Efficient Low-Bit Quantization with Adaptive Scales for Multi-Task Co-Training
Authors: Boyu Liu
Haoyu Huang
Linlin Yang
Yanjing Li
Guodong Guo
Xianbin Cao
Baochang Zhang
Venue: ICLR 2025 Poster (2025)
Keywords: Low-Bit Quantization
Multi-Task Learning
Co-Training
Quantization-Aware Training
Quantization Scale
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Abstract: Co-training can achieve parameter-efficient multi-task models but remains unexplored for quantization-aware training. Our investigation shows that directly introducing co-training into existing quantization-aware training (QAT) methods results in significant performance degradation. Our experimental study identifies that the primary issue with existing QAT methods stems from the inadequate activation quantization scales for the co-training framework. To address this issue, we propose Task-Specific Scales Quantization for Multi-Task Co-Training (TSQ-MTC) to tackle mismatched quantization scales. Specifically, a task-specific learnable multi-scale activation quantizer (TLMAQ) is incorporated to enrich the representational ability of shared features for different tasks. Additionally, we find that in the deeper layers of the Transformer model, the quantized network suffers from information distortion within the attention quantizer. A structure-based layer-by-layer distillation (SLLD) is then introduced to ensure that the quantized features effectively preserve the information from their full-precision counterparts. Our extensive experiments in two co-training scenarios demonstrate the effectiveness and versatility of TSQ-MTC. In particular, we successfully achieve a 4-bit quantized low-level visual foundation model based on IPT, which attains a PSNR comparable to the full-precision model while offering a $7.99\times$ compression ratio in the $\times4$ super-resolution task on the Set5 benchmark.

Published as a conference paper at ICLR 2025
EFFICIENT LOW-BIT QUANTIZATION WITH ADAPTIVE SCALES FOR MULTI-TASK CO-
Boyu Liu't, Haoyu Huang"i, Linlin Yang", Yanjing Li Guodong Guo Xianbin Cao*, Baochang Zhangl677
IIstitute of Artificial Intelligence, Beihang University
NNttoonl Superior College for Engineers, Beihang University
State Key Laboratory of Media Convergence and Communication, Communication University of China
"School of Electronic Information Engineering, Beihang University
'Ningbo Institute of Digital Twin, Eastem Institute of Technology, Ningbo Hangzhou Innovation Institute, Beihang University
"Zhongguancun Laboratory "Nanchang Institute of Technology
ABSTRACT
Co-training can achieve parameter-efffcient multi-task models but remains unex- plored for quantization-aware training. Our investigation shows that directly in- troducing co co-training into existing quantization-awarr training (QAT) methods re- sults in significant performance degradation. Our experimental study identifies that the primary issue with existing QAT methods stems from the inadequate ac- tivation quantization scales for the co-training framework. To address this issue, we propose Task-Specific Scales Quantization for Multi-Task Co-Training (TSQ- MTC) to tackle mismatched quantization scales. Specifically, task-specific learnable multi-scale activation quantizer (TLMAQ) incorporated to enrich the representational ability of shared features for different tasks. Additionally, we find that the deeper layers of the Transformer model, the quantized network suffers from information distortion within the attention quantizer. A structure- based layer-by-layer distillation (SLLD) is then introduced ensure that the quan- tized features effectively preserve the information from their full- full-precision coun- terparts. Our extensive experiments in two co-training scenarios demonstrate the effectiveness and versatility of TSQ-MTC. In particular, a 4-bit quantized low-level visual foundation model based on IPT, which attains a PSNR comparable to the full-precision model while offering 7.99x compression ratio in the x super-resolution task on the Set5 benchmark.
successfully achieve
INTRODUCTION
The paradigm of multi-task training (Caruana 1997) facilitates knowledge sharing between different
tasks and enables the learning of universal representations through multi-objective training across
diverse datasets. Multi-task learning has achieved significant advancements in various fields such
as natural language processing (Collobert Weston 2008 Raffel et al.) (020), computer vision
(Bigen & Fergus 2015 Likhosherstov et al. 2022) and multi-modal learning (Hu & Singh 202T
Chen et al. 2023a).
Unlike early multi-task learning methods (He et al. 2017 Zhang et al. 2014), which develop mod- els to complete multiple tasks simultancously, co-training (Maninis et al. 2019) trains a versatile model that performs single task time for given input. This learning strategy enables the co- trained model to handle multiple datasets with varying characteristics (Zhang et al.) (02T). There- fore, co-training has been widely used to establish a universal representation learning framework for
multi-modal data (Girdhar et al. 2022) or single-modal task-related data (Chen et al. 2021). No-
Equal contribution. "Corresponding Authors: lyangEcuc.edu. en, yanji iinjing] @buaa. enu

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Sceled Feeture Distribution of Tesk
_.caeeed Distribution 0,805 quart 4,57
Sceled Feeture Distribution p.093 quentiv 4,30
0,093 cuattin 4.45
0,003 cuantiie 4.57
0.055 -1.49
5.005 quantive 4.30
Fequeecy
Frequency
Value
Value
Value
Scaled Feature Distribution of Tesk
..calee Festure Distribution of Task
Sccaedd Feature Distribution of Tesk
2,965 quantie 3.75
0.995 quantle: 7.64
b. 9.9 quantle 7:21
9,003 quantil 3.72
0.005 ait5-quntlle le: 7.46
0.003 quantle: 7.24
Reqqeency
Frequency
Frequency
Value
Value
Figure 1: Feature distributions of decoder.layer9 value in the 4-bit IPT model co-trained with LSQ+ (see baseline-multi in Sec. (32). The first row shows three tasks (tasks 0, 1, and 2) for x2, x3, and x4 super-resolution tasks, respectively. In the second row, task 3 corresponds to the deraining task, while task 4 and task 5 are denoising tasks with o = 30 and a = 50. The green dashed line indicates the position of the 0.995 quantile, and the orange one shows the position of the 0.005 quantile. We can observe significant distribution differences between tasks, which require diverse activation quantization scales for co-training.
tably, the co-training paradigm efficiently integrates multiple tasks into single framework through shared parameters, offering considerable advantages for deployment on edge computing devices (Likhosherstov et al. 2022).
Despite the high performance with shared parameters, the inherent computational and memory over- head of co-trained models still restricts deployments in scenarios with limited memory and computa- tional resources Likhosherstov et al. 2022). By integrating low-bit quantization (Esser et al.) 2019 Hubara et al.] (021) into the co-training framework, we can effectively enhance the deployment potential of co-trained models in practical scenarios. Quantization mainly involves mapping the original floating-point values of weights and activations lower-bit representations, such as 8-bit or even binary formats, while striving to maintain model accuracy (Zhou et al.) (016). For models designed for image classification and object detection, quantization methods have been extensively studied and shown notable success (Yang& & Jin |02 Wang et al. 2022. Xu et al.) 2023). However, in the context of co-training, directly introducing quantization-awanl aware traming (QAT) (Bhalgat et al., 2020) suffers from inferior quantization performance.
