TS-DDAE: A Novel Temporal-Spectral Denoising Diffusion AutoEncoder for Wireless Signal Recognition Model Pre-training
Authors: Yaoqi Liu
Jin Wang
Hui Wang
Chuan Shi
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion
Wireless Signal Recognition
Pre-training
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Abstract: Wireless Signal Recognition (WSR) aims to identify the property of received signals using Artificial Intelligence (AI) without any prior knowledge, which has been widely used in civil and military radios. The current AI trend of pre-training and fine-tuning has shown great performance, and the existing pre-trained WSR models also achieve impressive results. However, they either apply the ``mask-reconstruction" pre-training strategy, which may disrupt intricate local dependencies of signals, or overlook latent spectral characteristics. Therefore, in this paper, we follow the diffusion models and propose a pre-training framework for WSR, named the Temporal-Spectral Denoising Diffusion AutoEncoder (TS-DDAE), which learns signal representations by corrupting signals with temporal and spectral noise, and then reconstructing the original data with a learned neural network. Moreover, we design a novel neural architecture, named TS-Net, which couples self-attention for temporal encoder with channel attention for spectral encoder. Extensive experiments on several datasets and WSR tasks show that TS-DDAE achieves superior performance compared to state-of-the-art (SOTA) baselines, which demonstrate the potential to be a foundation model for WSR. Code is available at https://github.com/BUPT-GAMMA/FoundWSR.

Published as a conference paper at ICLR 2026
TS-DDAE: A NOVEL TEMPORAL-SPECTRAL DENOIS- ING DIFFUSION AUTOENCODER FOR WIRELESS SIG- NAL RECOGNITION MODEL PRE-TRAINING
Yaoqi Liu Jin Wang", Hui Wang", Chuan Shi
Beijing University of Posts and Telecommunications! Peng Cheng Laboratory²
proggiluubbuge edu. en, waw..sss..nngoopp.....oooo shichuan&bupt. enu.on
ABSTRACT
Wireless Signal Recognition (WSR) aims identify the property of received sig- nals using Artificial Intelligence (AT) without any prior knowledge, which has been widely used in civil and military radios. The current AI trend of pre-training and fine-tuning has shown great performance, and the existing pre-trained WSR models also achieve impressive results. However, they either apply the "mask- reconstruction" pre-training strategy, which may disrupt intricate local depen- dencies of signals, or overlook latent spectral characteristics. Therefore, in this paper, we follow the diffusion models and propose a pre-training framework for WSR, named the Temporal-Spectral Denoising Diffusion AutoEncoder (TS- DDAE), which learns signal representations by corrupting signals with temporal and spectral noise, and then reconstructing the original data with a learned neu- ral network. Moreover, which couples self-attention for temporal encoder with channel attention for spec- tral encoder. Extensive experiments several datasets and WSR tasks show that TS-DDAE achieves superior performance compared to state-of-the-ar (SOTA) baselines, which demonstrate the potential be foundation model for WSR. Code is available at /tttps://qithub: com BUP T GAMMAAGMMAFOOOAO FoundWsR GAMNA/FoundiSR
design novel neural architecture, named TS-Net,
INTRODUCTION
Various signals are generated and play key roles in our daily communication activities Vinciarelli et al. (2008) As the scale of signal data continues expand, intelligent communication technol- ogy Huang et al. (2018) that uses artificial intelligence (AI) Wang et al. (2020) for signal analysis is gradually becoming one of the most important characteristics of the sixth-generation (6G) network due to its high efficiency and accuracy Alsharif et al. 2020). Wireless signal recognition (WSR) Li al. 2219)) is to identify the basic property without any prior knowledge of the signals, such as its modulation type Meng et al. (2018) and wireless technology Bitar et al. (011)). which requires experts to capture subtle features the time and the spectral domains. Once prior knowledge of the signals is known, the receiver can choose the corresponding demodulator, and later processing steps run faster and more reliably Eldemerdash et al. (2016). WSR now routine civil and military radios and is basic building block of intelligent communication systems Li et al. (2019).
Currently, deep learning models for WSR such as IQFormer Shao et al. (2024) have delivered com- petitive accuracy on individual benchmark, while they are difficult generalize to multiple tasks. The pre-training and fine-tuning paradigm Han et al. (202I) has proven that generic representations can be fine-tuned to wide range of downstream objectives with minimal effort. However, wireless signals have not yet enjoyed the same benefit. The few existing models like SpectrumFM /Zhou eet al. (2025) choose the mnans-reconstrucction" strategy Devlin et al. (2019) by setting the ampli- tude of part of the signal to 0 and restoring the original one, which risks scrambling the delicate temporal-spectre structure of waveforms Yang al. (2023). Moreover, these methods usually fo-
"Corresponding author

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Published as a conference paper at ICLR 2026
exclusively on time-series, ignoring the rich information embedded in the spectral domain. A robust pre-training framework that respects the inherent temporal and spectral nature of wireless signals remains an open challenge.
To retain the original data information rather than directly climinating it, we refer to the "noise- reconstruction" strategy proposed by diffusion models Ho al. (2020), which is adding random Gaussian noise to the clean data and restoring it as the pre-training objective, and propose our frame- work, named the Temporal Spectral Denoising Diffusion AutoEncoder (TS-DDAE) for WSR pre- training. The pre-training strategy used by the TS-DDAE can maintain the temporal-spectral struc- ture of the original waveform compared to the "mask- reconstruction" strategy, and the TS-DDAE applies neural network to reconstruct the signal from both temporal and spectral perspectives. Following the diffusion paradigm, the TS-DDAE contains a forward Markov process that incremen- tally corrupts the data and a learned backward process that iteratively restores it. During the forward phase, we add random noise to both the time and the spectral domains to form noisy signals. In the backward phase, we design novel neural network architecture named TS-Net to reconstruct the original data. The TS-Net can be divided into the temporal encoder and the spectral encoder, where we apply the self-attention Hu (219) for the temporal encoder and the channel attention Guo jet al.] (2022) for the spectral encoder. The encoders interact with each other to jointly learn the temporal and spectral characteristics of the signal data. Pre-trained and fine-tuned on various signal datasets and WSR tasks have achieved state-of-the-art (SOTA) performance, with 1.32% improve- ments on average over the best bascline, and about 8.75% improvements on average compared to IQFormer Shao et al. (2024), the SOTA Automatic Modulation Classification (AMC) model. Sup- plementary and ablation experiments also demonstrate the effectiveness of our designed model.
