically adjusts the update coefficient for each layer based on its variance level. This allows low-variance (stable) layers to learn more efficiently and high-variance (unstable) layers to be stabilized more effectively. The authors demonstrate that LDT achieves superior generalization performance compared to existing methods across diverse vision tasks (e.g., Super-Resolution, Classification, Semantic Segmentation) and various architectures (CNN, Transformer, Mamba).", "questions": "1. The DPU update coefficients (Eq. 10) are determined by a linear mapping based on 'rank' rather than the variance values themselves. This method (e.g., 0.99 + 0.01 * Rank) appears somewhat empirical. I am curious if you considered other functions that directly utilize the actual variance values (e.g. proportional to the normalized variance), and if so, why the current rank-based approach was found to be optimal.\n2. The increased training cost from the dual-branch setup could be prohibitive for larger models. Are there potential ways to reduce this cost while retaining the benefits of LDT, such as parameter sharing between the two networks or knowledge distillation?\n3. The stable/unstable status of the layers is fixed throughout the entire training process. If a dynamic LDT were applied, for instance, by re-measuring the gradient variance and updating the partitions periodically, could this lead to further performance improvements? Or, conversely, would it risk harming training stability?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Intuitive Problem Definition: The paper clearly points out the problem with existing methods (like DeFT) that simply separate the 'backbone' and 'head'. Fig 2(a) visually demonstrates that some backbone layers are even more unstable than the head, strongly justifying the core idea that a fine-grained, 'layer-wise' separation based on 'gradient variance' is necessary.\n2. Excellent Versatility (Task- & Architecture-Agnostic): The proposed methodology was successfully applied not only to low-level vision tasks (Super-Resolution) but also to high-level vision tasks (Classification, Segmentation). Furthermore, it demonstrated consistent performance improvements across diverse models, including CNN, Transformer, and the recent Mamba architecture, proving that LDT is a robust generalization 'strategy' not limited to specific conditions.\n3. Robust Experimental Validation: The ablation studies (Tables 1 and 2) are very well-designed, clearly isolating and verifying the effectiveness of LDT's core component (gradient variance-based partitioning) and the additional contribution of DPU (LDT+DPU > LDT > Baseline). This demonstrates that each proposed component makes a tangible and significant contribution.", "weaknesses": "1. Increased Training Cost: LDT uses a dual-branch architecture, which significantly increases training memory (approx. 1.3x based on Table 3) and training time (approx. 1.8x) compared to the baseline. This could be a practical barrier for training large-scale models.\n2. Sensitivity to Key Hyperparameter: The method introduces a new key hyperparameter, the 'unstable layer ratio ($Ratio^U$)'. According to Table 9 in the appendix, performance is sensitive to this ratio (optimal around 0.4-0.5), requiring careful tuning when applying it to new tasks or datasets.\n3. Fixedness of Layer Selection: The distinction between stable and unstable layers is determined only once—immediately after the warm-up stage—and remains fixed throughout the entire training process. However, as training progresses, the stability of certain layers may change dynamically.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0uZR5H6PFl", "reviewer": "Reviewer_kBcr", "summary": "The paper proposes Layer-Decomposition Training (LDT), a fine-grained, layer-wise method to improve domain generalization by explicitly separating “stable” and “unstable” layers based on gradient variance measured after a warm-up. Training uses two copies of the model with cross-freezing (stable layers train in the primary network; unstable layers train in the auxiliary network), while frozen counterparts are updated via EMA. A Dynamic Parameter Update (DPU) strategy adapts EMA coefficients per layer according to each layer’s relative variance rank. Experiments span super-resolution (DRealSR across cameras), classification (VLCS), and semantic segmentation (Cityscapes/BDD/Mapillary), and multiple backbones (CNN/Transformer/Mamba). Reported gains are strongest on SR, with modest training-time overhead and identical inference memory to baseline.", "questions": "1. How exactly is gradient variance computed per layer? Is it over per-parameter gradients aggregated by mean/std? Over mini-batches or single examples? Any normalization by parameter scale?\n\n2. DPU coefficients. Did any layers receive W=1? If so, are those layers effectively frozen? Could you share the distribution of learned W across layers and runs?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Clear problem framing**: The paper targets a concrete weakness of coarse parameter partitioning (backbone vs. head) in prior work (LP-FT, DeFT) and motivates finer granularity using measured layer-wise gradient variance. The dual-branch cross-freezing setup is sensible and well-aligned with the goal of mitigating unstable-to-stable gradient interference.\n2. **Method simplicity & generality**: LDT and DPU are architecture-agnostic and slot into standard fine-tuning loops with minimal code complexity (once gradients are collected). Conceptually straightforward; likely portable to many vision backbones.\n3. **Empirical signal on SR**: On DRealSR, LDT and especially LDT+DPU improve PSNR across several target domains; the Canon branch shows a large jump ($\\approx$ +1.9 dB over baseline). The ablations comparing variance vs. mean vs. random partitioning support the central design choice.", "weaknesses": "1. Metric inconsistencies: On SR, SSIM sometimes decreases even when PSNR increases (e.g., Pan and IMG branches in Table 1). The paper does not analyze why nor whether perceptual quality suffers. This matters for claims of “superior generalization capability.”\n\n2. Hyperparameter sensitivity: The ratio of unstable layers (Ratio_U) is central but lacks guidance: how is it chosen, how sensitive are results, and does it vary by architecture/task? The normalized-variance vs variance choice is argued for simplicity, yet Table 2 shows Var/Mean sometimes wins; the trade-off merits deeper discussion.\n\n3. Writing/Presentation issues：Several language glitches (e.g., “pm” instead of “±”), accidental filename text (“panasonic 132.png”), and inconsistent naming (“Layer-Decomposition” vs. “Layer-decoupled” in Appendix) distract from the contribution and could impede reproducibility.\n\n4. Novelty & Relation to Prior Work: Relative to LP-FT/DeFT, the novelty is finer-grained, data-driven layer partitioning and ranked EMA coefficients. This is incremental but non-trivial: the idea that some backbone layers can be less stable than head layers is interesting and empirically supported. Comparisons to other stability-oriented training schemes (e.g., sharpness- or noise-based regularizers) would situate the contribution better.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)