In this work, we focus on integrating quantization-awar training with the co-training paradigm, which is underexplored in prior research. Based on the 4-bit QAT' baseline established Image Processing Transformer (IPT) (Chen et al.] (202T), we observe that the quantized baseline model after co-training exhibits significant performance degradation compared its full-precision coun- terpart, with varying performance gaps across different tasks. Through an analysis of the activation quantizer, we identify that the incompatibility of existing QAT methods for co-training primarily stems from the activation quantization scales. Specifically, as shown in Fig. | for the same activa- tion quantizer, the input feature distributions vary significantly between tasks, particularly between super-resolution and denoising tasks. This discrepancy results in a conflict, which degrades the representational ability of the shared quantized features across tasks. Moreover, we find that in the deeper layers of the Transformer model (Vaswani] 2017). the quantized attention module of- ten experiences information distortion. Therefore, simple logit-level supervision is ineffective and coarse gaanse-rranned for differences in attention information distortion across different layers.
In this paper, we build the baselines by naively integrating the multi-task co-training with QAT, validate their effectiveness, and show the analysis through comprehensive experiments. Based on baselines and analysis, we propose an effective low-bit quantization method, Task-Specific Scales Quantization for Multi-Task Co-Training (TSQ-MTTC. To address the issue of mismatched quantiza-
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tion scales during co-training, we introduce task-specific learnable multi-scale activation quantizer (TLMAQ), enabling different task imputs to have richer representational ability and improve the low- bit quantization performance. Meanwhile, to ensure that the quantized features effeetiiveyy preserve the information from their full-precision counterparts, we propose a structure-baset layer-by-layer distillation (SILD). It leverages the structural similarity of features, thereby enhancing the model's adaptability in each task. We conduct comprehensive experiments in two co-training data scenarios: a single-modal task-related data scenario based on IPT and a multi-modal data scenario based on ResNet, demonstrating the effectiveness of TSQ-MTC.
To summarize, our contributions are as follows:
1. Our Task-Specific Scales Quantization for Multi-Task Co- Co-Training (TSQ-MTC) effectively incorporates quantization-ave sallaaattoo--aaeeeeeaee training into co-training and significantly reduces the per- formance gap between multi-task co-trained models and their 4-bit quantized counterparts.
2. We introduce task-specific learnable multi-scale activation quantizer (TLMAQ) for multi- task co-training. TLMAQ addresses the issue of mismatched quantization scale and im- proves the representational capabilities of quantized features acToss tasks.
3. We design a structure- pruuture-bbsed layer-by-layer distillation (SLLD). It deals with information distortion problems in deeper layers of Transformer, thereby enhancing framework's effec- tiveness and versatility.
4. Extensive experiments on two co-training data scenarios (a single-modal task-related data scenario and multi-modal data scenario) show that our 4-bit quantization outperforms the baselines by large margin and achieves comparable performances with the full- cull-precision counterparts.
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RELATED WORK
2.1 MULTI-TASK LEARNING AND CO-TRAINING
Early multi-task learning (MTL) methods (Zhang et al. 2014 Eigen & Fergus 2013 Ren et al, 2016) improve the performance by using a single input to produce different task outputs. They aggregate the loss of tasks while adjusting their importance. However, these models encountered the challenge of task interference (Maninis et al. 2019), where potent backbone could enhance overall multi-task performance, yet the performance of each task might still fall short compared to that achieved through single-tasi task training with the same backbone (He et al. 2017 7 Kokkinos 2017). Co- To-Training (Maninis et al., 2019). also referred to as "single-tasking of multiple tasks". develops model trained on multiple tasks but executes only one task at time, effectively addressing task interference in shared networks. As research on unified frameworks (Gao et al., [023]] Wang et al.) 2023 Han et al. 2024) continues to expand, co-training typically adopted in two types of data scenarios: the multi-modal data scenario and the single-modal task-related data scenario. In the former scenario, co-training integrates varying modalities of data and tasks to unify representations (Hu & Singh 2021 Zhang et al. 2021 Likhosherstov et al. 2022 Chen et al.) 2023b Srivastaval & Sharma 2024). In contrast, in the latter scenario. models are trained to focus on the intrinsic correlation of feature learning among different tasks under single modality (Chen al. 2021 Ma
et al. 2024 [i et al. 2024). Due to the training efficiency and multi-task capabilities of co-training, is frequently utilized one of the training techniques for foundation models (Chen et al.) 2023a). However, incorporating quantization-awarr training into this learning approach to further enhance the model's lightweight deployment capabilities remains underexplored area for research.
2.2 QUANTIZATION SCALE
Quantization scale (the width of a quantization bin) is a crucial parameter within quantizers, en- abling effective quantization mappings for both activation inputs and weights (Esser et al. 2019)) Early methods (Li et al.) 2016; McKinstry et al. 2018) typically calculate quantization scales di- rectly using data distribution statistics during each forward pass. Due to the fluctuating ranges in activations, these methods often result in training instability (Jacob al. 2018). In quantization- aware training, follow-up methods (Esser et al. 2019 Bhalgat et al. (2200) propose novel strategies to learn quantization mapping in deep networks. Specifically, LSQ (Esser al. 2019) approximates
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the gradient to the quantizer step size (scaling factor), sensitive quantized state transitions. In contrast, LSQ+ (Bhalgat et al. 2020) points out to involve learnable offsets for more effective asym- metric quantization. For super-resolution (SR) models (Lim al. 2017). PAMS (Li et al. 2020) applies a trainable truncated parameter (can be regarded as quantization scale) to explore the upper bound of the activation quantization range to accommodate varying distributions of feature maps. Additionally, multi-bit quantization methods (Xu et al. [022]] Hong et al. 2022) provide another efficient multi-scale quantization approach, whereas there is limited hardware deployment support. We also notice that some Parameter-Efiin Efficient and Quantization-ane aware Adaptation (PEQA) methods (Kwon al.] 2022 Chen et al. 2024) introduce quantization to efficient fine-tuning of large lan- guage models (LLMs), where only the scales of weights are fine-tuned in specific task, enhancing the adaptability of the LLMs to downstream tasks. Unlike existing multi-bit quantization or PEQA, we identify the bottlenecks in co-training during QAT, focusing on the shared representation learning for quantized models with adaptive scales suitable for efficient lower-bit deployment.
PRELIMINARIES
3.1 CO-TRAINING FRAMEWORK
In a multi-task co-training, the architecture of models usually comprises multiple heads Hask (.) and tails (.)k()) to address each task independently, along with shared body B(.) and task- specific query embedding fuck (Hu & Singh 2021 Likhosherstov et al.) 2022; Girdhar et al. 2022
(Srivastava & Sharma 2024). During the training, the model is trained multiple tasks. Still, for
each batch, specific task input is selected based on sampling strategy (Likhosherstov et al. 2022
Girdhar al. 2022). The overall process expressed as:
Ô Tusk (B (Heask (4), fisk)),
(I)
where / denotes the imput data for a specific task, and Ô represents the output generated by the network after processing through the head, shared body, and tail.