We summarize our contributions as follows.
To the best of our knowledge, models for WSR. We formulate the TS-DDAE, which contains a principled self-supervised learning objective from both temporal and spectral perspectives.
the first to adapt the diffusion theory to pre-train
We design a novel neural network architecture named TS-Net that jointly refines temporal sequence and spectral structure, allowing each encoder to inform the other and extract a richer, complementary representation of signals.
Related experiments demonstrate the effectiveness of our solution with most of the SOTA performance compared to 11 baseline models. Furthermore, we provide a code repository in supplementary materials that integrates signal data processing and various WSR models to facilitate user-replicated benchmarks and the design of their own solutions.
2 RELATED WORK
2.1 DENOISING DIFFUSION MODELS
In the past five years, Denoising Diffusion Models (DDM) Yang et al. (2023) have rapidly be- come a cornerstone of modern generative modeling and have demonstrated unprecedented success computer-vision (CV) tasks such high-definition image generation Dong et al. (2021), text-to- image generation Li et al. (202)), etc. The most typical DDPM Ho et al. (2020) has established the "noise-and-denoiss paradigm, which is add noise to the data in the forward process, and restore it step by step in the backward process to train the generative abilities of a neural network such as U-Net |Ronneberger et al. (2015). The DDIM Song al.) (20TT) further expands the solution space based on the DDPM and sets the variance as a hyperparameter. Beyond image generation, recent studies show that DDM can also serve as powerful self-supervised pre-training objective. The DDAE Xiang et | (2023) takes the mroise-and-denoise paradigm its pre-training task, which has also achieved a superior image classification Sanghvi et al. (2020) performance.
In the area of signals, RF-Diffusion Chi et al. (2024) has also adapted the diffusion theory to con- ditional signal generation. However, our goal is not to generate signals but to learn robust repre- sentations for WSR without any conditional assumptions. Therefore, depart from RF-Diffusion and instead build upon the theory of the DDAE and propose an unconditional diffusion based pre- training pipeline tailored wireless signals.
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2.2 WIRELESS SIGNAL RECOGNITION MODELS
Traditional signal analysis methods usually rely hand-crafted statistics for tasks such as AMC, but crafting these features is labor intensive Dobre et al. (007)) Therefore, CNN and Transformer] al. (2021) Vaswani et al. (2017) based deep learning models have emerged to replace most manual feature extraction, such as AMC Net Zhang et al. (2023), MSNet Zhang et al. (202I). etc. Recent studies, such IQFormer Shao al. (2024), jointly model raw signal samples and their spectro- grams, achieving excellent performance in AMC task.
Self-supervisel pre-training strategies for wireless signals can be grouped into five categories: deep clustering methods, contrastive methods, reconstruction-basel methods, generative methods, and predictive methods Milosheski et al. (2025). Among them, SpectrumFM Zhou et (2025) borrows the idea of lassked-llnguage-mmdeee
from NLP, randomly masks signals, and reconstructs them build a foundation model that supports multiple WSR tasks. In contrast, we corrupt the original signals by adding noise to both the time and the spectral domains, and train the neural network to denoise the corrupted signals. As corruption additive rather than masking. the input does not lose too much content, so the pre-text task retains finer- amer-grained information while still requiring the model learn semantically rich, transferable representations.
METHODOLOGY
3.1
OVERVIEW
X
temporal
&
Amp Phase
Noise 1)
g
Noise &t
-
Xt
Forward Process
Noise E, 171
Noise Ec. 7e
Noise Er,
X
Backward Process
signal reconstruction
TS-Net encoder
pretrain decoder
temporal encoder
temporal encoder
Loss
downstream task
X
classifier
spectral
spectral encoder
encoder
Zt
AMC
WTC
Figure 1: The overview of the TS-DDAE framework.
The real-world received signal is usually represented as IQ data, where and Q are the in-phase and quadrature components, respectively, which is often represented using complex data, i.e. x + jQjj (-1)) However. we believe that the two components are related and should be considered simultaneously when analyzing signals. Moreover, the real data calculations are usually more efficient than the complex data. Therefore. we represent the IQ data of length L as a two-row matrix E 2 where the two represent the and Q components, respectively. However, users can only obtain limited information through time series. Most signal analysis methods convert IQ data into spectral data by applying the Fourier transformation 3(x) E R2XL which describes

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the distribution of the signal amplitude across frequencies. The spectrum of signal data can better reflect the essential characteristics the signal.
Both the time series and spectrum of IQ data contain strong local dependencies, pre-train a WSR model using
will seriously damage the original information. Therefore, we take the "noise-reconssruution"" strategy from the diffusion models and propose novel diffusion theory named Semporal-Spectra Denoising Diffusion AutoEncoder (TS-DDAE), which is shown in Figure [ During the forward process, corrupt both the time and the spectral domains of the IQ data by adding the corresponding Gaussian noise, which can perturb the data while preserving its original characteristics. To address the two potential distortions, the reverse learning process needs to restore not only the temporal characteristics of the original data, but also the spectral char- acteristics. Therefore, we specially design a hybrid Sepeoral neural Network (TS-Net), which uses temporal encoders and spectral encoders to jointly learn the feature representations of the signal in the time and the spectral domains. reconstruct the original IQ data from the noisy one. At the fine-tuning stage, we optimize the model with task-specific data to adapt to downstream tasks like AMC, WTC, etc. Detailed TS-DDAE and TS-Net introduced in Section 3.2 and Section 3.3 respectively.