For instance, IPT (Chen et al. 2021), ), based on the degradation modeling of different low-level visual tasks, synthesizes various corrupted task -ase-speeifii images from randomly selected data in large-scall ImageNet (Deng et al.) (209) dataset as task inputs for co- co-training, It achieves dominant high per- formances in super-resolution, deraining, and denoising.
3.2 QUANTIZATION BASELINES
We follow LSQ+ (Bhalgat et al. 2020) to introduce general framework of symmetric channel weight quantization and asymmetric layer-wist wise activation quantization. The quantization operations are defined as follows:
}}
(x(x)= = clip
x - is
(w) = clip QF
w
Q
(2)
a
i Qx(z) o ar + Zz: ② Qw (w) a':: where r denotes the activation value, w denotes the weight value, and 2, represents the zero-point offset for activation quantization. a₂ and an are the scaling factors for activations and weights, respectively. With a-bit quantization, T -2-11 function clip(y. r2) returns y constrained between r11 and r2 (i.e., values lower than are set to 71. and values higher than r2 are set to r2). The operator [y] rounds y to the nearest integer, and the denotes the channel-wise multiplication (layer-wise for activations with scalar ag). ft and i represent the dequantized approximation values of the activations and weights, respectively. Denoting the loss function as L, the straight- -traight-through estimator (STE) (Bengio et al.) 2013) is used to retain the derivation of the gradient in backward propagation:
(3)
2--1 - I are the discrete bounds. The
ac OL dā
ac
i E 1-Q Q.1
BE to
(4)
dr ai dr
otherwise
We apply the quantization framework to perform 4-bit quantization co-training models like IPT, establishing two baselines (baseline-single and baseline-multi). Given the full-precision pre- trained model, baseline-single uses the above quantization framework to quantize full-precisio model for each task separately. Instead, baseline-multi co- co-trains the model with QAT directly. The details of baseline-multi and baseline-single are expatiated in Appendix A
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PSNR for 58
PSNR for
PSNR for
BM
34.29
8M
30.73
BM
28.89
85
54.03
85
30.59
85
28.70
FP
34.43
30.85
FP
29.01
PSHR (dB)
PSVR (dB)
PSNR (GB)
Figure 2: Comparison of PSNR on Set14 super-resolution benchmark for baseline-multi (red, BM), baseline-single (green, BS), and full precision IPT model (blue, FP).
Layer Feature Distribution
Layer Feature Distribution
Layer
Distribution
Distribution
15000
2000
13000
13500
11500
10000
2000
2000
3500
2500
Attention Volue
Attention Value
Attention Velue
Actention
Figure 3: Histogram of features produced by general attention calculation (orange) and quantized calculation (blue) from 4 selected attention layers in baseline-multi (4-bit quantized IPT model). We can observe that the distribution of quantized features differs noticeably from that of full-precision features, especially in the deeper layers, indicating information distortion.
CHALLENGE ANALYSIS
We conduct and analyze the aforementioned baselines on IPT. Sec. 4.1 indicates that baseline- multi exhibits more competitive performance. Sec.4.2 4.2 points out the bottlenecks arising from the optimization competition among tasks, which can lead mismatched quantization scales, affecting model performance.
4.1 CHOICE BETWEEN SINGLE-TASK AND MULTI-TASK QUANTIZATION
Considering that multi-task learning may lead to task interference problem (Maninis et al., 2019), a primary question whether co-training with multiple tasks can be integrated with quantization while maintaining the superior performance of full-precision models. We compare the performance of baseline-multi and baseline-single on super-resolution tasks (x2, x3, x4 scalc), as shown in Fig. 2 More results and evidence presented in Sec. 6. Due to the regularization of co-training Likhosherstov et al., 2022), the baseline-multi achieves better performance across multiple tasks compared to the baseline-single, which aligns with the training results of the full-precision model. Thus, it is worth noting that although the full-precision IPT exhibits prominent performance and generalization capabilities during co-training, the learned universal representation (Chen al. (2021) does not translate well to a low-bit quantization counterpart when finetuning on a single task. Based -ware aware training into co-training.
on this observation, is essential to incorporate quantization
4.2 BOTTLENECK OF CO-TRAINING QUANTIZATION.
With the quantized co-training framework, we further discover that the baseline-multt still exhibits significant performance loss compared to the full-precision model, with varying gaps in perfor- mance across different tasks, as shown in Fig. 2 The analysis of the quantized feature distribu- tions in the activation quantizers reveals that the incopptibiliiyy of existing QAT methods primarily stems from mismatched activation scales in the co-training framework. Specifically, we visual- ize the input feature distributions and their 0.995 / 0.005 quantiles for the activation quantizer of decoder.layer9.se/e wttpoyyerrseel.attttmot setf_attlffffeeaaeeeeeeeeeaa value in baseline-mults as shown in Fig. [ It can observed that, for the deraining task, the range of 3-3.72, 3.75] can cover 99% of its feature values, whereas de- noising tasks require an interval of [-7.66, 7.64]. This indicates that different tasks reveal distinct data feature distributions, particularly with significant differences in the mean and variance between supertiesolution and denoising tasks. Moreover, during training, the influence of different tasks on quantization scales may conflict, resulting in the failure of quantization for specific tasks. Conse-
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Mulli-modal dasa
Task related data
[classi]
Colandd
Demin
Denoiic
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Tusk Batch sampler
Transformr Layer
query ambodding fr
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Figure 4: Overview of TSQ-MTC. Note that our TLMAQ can be employed for all activation quan- tization in both the linear layer and attention module of the Transformer, well as the convolution layer in CNNs examined in our experiments.
quently, the scaling factors learned by the activation quantizer cannot effectively match all input data, negatively impacting the representational ability of the quantized features.
In addition, during co-training, we notice that quantization introduces information distortion and precision errors, which can be regarded cumulative distribution perturbations (Li al. 2022 Xu et al., 2023), especially within the attention module of Vision Transformers (Dosovitskiy) (021). As shown Fig. 3 there is a noticeable difference between the features produced by the general attention calculation and those generated by the quantized attention calculation, resulting in loss of quantization performance.
THE PROPOSED METHOD
This section provides a detailed explanation of our Task-Specific Scales Quantization for Multi-Task Co-Training (TSQ-MTC), as shown in Fig. 3 effectively integrating quantization-awee awwree training (QAT) with co-training. Our TSQ-MTC mailly consists of two novel techniques: task-specific learnable multi-scale activation quantizer (TLMAQ) and structure-based layer-by-layer distillation (SLLD), which are presented in Sec, 5.1 and Sec. 5.2 respectively.
5.1 ADAPTIVE QUANTIZATION SCALES FOR MULTI-TASK CO-TRAINING
Because LayerNorm (Vaswani, 2017) in Transformers and BatchNorm (loffe & Szegedy) 2015) in CNNs can not align the distributions of data in different tasks, multi-task learning may lead to degraded model performance, especially in quantization. The core of approach is design more suitable quantization mappings that allow the model to adapt simultaneously to the data distributions of different tasks. PAMS (Li cLi al. 2020) indicates that the range variations of the weights are stable. Therefore, adjusting the layer-wise activation quantizers is efficient and does not add overhead. We propose task-specific learnable multi-scale activation quantizer (TLMAQ), which learns the corresponding activation quantizer scales for each task input to alleviate the quantization mapping conflicts between different tasks. Specifically, given training tasks, for the input data æ belonging to task k, there is a corresponding activation function:
Qa fk) = clip { x (2(k))
2 2. }
(5)
$ = Qa(, ft) a (ak)) (zv);
where fü is the query embedding of task k. (fve)) and o (fx) are switching functions to select the learnable scaling factor a and offset 2 corresponding to task for activation quantizer respectively. Since the quantized feature is also inversely transformed the original scale after completing the

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Scaling
Derates 30
08440
Derease
04133
Denorse
1:3718
Denwisg
Deran
3,4566
Deniih
6.4475
Denan
0.4168
0.3288
0.325
0.8027
23.5%
X2 5R 0.5238
32.55 54
0. 5115
X2 54
0,5000
Figure 5: Visualization of the task-related scaling factors for the activation quantizer in en- coder.layerI.sef query decoder.ayeró... wan.derrlayerb...nar.... decoderlaaerr,.ee attn. value of the IPT model trained with TLMAQ. It is noted that the scaling factors for the denoising task (red), deraining task (green), and super-resolution task (blue) show distinct differences.
low-bit calculation using Eq. 6 different quantization scales will not affect the floating-poin range of the output feature. Our task-specific learnable multi-scale activation quantizer performs inde- pendent quantization for different task inputs, aligning with the co-training strategy and resulting in a simple and efficient implementation. Therefore, it can be used for the linear layer and attention module in the shared Transformer backbone and the convolution layer in CNNs, examined in Sec. 6 The overall process is summarized in Alg.
Initialization and convergence of scales. Although the quantization scales of the model are learned through gradient updates, their initialization significantly impacts the network's convergence and performance (Bhalgat et al.) 2020). Using the feature statisties of a pre-trained full-precision net- work as initialization enables the model to adapt effectively to training tasks (Li et al. 2020). There- fore, we initialize the weights from their full-precision counterparts the start of traming and then calculate the corresponding quantization scales a and 2 separately for each task & based on the different inputs x and task query embedding fi. Experiments indicate that converged full-pre full-precision weight is indispensable for task-specific quantization scales; otherwise, TLMAQ may not lead to performance improvements.
Task-scale consistency. To validate the effectiveness of TLMAQ, we conduct a visual analysis of the quantization scales for the IPT model trained with our module. Among the various activation quantizers, the quantization scales exhibit significant task consistency, with similar tasks showing comparable quantization scales. In contrast, dissimilar tasks demonstrate considerable differences, shown in Fig. [ Specifically, the scales for x2, x3, and x4 super-rcsolution tasks similar, whereas there is no apparent correlation of scales with the denoising tasks and super-resolution tasks. This observation aligns with the similarities in task distributions discussed in Sec. 4.2 Therefore, we suggest that quantization scales can be merged according to tasks after convergence, allowing similar tasks share a of quantization scales, further reducing the model's parameters.
5.2 STRUCTURE-BASE LAYER- LAYER-BY-LANE BY-LAYER DISTILLATION
To mitigate the information distortion observed in Sec. 4.2 we adopt a layer-by-layer distillation fol- lowing Wang et al. (2024). Specifically, in both the encoder and the decoder of the Transformer, q. k, and V simultaneously perform general and quantized attention caleulation, yielding full-precision outputs F and quantized outputs F for information alignment. However, vision tasks, especially low-level ones, are susceptible to information loss. Simple logit-level supervision is effective for classification tasks but coarse-grand coarse-grained and sub-optimal in this context. In contrast, structural simi- larity (SSIM) preserves spatial information more effectively, which is crucial for vision tasks (Xie) ct al. 2023). We compute the mean SSIM over the feature map using a sliding window. The quan- tized outputs f are refined with a distillation loss:
Lase A MS F)),
where MSSIM(.) is the mean SSIM function and Adis is the coefficient of distillation loss. This distillation method directly computes the full-precision attention results before quantization. Thus, it does not require full-precision model, thereby reducing the resource requirements for training. For Transformer models, the final optimization objective is the combination of the task-specific loss and the SLLDD loss. The effectiveness of SLLDD can be found in Appendix [
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Algorithm Task-Specitfe Scales Quantization for Multi-Task Co-Training with TLMAQ
Input: Set K task-related training datasets = [D,,..., DaJ. full-precision model My, task-specific activation quantizer scaling factor a and offset Output: Quantized model M,
1: Initialize weights of MM from My with Eq. 2
2: for 1 to K do
3: Initialize a and z with input from dataset DR:
4: end for
5: for -rri= 1 to epoch do
6: Randomly select task k;
7: Sample a mini-batch in Dx:
8: Forward propagation with Eq. 1 and Eq.
9: Update weights and a and 2zz via gradient backward propagation;
end for
11: return M4-
6 EXPERIMENTS
In this section, we evaluate the performance of TSQ-MTC two settings within the co-training framework. The first experiment focuses single-modal task-related data co-training, conducted on IPT, performing low-level vision tasks. The second experiment focuses on multi-modal data co-training and conducted on shared-parameter CNN classification models. Comparisons with state-of-the-art single-task super-resolution quantization methods are provided in Appendix B Ad- ditionally, we discuss the compression performance TSQ-MTC compared baseline-multi, in- cluding parameter counts and operations, in Appendix
6.1 SINGLE-MODAL DATA Co-TRAINING
Task. In this experiment, we focus on utilizing TSQ-MTC to improve the performance of 4-bit quantized IPT model, which processes single-modal task-related image data. We compare our TSQ- MTC with baseline-single and baseline-multi based on LSQ+ (Bhalgat et al. 2020). In addition, we also show results of several guantization methods related to our work, including MinMax using statistics to calculate scales, LSQ (Fsser et al. 2019) with learnable scales, PAMS (Li et al. 2020) designed for super-resolution and Q-ViT (Li et al. 2022) for Transformer-hasee model. Except baseline-single, all methods are trained in the co-training framework, including seven low-level
vision tasks, such as super-resolution (x2, x3 and x)), deraining, and denoising (a 30 and 50).
Datasets. We evaluate the super-resolution performace on Set5 (Bevilacqua et al. 2012), Set14 Zeyde al. 2012). B100 (Martin al. 20001) Urban100 (Huang et al. 2015). For the denoising task, we adopt CBSD68 (Martin et al. 2001) and Urban 100. As for deraining, we use Rain 2017p. For all benchmarks, the performances measured by PSNR.