the pre-training stage, the TS-DDAE learns to
3.2
TEMPORAL-SPECTRAL DENOISING DIFFUSION MODEL
Forward Process. Given IQ signal data E R2xL, the forward process gradually corrupts the temporal and spectral information of signals by adding I steps of Gaussian noise. The results of each step can be denoted as Z1, I2, ".,, Zy. To corrupt the spectrum, we need to apply the Fourier transformation 2g (20o), and formulate the spectral noise addition process as
24 = (==((((++1)) + (fr/o) 5,, where ii + (ft/o)² = 1, and 8, ~ ~(, Then, we add noise to the time domain,
Tt ā.3 (zx) Bre
(2)
where a₂ B = and E, NO0 I), which is standard Gaussian noise. By combining Equation and 2
we can formulate the noise addition from step & to t as
Jy = ā,
(fr/o) 8.) B,e, ã + ā. - +Bre. (3)
The inverse Fourier transformation of a Gaussian function is still a Gaussian function. Therefore, we denote 7/2 = 3 (5,) (O, T), which we require be standard Gaussian noise. Furthermore, we denote a₂ = aifi, ?t âi ft and simplify Be as 3t. The Equation can be rewritten as
a + B1E, + N07
where the coefficients satisfy of + 3 + 3 1. By iterating the Equation 4 recursively and incorporating the reparameterization trick Kingma et al. (2015), can describe the relationship between the IQ data zo and noisy data 2; as
2t = 2. + 3,E, + 707
(5)
where āg = -i' ai 3r El ã. a. Therefore, we can directly obtain the noisy data from the original IQ data given any step t.
9.) = Ei=r(a [ (3: Er, Th ~ (0 I), and at + 33 + 7
Backward Process. After we get the noisy data, try restore it back to the original IQ data. Here, we describe this process using the Bayesian theorem. According to the superposition of the Gaussian distribution, Equation can be rewritten as a Gaussian distribution conditioned on Zo.
p(æ ~ Nxxx 2, 0 (3) + +
Since the added noise from each step does not affect each other during the forward process, it can be approximated Markov process, i.e. p 20) ss p( ~ N(1rg (88
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li ). Therefore, can get the probability function p( 1 Tul for the backward process with the Bayes' theorem. Detailed formulation is shown in Appendix A However, p(zt-1 zp) is conditioned Zo. which is unseen during the backward process. We can apply neural network (NN) to fit Zg. Suppose that we use the NN ja(;;) to estimate To based on St, the loss function be defined ||000 ((x))]]- Following the Equation [ the NN be formulated as (a(x))
(It Sise, (It, t) Tolle (Te, t)). Then, our final loss function is
32 [(e - so (Ie, ()) + A(1 - -,,,,,, (x,,
(x,, 1)
where À = 7 can be seen as a hyperparameter, which is the ratio between the spectral noise and temporal noise intensity the noisy data, and 01, 0g are learnable parameters. During pre-training, we need to sample IQ data from the dataset, a diffusion step i, and two noise e from a standard Gaussian distribution.
Although, formally, Fast Fourier Transformation (FFT) is a unitary transform, the two noises are sampled separately from the Gaussian distribution and are not completely identical, thus forming two optimization objectives for joint optimization. Future work could explore alternative noise addition schemes better suited to the characteristics of wireless signals.
3.3
MODEL ARCHITECTURE
self-attention module
convolution
FFD
Linear
Linear
1D
Nomm
1D
1D
Batth
1D Conv
Linear
deeut
temporal
temporal
1D
Zt
1D
spectral encoder
spectral
n02
local representation
channel-attention module
FCN
x
1D
Batch
10 conv
10 conv
Barch
1D corv
BatzhNom
Lingarr
Linear
1D conv
BatchNorm
BatchNom
1D conv
conv
Figure The model architecture of the TS-Net encoder.
To jointly capture temporal structure and spectral signatures, we introduce TS-Net, a diffusion- inspired architecture that performs hybrid analysis in both domains. The detailed TS-Net architec- ture is shown in Figure 2 The TS-Net comprises two tightly coupled encoders based on Equation The temporal encoder learns to denoise raw IQ samples x; by predicting the temporal noise Egi- while the spectral encoder operates the spectral counterpart 2, 3(xz) to estimate the spectral noise 7 Before feeding into each encoder, a ID convolution layer (denoted as Conv ID) with ker- nel size k projects the respective input into C-dimensional embedding space, simulating the initial processing of complex-vale valued IQ data.
X cone Conv (DX)).X E Bx2XL
where is the batch size the data, and the default kernel size of Conv ID is 1. At the same time, the diffusion step also encoded with sinusoidal position embeddings from Transformer.

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input the IQ sequence into the temporal encoder. However, this input does not contain the spectral information, so we also feed the spectrum data into this encoder and fuse these inputs with the encoded step t, ic. X = X X + Z +t. The same is true for the spectral encoder. The following is a detailed description of the temporal encoder and the spectral encoder.
Then, following the theory outlined in Section 3.2
Temporal Encoder The time sequence IQ data is similar to traditional data like text. Therefore, borrow the idea from Transformer, and apply the self-attention mechanism and residual paths throughout. Concretely, each block first expands the features through a feed-forward network (FFN) that injects non- con-linearity with the Gaussian-erro linear unit (GELU) function, then lets a multi-head self-attention layer (MultiHead) aggregate long-range temporal dependencies. Formally,
X out X feal + MultiHead W X feat W X Xeeat W. Xleat).
(9)
where X/eeat Xonve + FFN cono)) and Wº, WK WV E Rexx Ch = c/ H denotes the dimension of each attention head, and H is the number of head. Inspired by SpectrumFM, next refine the feature map X out and timestep embedding through a ID point-wise convolution followed by Gated Linear Unit (GLU). A ID depth-wist wiit convolution with kernel size 3 then harvests local signal structure, after which a second ID convolution compresses the result into the final local representations.
Xlocal_ X + ConvID(BN(Com) ID (CGLLU(Conv) (XX + l), kernel 3))),
(10)
where BN represents the BatchNorm. Finally, we use another FFN to combine the results above and output the temporal embeddings. i.c. X Xfinal Xloeal FFN X toeal).