Yang al.
Results. Quantitative results for super-resolution tasks are shown in Table The baseline-single achieves only 33.00 dB PSNR for the x2 seale super-resolution task the Urban dataset, which is significantly lower than the 33.76 dB of the full-precision IPT. In contrast, the baseline-multi achieved 33, 33.46 dB the same task after co-training, demonstrating the effectiveness of co-training. Moreover, the PSNR on all super-resolution tasks achieved by our TSQ-MTC method is signifi- cantly higher than baseline-mult, showcasing the superior effectiveness of our adaptive scales. For example, the 4-bit IPT trained by our method achieves 27.08dB PSNR on Urban 100 dataset for SR, surpassing the baseline-multi by 0.23 dB. TSQ-MTC also attains higher PSNR than other existing methods, such as MinMax, LSQ, PAMS and Q-VIT. Furthermore, our approach reaches comparable results compared to full-precision counterpart on specific datasets (x2 super-resolution on Set14 / B100 and x super-resolution on Set5), while accelerates and compresses the model by 7.99x. Similar trends can also be found for denoising and deraining tasks in Appendix For in- stance, our TSQ-MTC reaches 42.02dB in deraining task on Rain dataset, while baseline-multi achieves only 41.89BB. The quantitative results show that our method surpasses other approaches, highlighting its overall performance.
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Table 1: Comparison of methods across different super-resolution scales (PSNR(dB)). All methods are implemented on IPT for 4-bit quantization. w, a and a represent the bit width of weights, activations, and attentions.
#Bits
Co- Training
Method
Scale
Set 5
Set 14
B100
Urban100
(wlala)
Full Precision
32/32/32
x2
38.37
34,43
32.48
33.76
saseline-single
38.13
34.03
32.32
33.00
Minmax
37.16
32.78
31.62
30.64
PAMS
38.12
34.14
32.35
33.12
LSQ
4/4/4
X2
38.18
34.34
32.42
33,43
baseline-multi
38.25
34.29
32.43
33.46
Q-
38.30
34.35
32.45
33.60
TSQ-MTC
38,33
34,42
32.47
33.67
Full Precision
32/32/32
x3
34.81
30.85
29.38
29.49
baseline-single
34.58
30.59
29.23
28.91
Minmax
33.44
29.61
28.59
26,92
PAMS
34.52
30.59
29.22
28.91
LSQ
4/4/4
X3
34.59
30.71
29.30
29.17
baseline-mult
34.64
30.73
29.30
29.18
Q-
34.70
30.76
29.33
29.29
TSQ-MTC
34.73
30.80
29.35
29.40
Full Precision
32/32/32
X4
32.64
29.01
27.82
27.26
sanglene-single
32.37
28.79
27.67
26.76
Minmax
31.17
27.88
27.10
25.06
PAMS
32.31
28.76
27.65
26.67
LSQ
4/4/4
X4
32.43
28.88
27.73
26.88
baseline-multi
32.48
28.89
27.72
Q-VIT
32.57
28.91
27.75
26,92
TSQ-MTC
32.64
28.97
27.78
27.08
Table 2: Comparison of accuracy on different 4-bit quantized CNN models and modalities.
Full Precision
LSQ+
TSQ-MTC
Backbone
Modality
Top-1
Top-1
Top-1
All
95.37
96.73
97.28
ResNet-18
SAR
94.54
96.17
96.17
RGB
96.20
96.74
98.37
All
96.19
95.64
97.55
ResNet-34
SAR
93.99
92.90
96.17
RGB
98.37
98.37
98.91
All
94,55
95.64
96.73
ResNet-50
SAR
91.80
92.35
93.44
RGB
97.28
97.83
100.00
All
94.55
96.46
97.55
ResNet-101
SAR
91.80
93.44
96.72
RGB
97.28
99.46
98.37
6.2 MULTI-MODAL DATA CO-TRAINING
Tasks. In this experiment, focus on utilizing TSQ-MTC enhance the performance of 4-bit quantized shared shared-parameter CNN classification models for multi-modal data. Our objective is to further validate the versatility and applicability our TSQ-MTC. We consider the classification of different modalities distinct tasks and modify our CNN backbones into multi-heade and multi-tails architectures. The implementation details are provided Appendix A The tasks involve classifying ships and airplanes based on SAR and RGB images. We compare
method with the general quantization framework in Sec. 3.2 namely LSQ+ (Bhalgat et al. 2020). to assess effectiveness. Specifically.
ResNet- 18, Res ResNet-50, and ResNet- 101 (He et al. 2016) shared- parameter backbones by adding heads and tails to process each modality. All models, including the full precision network, are trained within the co-training framework.
Datasets. For experiments on multi-modi data co- co-training, we utilize a SAR-RGB dataset collected from open-source datasets. Details of the dataset are provided in Appendix

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Table 3: Ablation study TLMAQ for super-resolution tasks. All methods implemented
IPT for 4-bit quantization.
Method
Scale
Set 5
Set 14
B100
Urban100
Baseline-mule
38.25
34.29
32.43
33.46
w/ TLMAQ
38.32
34.38
32,46
33.59
w/ TLMAQ & Init
38.34
34.34
32.47
33.64
w/ TLMAQ Init & SLLD (TSQ-MTC)
38.33
34.42
32.47
33.67
Baseline-multt
34.64
30.73
29.30
29.18
w/ TLMAQ
34.72
30.77
29.34
29.28
w/ TLMAQ & Init
34.72
30.80
29.35
29.36
TLMAQ Init & SLLD (TSQ-MTC)
34.73
30.80
29.35
29.40
Baseline-multt
32.48
28.89
27.72
26.85
w/ TLMAQ
32.60
28.94
27.76
26.95
w/ TLMAQ & Init
32.61
28.97
27.78
27.06
TLMAQ Init & SLLD (TSQ-MTC)
32.64
28.97
27.78
27.08
Table Ablation study on SLLD. All methods are implemented on IPT for 4-bit quantization.
Distillation Loss
None
Cross Entropy Cosine Similarity SSIM (TSQ-MTC)
PSNR
37.596
37.698
37.713
37.893
Results. Our quantitative results, shown in Table indicate that our method consistently outper- forms LSQ+ across various backbones. Furthermore, method performs better deeper models, such as ResNet-34 (97. 99.555 of all) and ResNet-50 (100% of RGB). Mismatching quantization scales may accumulate with deeper models, leading significant performance degradation. Our approach effectively mitigates this issue, resulting in superior performance. It is worth noting that overfitting may occur when the network becomes too deep. As result, the multi-task ResNet-101 trained by LSQ+ performs better in classifying RGB images but performs poorly on SAR images.
6.3 ABLATION STUDY
To demonstrate the effectiveness of the techniques in our TSQ-MTC framework, we conduct abla- tion studies on TLMAQ and SLLD.