Spectral Encoder The spectrum of IQ data typically has high amplitudes at certain frequencies, while other frequencies have very low amplitudes. This means that using the self-attention mech- anism is not suitable for capturing sequence dependency information in the spectrum of IQ data. Therefore, we use the convolution layers to extract the local features and use the channel attention to obtain the key feature dimensions. Moreover, we also apply residual paths throughout the encoder. Specifically, we learn from the IQFormer and first encode the spectral dimension with a lightweight ID depth-wiee wise convolution followed by 1D point-wiss wise convolution, which is formulated
glocal Z cone Conv DD ID(BN(ConV)) (0foonniI((22 ID omm kernel 3)))).
(11)
Next, we highlight the most informative channels through a channel attention module: the feature map is globally pooled, refined by FFN network, and the resulting attention scores multiplied with the original features. This re-weighting suppresses irrelevant bands and amplifies discrimina- tive ones, yielding a more compact and representative spectral signature for downstream learning. We formulate the channel attention as
Z feat FFN(Pool Zlocal)) Zlocal
(12)
Finally, we feed the feature map into the feedforward convolution network (FCN) that contains two point-wise convolution layers for spectral embeddings, i.e. //inal Zfeat + FCN Z feat).
During pre-training, the two embeddings are optimized by minimizing Equation 7 At the fine- tuning stage, we first apply global average pooling to each embedding, concatenate the resulting vectors into single representation, and feed it to classifier for the WSR task. The entire network fine-tuned with standard cross-entropy loss.
EXPERIMENTS
In this section, we evaluate the performance of TS-DDAE on several datasets and WSR tasks. Specifically, we design series of experiments to address the following questions. Q1: How ef- fective is TS-DDAE in solving multiple WSR tasks? Q2: What is the quality of representation obtained by TS-DDAE pre-training? Q3: How do the temporal encoder and the spectral encoder contribute the overall performance?

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4.1 EXPERIMENTAL SETTINGS
The TS-DDAE is pre-trained and fine-tuned NVIDIA Tesla A100 GPUs, using PyToorch Ansel et al. (2024) for implementation and Optuna Akiba et al. (2019) for hyperparameter optimization. The implementation details are listed in Appendix [C] The hyperparameters used for TS-DDAE listed in Appendix [ To evaluate the performance of TS-DDAE, we implement 11 models as our baseline models, which can be categorized as deep learning models, SOTA WSR models, and WSR foundation model. Deep learning models contain ResNet Liu et al. 2017). MCNet Huynh- (2018), GRU2 CGDNN Njoku et al. (2021). Transformer Vaswani et al. (201)) MSNet Zhang et al. 2021). SOTA
The (2020). VGG
al. (2018), CNN2
(2017).
WSR models are Aet Zhang al. (2023) and IQFormer Shao et al. (2024). The WSR foun-
dation model is SpectrumFM Zhou et al.
2025
4.2 MAIN RESULTS (Q1)
In this part, we present the overall results of the TS-DDAE, which demonstrates the ability of our pre-trained TS-DDAE model to handle multiple WSR tasks. The Automatic Modulation Classifica- tion (AMC) and Wireless Technology Classification (WTC) results are presented in Table where the model followed by the "probe" "k-shot" is represented as the results of lincar probe and few- shot learning described in Section 4.3 Moreover, we plot the performance across various SNRs of the AMC and WTC tasks in Figure
The analysis and results of the Anomaly Detection (AD) task on dataset ICARUS Roy et al. (2023 are presented in Appendix E.I
Table I: The overall results of different models (percentage), where the "Average" represents the average performance and the "Best" represents the best performance among all SNRs datasets (The best performance is represented in bold, and the second-bes, performance is underlined).
RML2016 10A
RML2016.
RML2208
TechRec
Model
Average
Best
Average
Best
Average
Best
Average
Best
ResNet
48.25
79.36
63.87
92.98
43.42
76.94
66.29
80.00
MCNet
60.37
90.86
57.18
87.43
52.70
89.22
67.12
85.68
VGG
39.99
62.18
38.11
56.63
42.53
66.73
79.83
94.07
CNN2
54.18
80.68
54.70
79.24
41.67
61.16
60.52
71.38
GRU2
60.39
90.86
63.93
93.34
52.57
82.44
37.80
39.09
CGDNN
51.62
76.59
47.90
70.68
35.34
52.56
49.27
58.15
Transformer
54.77
82.36
61.17
90.42
58.22
93.18
71.77
79.74
MSNet
58.94
88.59
63.37
93.34
57.73
91.92
85.80
97.71
AMC_Net
60.82
90.68
63.87
92.98
41.14
60.54
88.71
98.66
SpectrumFM
60.01
90.00
53.12
76.28
59.86
95.87
62.22
69.47
IQFormer
64.05
93.77
65.00
94.14
40.22
60.71
77.74
88.92
TS-DDAE
63.61
93.82
65.50
94.72
64.15
96.80
89.62
99.47
TS-DDAE (probe)
54.40
80.91
56.15
83,44
33.41
49.56
86.00
97.72
TS-DDAE (25-shot)
48.91
73.91
47.11
71.37
43.95
67.43
80.42
94.56
TS-DDAE (100-shot)
55.47
84.95
57.73
86,90
56.07
86.53
84.00
97.28
4.2.1 AUTOMATIC MODULATION CLASSIFICATION
Task Description. The AMC task is to automatically identify the modulation type used by the received signal under unknown prior parameters.
Dataset. We choose three most typical AMC benchmark datasets including RML2016. RML2006.. 1.8...0B00 O'shea & (216)) and RML2018 Shea et al. (2018) for this task. All datasets contain several modulation types under various Signal-to-Noise Ratio (SNR) conditions. The statis- ties of the datasets can found in Appendix B and we use 80% the data for training, 20% the data for evaluation.

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1.
10
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10Porme
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SpactnumFF
TS-00AS
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SNR(dB)
SNR(dB)
SNR(dB)
RML2016.10A
RML2016.108
TechRec
Figure 3: Visualization of performance under different SNR conditions (left: R.ML2016.100 10A, center: RML2016. 10B, right: TechRec).
Overall Performance. The general AMC accuracy results are listed in the first three columns of Table 1 where we list the average and the best performance of different SNR conditions. From the results, we can find that compared with the deep learning baselines, the TS-DDAE outperforms the best baseline by 1.35% on average, indicating the effectiveness of our theory and model archi- tecture. Compared with IQFormer, the SOTA method in AMC, the TS-DDAE also achieves SOTA performance in both "Average" and "Best" on RML2016. 10BB and RML2018 datasets. Though, the "Average" performance of TS- TS-DDAE on RML2016. 10A is slightly lower than that of IQ- Former, the "Best" performance is better. From this result, believe that for simple datasets like RMI.2016. 10A, TS-DDAE has not yet learned thoroughly, while for larger datasets like RMI.2018, TS-DDAE outperforms IQFormer by 23.07%, which means TS-DDAE has the potential to be trained on large-scale datasets and adapted to various AMC scenarios.
Performance under various SNR conditions. To better evaluate the robustness of TS-DDAE, we plot the performance of the models across various SNR conditions in Figure 3 We plot the results of dataset RML2016. 10A, RML2016. 1OB, and choose the baseline models Transformer, AMC.Net, SpectrumFM, IQFormer for clear view. At very low SNR (-20dB), the signal data is filled with noise, making almost impossible learn an appropriate signal representation. As the SNR in- creases, the model can more easily capture signal features, allowing the learned representation to be better applied to downstream classification tasks. Furthermore, our model performs well not only at high SNRs (greater than OdB), but also at low SNRs (-10dB to OdB). To have more comprehensive view, we also provide the visualization of confusion matrix and t-SNE Maaten & Hinton (2008) of the model classification effect in the Appendix
4.2.2 WIRELESS TECHNOLOGY CLASSIFICATION
Task Description. The WTC task is used to determine to which communication system or protocol family the received wireless signal belongs without prior knowledge.
Dataset. We use the TechRec dataset for the WTC task. The TechRec contains Wi-Fi, LTE, and DVB-T three types of wireless technology. To reduce memory consumption, we slice each signal from the raw dataset into 1024-length segments and label each slice based on the label of the signal to which it belongs. Furthermore, to simulate real-world noise, we manually add Gaussian noise to generate different SNR signals ranging from -15dB to 20dB
The WTC accuracy results are listed in the fourth columns of Table I which demonstrate the superior performance the TS-DDAE compared to the other baseline models. For the "Best" performance among all the SNRs, the TS-DDAE result. For the "Average" result, the TS-DDAE also achieves the SOTA performance, with about 11.88% improvement compared to the IQFormer, and 0.91% % compared with the SOTA baseline.
Overall Performance.
achieve nearly 1.0 identification
Performance under various SNR conditions. The performance across all SNRs TechRec is shown the right of the Figure 3 Similar to the performance of the AMC task, the performance
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improves the SNR increases. In particular, starting at -00dB, model consistently outperforms the SOTA AMC_Net model and significantly outperforms other baselines.
4.3 EVALUATION OF PRE-TRAINED MODEL CAPABILITIES (Q2)
To further evaluate the capabilities of our pre-trained model, we conduct the linear probe and few- shot experiments on the datasets RML2016. 10A, RML2016.100 10B, RML2018, and TechRec.
Linear Probe. We freeze the pre-trained model parameters and only train the classifier with the training dataset. The results are listed in the row "TS-DDAE (probe)" of Table [ Our linear probe results are comparable
the performance of some deep learning models. particular, for the TechRec dataset, our results outperform all deep learning baseline models, which exhibits good linear separability of the pre-trained features.
Few-shot Learning. As the IQ signals contain N classes under M SNRs, we refer to the N-way K- shot sampling methods from CV, and define our N-way K-shot M M-SN sampling method, which is to select N samples from each classification category and each SNR to form dataset of K x N x M IQ samples. In our work, we evaluate the 25-shot and 100-shot situation, whose results are presented in the rows "TS-DDAE (25-shot)" and "TS-DDAE (100-shot)" of Table [ respectively. Even with fewer than 1% training data, the TS-DDAE still achieves an accuracy comparable to certain deep learning baselines. This result highlights the few-shot capabilities of our TS-DDAE model.
4.4 ABLATION STUDY (Q3)
Table 2: The ablation results (percentage) of TS-DDAE architecture.
RML2016. 10A
RML2016. 10B
TechRee
Model
Overall
Best
Overall
Best
Overall
Best
TS-DDAE
63.61
93.82
65.50
94.72
89.62
99.47
w/o temporal
48.81
75.95
50.94
75.68
87.28
99.05
w/o spectral
62.97
93.09
64.79
93.73
37.48
47.58
w/o interactive
62.92
93.27
65.09
93.97
87.87
99.21
w/ single noise
63.01
92.59
65.12 12
94.14
89.27
99.21
63.4
65.6
65,54
90.5
90.41
89.97
65,21
90.0
63.0
62.98
65.2
65,08
65,13
89.76
62.84
89.56
Accuracy
65.0
64.8
64.72
62.6
62.22
64.4
88.5
62.2
64.2
1.0
0.4
0.8
1.0
1.0
RML2016.10A
RML2016.108
TechRec
Figure 4: The accuracy of different À value ranging from 0.2 to 1.0 (left: RML2016.10A, center: RML2016. 10B. right: TechRec).
In this part, we present the ablation study of TS-DDAE to evaluate the contribution of each compo- nent and the hyperparameter À analysis. The model parameter analysis is in Appendix E.