Effectiveness of TSQ-MTC. For TLMAQ. ablation studies are conducted on single-mont moddll task- related data co-training with 4-bit quantized IPT. We validate the effectiveness of TLMAQ, task- specific initialization, and SLLD. The resulting PSNR values of SR tasks are presented in Table Results show that TLMAQ significantly improves performance compared to the baseline-multi, and the model achieves further improvement after incorporating the initialization strategy. Furthermore, with SLLD, our model exhibits noticeable improvements in specific metrics.
Comparison of different distillation losses. To further validate the effectiveness of SLLD, we use baseline-single for comparison. We train our models DIV2K (Timofte et al.) (017) dataset in x2 SR task for 150 epochs and evaluate the performance of different loss functions on Set5 benchmark, including cross-entropy, cosine similarity, and structural similarity. As shown in Table the SLLD outperforms baseline and other loss functions, with 37.893dB PSNR, demonstrating the effectiveness of the structural constraints imposed on the attention module.
CONCLUSION
This paper proposes Task-Specific Scales Quantization for Multi-Task Co- Co-Training (TSQ-MTC). This effective method addresses the bottleneck of combining co-training and quantization-awe training. Specifically, TSQ-MTC leverages a task-specific learnable multi-scale activation quantizer (TLMAQ) to enhance the representational ability quantized features across different tasks and structure-based layer-by-layer distillation (SLLD) technique to preserve the information from the full-precision features. Through comprehensive experiments, we demonstrate the effectiveness of TSQ-MTC and its components, TLMAQ and SLLD. Especially, quantized low-level visual foundation model based on IPT, which outperforms existing state-of-the- art quantized super-resolution networks, demonstrating the superior performance of TSQ-MTC.
successfully achieved 4-bit
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ACKNOWLEDGEMENTS
The work was supported by the National Key Research and Development Program of China (Grant No. 2023YFC3306401) This research also supported by the Beijing Natural Sci- ence Foundation L244043 and L223024, the Zhejiang Provincial Natural Science Foundation of China under Grant LD24F020007, the National Natural Science Foundation of China under Grant 62076016, 62406298, and 623B2016, the "One Thousand Plan" projects in Jiangxi Province (xsq2023102268) and the Fundamental Research Funds for the Central Universities, Communi- cation University of China (CUC24XT07).
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APPENDIX
A IMPLEMENTATION DETAILS
Baseline-multi and baseline-single. Both baseline-multi and baseline-single are quantized by LSQ+ (Bhalgat et al., 2020). The difference is that baseline-multi employs the co-training strategy used in IPT to learn a shared representation across different low-level vision tasks. On the ImageNet (Deng et al. 2009) dataset, baseline-multi is trained for 50 epochs. In contrast, baseline-single con- ducts single-task quantization training on the DIV2K (Timofte et al. (017) dataset for each SR scale. Following Hong et al. (002). baseline-single is trained for 900 epochs until convergence, achieving performance that is consistent with existing single-task super- super-resolution quantization models (Qn
et al. 2024).
TSQ-MTC on IPT. TSQ-MTC is employed to quantize the IPT model for single-modal task-related data co-training. Py PoTch is used to implement both our baselines and TSQ-MTC. The training is conducted on NVIDIA Tesla A100 GPUs, each with 80 GB memory, using the Adam optimizer with 31 = 0.9 and S2 0.999. We initialize all the quantized models from full-precision pre-trained weights and perform co-training for 50 epochs on the ImageNet (Deng et al. 2009) dataset. The learning rate starts at 5 x 10 and decays to 2 x 10-5 throughout the training, with a batch size of 225. SSIM the loss for SLLD, with a weight coefficient of 0.01. To ensure a fair comparison with the full-precision IPT model, for each super-resolution task, we perform 30 epochs of single-task fine-tuning on the DIV2K dataset (Timofte et al.) 2017) respectively, with a learning rate of 1e-6.
TSQ-MTC on CNNs. We use ResNet-18. 18, ResNet-34, ResNet-50, and ResNet- as shared- parameter backbones for multi-modal data co-training. We add multiple task-specific heads (a convolutional block) before the backbone to process the input data. After the backbone, multi- ple task spec-fpe fully connected layers are used for output classification. The training is conducted on 2 NVIDIA Tesla A5000 GPUs using the Adam optimizer, initialized with full precisson weights. We do co-training for 50 epochs on our SAR-RGB dataset with an initial learning rate of 1 x 10-5 multiplied by 0.1 epoch 25.
B ADDITIONAL EXPERIMENTAL RESULTS ON SINGAL-MODAL DATA CO-TRAINING
Quantitative results. As a multi-task model, the quantized IPT is also evaluated on denoising and deraining tasks. We compare our approach against Minmax, PAMS (Li et al. (020), LSQ (Lsser et al. 2019), QiT (Li et al. 2022). ) and baseline-multi. Our method consistently demonstrates its superoor performance, as shown in Table the denoising task and Table for the deraining task. Specifically, the results show that the performance gap among different QAT methods is relatively small for the denoising and deraining tasks. For instance, in the denoising task (a = 30), the difference of PSNR between TSQ-MTC and baseline-multi is only 0.01 on CBSD68 dataset. This is due the quantization scales being more suitable for these two tasks. Nevertheless, our TSQ-MTC still achieves the best performance.
Qualitative results. We visualize the 4x super-resolution task results on three selected images from the Urban I 00 (Huang et al. (2015) dataset in Fig. [ It can be observed that TSQ-MTC better restores the fine details of the original image than other methods.
Comparison with the state-of-the-art quantization methods for super-resolution. We also com- pare our approach against several single-task quantized SR models, such as PAMS (Li et al. 2020). CADyQ (Hong et al. 2022) and QuantSR-T (Qin et al., 2024). Note that the backbone for PAMS and CADyQ is SetesNet (referred to as PAMS-C and CADyQ-C), while the backbone for QuantSR is SwinIR-S (referred to as QuantSR-T), We also add the results of the IPT quantized by PAMS on super-resolutin clear performance advantage compared to single-task quantized super- reporrresolution models. For exam- plc, for the x2 super-resolution task on the Urban 100 benchmark, TSQ-MTC achieves a PSNR of 33.67dB, significantly surpassing the performance of QuantSR-T, which recorded 32. 22.0
tasks (referred to as PAMS-IPT), As shown in Table C our method exhibits a
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Table A: Comparison of methods for denoising tasks (PSNR(dB)). All methods implemented
IPT for 4-bit quantization. w, a and a represent the bit width of weights, activations, and attentions.
#Bits
Method
Task
CBSD68
Urban100
(w/o/a)
Full Precision
4/4/4
Denoising (a 30)
32.40
34.10
Minmax
30.30
30.13
PAMS
32.37
33.92
LSQ
32.39
34.00
4/4/4
Denoising (a 30)
baseline-multi
32.39
33,99
Q-VIT
32.40
34.02
TSQ-MTC
32.40
34.05
Full Precision
4/4/4
Denoising (a 50)
29.99
31.67
Minmax
28.89
28.68
PAMS
29.97
31.47
LSQ
29.97
31.56
4/4/4
Denoising (a 50)
baseline-multi
29.98
31.54
Q-ViT
29.98
31.59
TSQ-MTC
29.99
31.61
Table B: Comparison of methods for deraining task (PSNR(dB)). All methods are implemented on IPT for 4-bit quantization. w, a, and represent the bit width of weights, activations, and attentions.