To validate the contribution, we evaluate four variants: (1) remove the temporal encoder (w/o tempo- ral), (2) remove the spectral encoder (w/o spectral), (3) remove the interactive process and complete

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denoising separately (w/o interactive), (4) use single noise the objective (w/ single noise), and keep the number of encoder layers consistent. The results are presented in Table First, compared to the TS-DDAE, the ablation variants w/o temporal and w/o spectral exhibit performance degrada- tion. For the datasets RML2016. 10A and RMI,2016. 10B of the AMC task, the temporal encoder plays key role, with decrease of about 15% in accuracy without the temporal encoder, while for the TechRec of the WTC task, the spectral encoder is more important, and the model without spectral encoder even fails learn a proper model. This result demonstrates the contributions of each component in TS-Net. Then, we remove the interactive process proposed in the TS-Net, and complete denoising separately (w/o interactive). The performance degradation further demonstrates the necessity of our interactive design. To further illustrate the effectiveness of using two types of noise, we conduct the experiment using only one noise, retaining TS-Net, and transforming the op- timization objective into the reconstruction of the original data (w/ single noise). The performance still degrades, which further illustrates that using different noise levels can optimize our training process. Fine-graines optimization helps the model learn the representation of the data, resulting in hugher-quality features.
Then, analyze the impact of hyperparameter À in Section 3.2 which is shown in Figure 4 As À gets closer to about 0.5, the model can achieve the best performance, which means that the two types of noise that we add will affect the model feature extraction certain extent. Keeping balanced noise addition will improve the quality of the model.
CONCLUSION
In this work, we introduce TS-DDAE, a diffusion-style framework that pre-trains models by jointly noising and denoising the temporal and spectral perspectives of IQ data. The accompanying TS-Net uses self-attention for the temporal encoder and channel attention for the spectral encoder, letting each reinforce the other. Across several WSR benchmarks, TS-DDAE achieves SOTA performance, and ablation studies confirm that all component matters. We therefore expect TS-DDAE to serve as a solid starting point for future WSR foundation models.
ACKNOWLEDGMENTS
This work is supported in part by the National Natural Science Foundation of China (No. 62550138, 62192784, 62572064, 62472329), BUPT Excellent Ph.D. Students Foundation (No. CX20241010), the Major Key Project of Peng Cheng Laboratory (PC1.2024A08).
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A THE DERIVATION OF THE BACKWARD PROCESS
We provide detailed derivation of the Bayes's theorem, given Equation 4 and Equation
p( 112, -(xx p(ppp-111tt (r0)
p (z0)
x exp(-3 (3f + (B) 7-1)
10
- 2(
a2
Tt
+
Ø1-1
20) + C)],
30
83
88
92
(13)
where C constant number that can be omitted.
B DETAILS OF THE DATASETS
We present the detailed statistics of the datasets that we used in Table where SNR denotes the signal-to-noise ratio used for the datasets RML2016. 10A, RML2016. 10B..,...BBB... RML2018, and TechRec, ratio used for the dataset ICARUS. For all datasets, evenly sample 80% of the data for each category and SNR (SIR) for training and 20% for testing.
and SIR denotes the signal-to-intererree
Table 3: The statistics of the datasets used in this work.
Dataset
RML2011.110A
RML2016. 10B
RML22018
TechRec
ICARUS
number of samples
220,000
1,200,000
2,555,90 5,49
202,762
673,200
length of each sample
128
128
1,024
1,024
1,024
number of classes min SNR (SIR) max SNR (SIR) SNR (SIR) interval
11
24
2
-20
-20
-20
-20
0
18
18
30
20
10
2
2
5
5
Table The pre-training time of TS-DDAE.
RML2016.10A
RML2016.10B
RML2018
TechRee
Time(s)
1,791
1,166
1,178
1,791
C IMPLEMENTATION DETAILS
Our pre-training initialization method is the default PyTorch initialization method, i.c., a uniform distribution bounded by Sigrttin_featurees Our pre-training learning rate strategy is the "ReduceL- ROnPlateau", with parameters: mode="mmn" factor=0.5, patience=250, min_Ir=le-12. To ensure a fairer comparison with baselines, pretrain model each dataset separately. Furthermore, to save time, we implement an early stop mechanism with a patience 1000. The overall pre-training time is shown in the Table
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D HYPERPARAMETER USAGE IN EXPERIMENTS
We present all hyperparameters used in experiments in Table where we list the description and the value of hyperparameters. Furthermore, we use the same hyperparameters for all of our datasets.
Table 5: The hyperparameters used in our experiments.
Parameter name
Description
Value
num_layers
The number of TS-Net layers
4
max max_step timestep
The maximal diffusion steps
3000
The step used in fine-tuning The ratio between the temporal noise and the spectral noise
4
ratio
0.414
The minimal noise added to signals
5.45e-06
max_noise
The maximal noise added to signals
0.0072
1.0
Rere (TP3)
Palas fasttive ROC Curve (SIR=DdB)
Fabe Pestave fatt (PP)) RDC Curve (SiR=5dB)
Palas Pos fase (178) (P7R)
ROC Curve (SIR 10d8)
Figure 5: AUC.ROC curve of the anomaly detection under different SIR (left: OdB, center 5dB right: 10dB).
E ADDITIONAL EXPERIMENTS
E. ANOMALY DETECTION
In this section, we supplement the results of the anomaly detection (AD) task.
Task Description. The AD task aims detect whether other unknown signals are mixed in the received signal.
Dataset. We use part of the ICARUS dataset for the AD task. The AD task the ICARUS dataset to detect whether the normal LTE signal is mixed with the anomalous DSSS signal. Similarly to the WTC settings, we also slice each signal into 1024-length segments. The signal-to-interferenne ratio (SIR) varies from OdB to 10dB, which is extracted from the dataset metadata.
Overall Performance. The results are presented in Figure 5
When SIR is OdB or 5dB, the TS- DDAE can get AUC nearly to 1.0, which demonstrates excellent anomaly signal detection capa- bilities. As the SIR increases to 10dB, the power the DSSS signals also increases, the detection performance drops, while still maintaining high performance (over 0.9). Compared with Spec- trumFM, the TS-DDAE can still achieve a higher performance, which further indicates the effec- tiveness of the designed model.
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0P5K 0.42
0.02
0.14
0.01
0.00
0.16
0.12
0.12
0.00
0.00
0.00
0.98
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
8P5K-
0:15
0.02
0.00
0.00
0.00
0.00
0.02
0.00
0.00
0.00
0.02
0.00
0.00
1.00
0.00
0.00
0.00
0.00
0.00
GISK 0.00
0.00
0.02
0.08
0.00
0.00
0.00
0.00
0.00
0.00
0.08
0,00
0.00
0.00
1.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
PAM4 0.00 0.00
1.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.00
QAM64 0.00
0.00 0.00 0.00
0.00
0.05 94
0.00
0.00 0.01
0.00
0.000
0.00
0.00
0.00
0.00
0.01
0.97
0.00
0.00
0.02
0.00
OPSK 0.24 0.03 0.13 0.00 0.00
0.12
0.06
0.38
0.00
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
0.00
0.00
0.00
0.00
0.00
0.00
AM-SSB- 0.00 0.00 0.02 0.02 0.02 0.00 0.00 0.00 0.00 0.92
0.01
0.00 0.02 0.02 0.02 0.01 0.00 0.00 0.00
0.00
0.94
0.00
WEFM 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.56
0.00
0.39
0.00
0.00
0.00 0.02 0.00 0.00 0.00
0.00
0.54
0.00
0.45
BPSK
BPSK
OPFSK
OPSK
PRM
044416
C46664
apsk
AM-
AM-55B
WBPM
BP9K
BPak
CPrss
GPSK
FAM
066418
QA464
OPSK
AM-559
WB71
Figure 6: Normalized confusion matrix on 10A dataset (left: -4dB, right: 2dB).
BP5K 0:48
0.02
0.04
0.00
0.01
0.08
0.16
0.21
0.00
0.00
0.99
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.83
0.00
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.00
0.91
0.02
0.03
0.00
0.00
1.00
0.00
0.00
0.00
0.00
GFSK- 0.00
0.00
0.00
0.00
0.00
PAM4 0.00 0.00
0.00
0,00
0,00
0.00
0.00
0.00
0.01
0.00
0.00
0,00
0.00
0.00
QAM16 0.01
0.00
0.00
0.00
0.00
0.84
0.15
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.99
0.00
0.00
0.00
0.00
UAM64 0.00 0.00
0.00
0.00
0.00
0.03
0.96
0.00
0.00
0.00
0.01
00
0.00
0.00
0.00
0.01
0.98
0.00
0.00
0.00
QPSK- 0.20
0.01
0.04
0.00
0.00
0.06
0.09
0.58
0.00
0.00
0.00
0.00 0.00 0.00
0.00
0.00
0.00
0.99
0.00
0.00
AM-D5B- 0.00 0.00 0.00 0.00 0.00
0.00
0.00
0.00
0.87
0.13
0.00
00 0.00 8.00 0.00 0.00
0.00
0.00
0.96
0.94
WBFM 0.00
0.00
0.62
0.57
0.43
BPaK
Brik
orak
PAMM
0
04464
QpSk
AM.05B
WBFM
BPSk
BPak
CRPSK
GrSk
GAM16
04M66
OPSK
AM-05B
WBFM
Figure 7: Normalized confusion matrix on RML2016. 10B dataset (left: -4dB, right: 2dB).
E.2 VISUALIZATION OF THE VS-DDAE OUTPUT
In this section, we visualize the output of the TS- TS-DDAE model in the AMC task. The results of the figures are consistent with the conclusions of the SpectrumFM and the IQFormer, and can further verify the the correctness and effectiveness of the TS-DDAE.
First, we give the normalized confusion matrix of TS-DDAE results on the RMI20016. 10A and the RML2016.109 dataset with -4dB and 2dB SNR, which is shown in Figure 0 and Figure 7 From the results, we can find that under low SNR conditions, noise will seriously affect the TS- DDAE's distinction between the three modulation types: 8PSK, QPSK and WBFM. When the SNR reaches 2dB, the model can already distinguish most modulation types well, even distinguishing some modulation types with 100% accuracy. However, the model can hardly distinguish the WBFM modulation type from the AM-DSB, which is consistent with the conclusions of the SpectrumFM and the IQFormer.
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Figure 8: Visualization of feature output with t-SNE on RML2016. 10A dataset (left: -00dB, center:
-6dB, right: 12dB).
Figure 9: Visualization of feature output with t-SNE on RML2016. 10B dataset (left: -20dB, center:
-6dB, right: 12dB).
63.61
63.6
65.6
65.50
90,62
65.38
90.5
65.24
90,20
63.2
63.05
63.05
63,11
65.2
90.0
89.85
Accuracy
65.0
64.83
89.5
62.6
64.6
62.4
68.5
64.2
62.0
64.0
88.0
3
3
5
5
RML2016.10A
RML2016.108
Figure 10: The accuracy of different number of layers ranging from 2 to 5 (left: RMM.2016.10A. center: RML.2016.105 10B, right: TechRec).
Then, we present the t-SNE visualizations of the RML20016. 10A and the RML2016.. 6.B dataset under -20dB, -6dB, and 12dB for TS-DDAE. The results are shown in Figure X and Figure At -20dB SNR, the data is filled with noise, leading the model to classify data randomly. Consequently, the various modulation categories appear mixed together in the t-SNE figure. At low SNR (- 6dB), our TS-DDAE already demonstrates a certain degree discrimination, with many modulation better.
categories clearly distinguished. At high SNR (12dB), the model's discrimination Furthermore, at 12dB, the model fails to distinguish WSFM and AM-DSB modulation categories particularly well, which is consistent with the conclusions in Figure 6 and Figure 7
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E.3 THE EFFECT OF THE MODEL SIZE
In this section, we provide experiments of different numbers of encoder layers in TS-Net. Each additional layer means adding one temporal encoder and one spectral encoder simultaneously. The results are shown in Figure TO As the number of layers drops, the performance of RML2016. 10A and RML2016. 1OB also drops. The proper number of layers 4, which used in our main exper- iments. For dataset TechRec, the performance reaches the highest when the number of layers is 5, which we think that the scale of the data is relatively large, and large model can get better signal representations.
F
THE USE OF LARGE LANGUAGE MODELS (LLMS)
We use the LLM Kimi K2 to aid or polish writing of this paper.