#Bits
Method
Rain
(wlala)
Full Precision
4/4/4
41.98
Minmax
35.91
PAMS
41.46
LSQ
41.87
4/4/4
baseline-multi
41.89
Q-ViT
41.98
TSQ-MTC
42.02
Table C: Comparison of methods across x2 and x4 super-resolution scales (PSNR(dB)). w, a and represent the bit width of weights, activations, and attentions.
#Bits (wlola)
Method
Backbone
Training
Scale
Set
Set 14
B100
Urban100
PAMS-C
SRResNet
single-task
37.67
33.19
31.90
31.10
CADyQ-C
SRResNet
single-task
37.58
33.14
31.87
30.94
QuantSR-T
SwinIR
single-task
4/4/4
x
38.10
33.65
32.21
32.20
PAMS-IPT
IPT
multi-task
38.12
34.14
32.35
33.12
TSQ-MTC
IPT
multi-task
38.33
34.42
32.47
33.67
PAMS-C
SRResNet
single-task
31.59
28.20
27.32
25.32
CADyQ-C
SRResNet
single-task
31.48
28.05
27.21
25.09
QuantSR-T
SwinIR
single-task
4/4/4
x4
32.18
28.63
27.59
26.11
PAMS-IPT
IPT
multi-task
32.31
28.76
27.65
26.67
TSQ-MTC
IPT
multi-task
32.64
28.97
27.78
27.08
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Published as a conference paper at ICLR 2025
Table D: Ablation study TLMAQ for denoising tasks (PSNR(dB)). All methods are implemented on IPT for 4-bit quantization.
Method
Task
CBSD68
Urban106
Baseline-multt
32.39
33.99
w/ TLMAQ
32.40
34.02
Denoising (a 30)
w/ TLMMQ & Init
32.40
34.04
w/ TLMAQ & Init & SLLD (TSQ-MTC)
32.40
34.05
Baseline-multe
29.98
31.54
w/ TLMAQ
29.99
31.57
Denoising (a = 50)
TLMAQ Init
29.99
31.59
w/ TLMAQ & Init & SLLD (TSQ-MTC)
29.99
31.61
Table E: Ablation study on TLMAQ for deraining task (PSNR(dB)). All methods are implemented
on IPT for 4-bit quantization.
Method
Rain100L Deraining
Task
Baseline-multi
41.89
w/ TLMAQ
41.99
w/ TLMAQ & Init
41.98
w/ TLMAQ & Init & SLLD (TSQ-MTC)
42.02
C ABLATION STUDY ON TLMAQ FOR DENOISING AND DERAINING
Ablation experiments also performed on the denoising task and deraining task with 4-bit quan- tized IPT within the single-modal task-related data co-training setup. We evaluate the effectiveness of TLMAQ, task-specific initialization, and SLLD. The results of the denoising task shown in Ta- D while the results of deraining task are presented in Table We can see that TLMAQ improves performance over the mastline-multi, with more gains achieved through specifc initialization. The addition of SLLD provides further boost, resulting in the best performance across both tasks.
D EFFECTIVENESS OF SLLD
We visualize the distributions of full-precision and quantized attention results in the 4-bit quantized IPT. comparing models trained with and without SLLD, shown in Fig. B It can be observed that after adding SLLD, the distribution of quantized features aligns more closely with that of full- precision features, demonstrating the effectiveness of structural loss in preserving full-precision feature information.
E COMPRESSION RATIO
In F we present the parameter counts and operations (i.e., Ops) of the baseline-multi and the IPT trained by TSQ-MTC, as well the fuel-precision model, along with their PSNR performances on the 100 benchmark for x2 super-resolution task. For the super-resolution, we do not quantize the input and output layers of the model. Additionally, the upsampling layer at the tail's end is not quantized. It can be observed that baseline-multi and TSQ-MTC successfully achieve significant accelcration, with a compression ratio of 87.5%. Notably, TSQ-MTC achieves better performance by introducing small number of additional activation quantizer scales, improving the PSNR from 33.46dB to 33.67dB, further narrowing the gap with the full-precision model. fact, the parameter count of TSQ-MTC is almost identical that of the baseline-multi, with merely an increase of 0.04M, demonstrating the efficiency of our method.
F DETAILS OF SAR-RGB DATASET
Due to the fact that existing classification datasets primarily contain a single modality and consider- ing the significant quality differences between SAR and RGB datasets, organize dataset suitable
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Published as a conference paper at ICLR 2025
GT (PSNR)
Minmax (17.22)
PAMS (20.50)
LSQ (20.93)
BM (20.80)
TSQ-MTC (21.42)
(a)
Minmax (24.73)
PAMS (25.89)
LSQ (26.07)
BM (26.09)
TSQ-MTC (26.21)
(PSNR)
(b)
GT (PSNR)
Minmax (25.95)
PAMS (27.62)
LSQ (27.98)
BM (27.87)
TSQ-MTC (28.34)
(c)
Figure A: Visualization of results for 4x super-resolution task (on selected images of Urban 00).
Layer 5 Feature Distribution
Layar Feature Distribution
Layer 35 Feature Distribution
Layer 35 Feature Distr bution
30000
34009
1400
7500
15030
1793
13009
1000
8009
1500
1900
1050
300
300
Attention Value wnoo SLLD
Attention Value w/ SLLD
Attertion Value wio SLLD
Attention Value el SLLD
Figure B: Histogram of features produced by general attention calculation (orange) and quantized calculation (blue) from selected attention layers in models with and without SLLD, It can be seen that SLLD can reduce the information distortion caused by quantization.
for the tasks described in this paper from open-source datasets. We manually segment and annotate these images, ultimately creating a high-quality dataset with aligned categories and consistent quan- tities across the two modalities. Some sample instances illustrated in Fig. C Specifically,
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Published as a conference paper at ICLR 2025
Table F: Efficieney comparison in terms of Bit Width, Parameter Count, Ops, and PSNR for x2 super-resolution on Urban100 (Huang et al. 2015). The input image has shape of (1, 3, 48, 48). w and represent the bit width of weights and activations.
#Bits
Params (M)
Ops (G)
Set14
Urban100
Method
(wlada)
(aRatio)
(aRatio)
PSNR
PSNR
Full Precision
32/32/32
115.31 (10.0%%
33.06 (10.0%%
34.43
33.76
baseline-muul
4/4/4
14.34 (187.6%)
34.29
33.46
4.14 (187.5%)
TSQ-MTC
4/4/4
14.38 (187.5%)
34.42
33.67
Aireraft carrier
Container ship
DFC Destroyer, Frigate, Cruiser
Amphibious
Bulk cargo
Tanker
Airliner
Transport plane
Figure C: Visualization of the SAR-RGB dataset.