17


Reviews:
[{"review_id": "xTlX1dytmp", "reviewer": "Reviewer_Aix4", "summary": "In this paper, the authors propose a Temporal-Spectral Dual Denoising Autoencoder designed for wireless signal classification . The proposed model includes two contributions: (1) a dual-branch encoder. One for temporal (IQ) and one for spectral (FFT of IQ) data; and (2) a dual-diffusion denoising pre-training scheme, where both temporal and spectral branches learn to reconstruct noisy inputs via a diffusion-based objective. The authors claim this design captures complementary information (time and frequency structure) and improves robustness under low SNR or unseen modulation types.", "questions": "(1) What architecture or initialization is used during pre-training (same as fine-tuning or smaller encoder)?\n\n(2) How long is the pre-training and on which dataset?\n\n(3) What self-supervised schedule or learning rate strategy is used in pre-training?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) The paper is well-motivated and well-presented. The paper identifies an underexplored limitation in AMC where the existing methods often rely solely on IQ or spectral representations. The temporal-spectral dual design is conceptually well-grounded. \n\n(2) Introducing a denoising diffusion to selectively mask or corrupt input signals is an elegant and well-motivated regularization strategy for representation learning, particularly in the context of wireless signals. The formulation aligns well with the established diffusion pre-training principles and has clear potential to inspire future research directions in this domain.\n\n(3) The empirical validation is comprehensive. The proposed method achieves strong performance on large-scale datasets and maintains robustness at low SNRs. Results demonstrate that the fusion of temporal and spectral cues contributes meaningfully.", "weaknesses": "(1) While the paper briefly mentions the denoising objective, some important implementation details are missing (see Questions 1 to 3). Without these details, it is difficult to validate the claim that the improvement arises from the proposed pre-training rather than other training heuristics.\n\n(2) In Lines 267 to 269, the authors claim that the Feed-Forward Network (FFN) injects non-linearity. However, Figure 2 and the surrounding explanation show only linear components. There is no mention of a nonlinear activation. If the FFN is purely linear, it cannot increase representational capacity in a nonlinear way. Please clarify whether an activation function is omitted in the figure or truly absent. If absent, revise the wording to avoid implying nonlinearity.\n\n(3) My main concern is the input redundancy across encoders. Both the temporal encoder and spectral encoder are said to take both IQ data and spectral data as input. Conceptually, this does not follow the motivation (temporal and spectral disentanglement) and could lead to redundant or entangled features. Please conduct an ablation study where each encoder only uses its corresponding modality to confirm its necessity.\n\n(4) The reported results show that TS-DDAE underperforms IQFormer on small-scale datasets but outperforms on large ones. This raises a concern that improvements may be primarily from larger model capacity or heavier training rather than architectural novelty. No evidence (e.g., parameter-matched comparison or FLOPs table) is provided to rule this out. Please add an ablation where model parameters are matched with the baseline, or provide FLOPs/parameter counts for each model in a table to isolate architecture-driven performance improvements.", "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": "qMsaopo64L", "reviewer": "Reviewer_sJ6p", "summary": "The authors propose a novel self-supervised representation learning approach for wireless signals based on the paradigm of denoising diffusion. A key contribution is the use of both, the time domain and frequency domain representation of the signals, in the diffusion process. In addition, they propose a model architecture that performs the diffusion process for both data views in an interconnected manner. They use attention to detect important contributions along time-steps and along convolutional channels according to the properties of the respective view.", "questions": "- The Metric used in section 4 is not explained properly. Is it the Top-1 accuracy? \n- How are the baseline models trained?\n- Can you please explain the rationale behind adding noise in the frequency *and* in the time domain?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "- The use of denoising diffusion for wireless signals is original. \n- The authors consider the properties of each view of the signals in the design of the neural networks architecture.\nUsing attention to extract features along time and convolutional channels appears to be thoughtful.\n- A comprehensive evaluation on two downstream tasks is performed. The proposed method shows significant gains over the state-of-the-art across multiple datasets.", "weaknesses": "The theoretical model in Section 3.2 may contain a substantive problem. The authors appears to suggest that temporal–spectral diffusion reduces to adding two noise processes with different intensities to the time-domain representation $x_{t-1}$ (Eq. 1-5). Since the FFT is a unitary transform, adding noise in the frequency domain is equivalent to adding noise in the time domain. The manuscript does not currently explain why adding noise twice, rather than once, would improve the diffusion process. In addition, the role of the hyperparameter λ—intended to balance spectral and temporal noise power—remains unclear if these quantities are effectively equivalent.\n\n\n- Minor Problems: \n\t- Unintroduced formula symbols (mu, tau) in section 3.2. \n\t- The claim \"Masking corrupts fine-grained information in the input data\" is not backed up by citations/theory\n\t- In the ablation study in 4.4 a simple case should be investigated that considers both data views simultaneously without using TS-Net, to ensure the reported gains are achieved by the network design and not by using richer input data. \n\t- Figure 2. is not explained sufficiently", "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": "UdyUTEDyWf", "reviewer": "Reviewer_73Fu", "summary": "This paper proposes a new framework for Wireless Signal Recognition (WSR). The authors introduce a pre-training and fine-tuning paradigm that enables the model to adapt to multiple downstream WSR tasks. Experiments further demonstrate the few-shot learning capability of the proposed method.", "questions": "Based on the Weaknesses, I have totally three questions:\n- Did the authors provide details about the total amount of data used in the pre-training stage?\n- Can the authors further test the algorithm on the larger-scale datasets?\n- The authors can test the out-of-domain experiments to further verify the generalization ability of the method.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper is easy to follow, and each section is well-written. The authors provide detailed texts to describe their methods.\n- The authors provide experiments on several benchmarks to validate the performance of the proposed method.", "weaknesses": "- The authors claim that this method is a \"foundation model\" in the abstract. In this case, did the authors provide details about the total amount of data used in the pre-training stage?\n- I am also curious about the scalability of the method. Can it be extended to larger-scale datasets?\n- It is recommended to conduct out-of-domain (OOD) experiments to evaluate the generalization ability of the method. The OOD tests are important for any foundation model.", "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)