SAR-RGE RGB dataset is sourced from several open source datasets (Hou et al. 2020: Huang et al,
2017 Di al., 2021 Xia et al. 2018), collected from Google Earth (satellite), TerraSAl RadarSat, Capella, a, Gaofen-3, consisting of 1709 instances within RGB categories and corresponding SAR categories.
G ACCURACY DURING TRAINING OF MULTI-TASK CNN MODEL
We visualize the validation accuracy during the training of the multi-task ResNer-34 quantized with TSQ-MTC and I.SQ+, as shown in Fig. D evident that in the later stage of training, our method significantly outperforms both LSQ+ and the full-precision model, while the models trained with LSQ+ underperform compared to the full-precision model for period time.
It is worth noting that the convergence of the full-precision model directly impacts the quantization performance of TSQ-MTC. Specifically, we utilize TSQ-MTC to quantize ResNet model trained for only 30 epochs in full precision, while the models employed in main experiments trained for 50 epochs. The accuracy of the model after quantization not significantly different from that of the same model quantized by LSQ+.
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Published as a conference paper at ICLR 2025
Validation Accuracy during Training
Accuracy
0.50
Full Precision
Epochs
Figure D: Validation accuracy during training of quantized multi-task ResNet-34.
S48
SAR
SAR
R68
0.0228
RGB
0 0206
RGB
0,0219
Figure E: Visualization the task-related scaling factors for the activation quantizer in of
quantized multi-tast ResNet-34. It is noted that the scaling factors for SAR (green) and RGB (blue) show distinct differences.
H VISUALIZATION OF THE TASK-RELATED SCALING FACTORS OF MULTI-TASK CNN MODEL
We conduct visual analysis of the quantization scales for the CNN model trained with TSQ- MTC. The scaling factors of the three modules quantized multi-task ResNet-34 network, fea- uure_extractor2 ....o...cc.. (et.cc.feattureeeettctttto...... 2.t.ucttor....com.22.. act, feature featuree_xtracttooe 3.t.racoor...2ccoomm 3.2. conoon2..ct, visualized Fig. E We also observed pronounced differences in scales between different tasks, fur- ther supporting the validity of approach. For example. in scaling factor for the SAR task is 1.23 times that of the RGB task, which can lead to significant changes in the quantization mapping.
1. I.cov22..ct the
I EXPERIMENTAL RESULTS ON HIGH-LEVEL TASKS
Task and dataset. To verify the effectiveness of TSQ-MTC in more scenarios, conduct exper- iments on high-level vision tasks. We train multi-task model on the NYUD-V2 (Silberman et al., 2012) dataset based on the code DiSparse (Sun et al. 2022) within co-training. The tasks include semantic segmentation for RGB input and surface normal prediction for RGBD input. Each task assigned corresponding input head and output tail, while sharing ResNet-based backbone. We first train converged full-precision model to initialize the 4-bit quantized model. Our evalua- tion compares TSQ-MTC with baseline-multi (LSQ+) using four evaluation metrics: mloU, Pixel Accuracy (Pixel Acc), Angle Mean Error (Mean Er) and Angle Median Error (Median Err).
Results. The results in high-level vision tasks are shown in Table G It can be observed that our method significantly improves the performance of the semantic segmentation task (mloU increased by 0.25%) while maintaining the performance of the surface normal prediction task. This demon- strates that method effectively alleviates quantization conflicts between tasks.
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Published as a conference paper at ICLR 2025
Table G: Comparison of methods high-level vision tasks for 4-bit quantization.
Semantic Segmentation
Surface Normal Prediction
Method
m
Pixel Ace f
Mean Err
Median Err +
Full Precision
23.15
55.18
17.05
15.85
baseline-multi
23.22
55.16
16.97
15.17
TSQ-MTC
23.47
55.27
16.97
15.16
J EXPERIMENTAL RESULTS ON CROSS-DOMAIN DATASET
Task and dataset. We also conduct experiments to explore the effectiveness of TSQ-MTC on cross-domain datasets. Specifically, select subset of DomainNet (Peng et al.) 2019) for cross- domain classification tasks, which includes 13504 instances within four domains and ten categories for each domain. Consistent with the experiments in the main paper, we follow the DomainNet to use ResNet-101 101 (He et al. 2016) as the backbone and consider the classification of the four domains as four distinct tasks, adding corresponding heads and tails to process each modality. We compare TSQ-MTC with LSQ+ (Bhalgat et al. 2020).
Results. Experimental results show that compared to the full-precision model, both TSQ-MTC and LSQ+ achieved effective quantization. However, method does not exhibit significant advantages. To investigate this finding, we further examine the quantization scales for different tasks within the network and found that the quantization scales of the same activation quantizer almost the same. One possible explanation is that for cross-domain data within the same task, the feature distribution differences negligible, and different tasks do not exert opposing effects the scales. Using single scale might actually lead to faster convergence.
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Reviews:
[{"review_id": "RZ41dOAsqI", "reviewer": "Reviewer_LoRG", "summary": "The study finds that directly applying co-training to existing QAT methods significantly degrades performance. The main issue identified is the inadequacy of activation quantization scales in the co-training framework. To address this, the authors propose a Task-Specific Scales Quantization method suitable for multi-task co-training.", "questions": "See weakness.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. This work effectively incorporates quantization-aware training into co-training and significantly reduces the performance gap between multi-task co-trained models and their 4-bit quantized counterparts.\n2. The authors design task-specific learnable multi-scale activation quantizer and SLLD to solve the issues of naive integration.\n3. From the experimental results, it appears that the author's techniques are effective.", "weaknesses": "1. In table 1, quantitative results for super-resolution tasks are shown. But I am still curious about the results of deraining and denoising tasks.\n2. How about the parameters change of your method?\n3. Can you provide the some comparisons with some SOTA single task methods to further demonstrate the superiority of your method?", "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": "6iUTf2hEGc", "reviewer": "Reviewer_gsJW", "summary": "This paper proposes Task-Specific Scales Quantization for Multi-Task Co-Training (TSQ-MTC) to address the performance degradation issue of existing quantization-aware training (QAT) methods when integrated with co-training. The proposed method introduces a task-specific learnable multi-scale activation quantizer (TLMAQ) to enrich the representational ability of shared features across different tasks and a structure-based layer-by-layer distillation (SLLD) to ensure that the quantized features effectively preserve the information from their full-precision counterparts. Extensive experiments on two co-training data scenarios demonstrate the effectiveness of TSQ-MTC, which achieves a 4-bit quantized low-level visual foundation model based on IPT with a PSNR comparable to the full-precision model and a 7.99× compression ratio in the ×4 super-resolution task on the Set5 benchmark.", "questions": "As listed in Weaknesses, there are two questions:\n1. Could the authors provide the computational cost of training and inference compared to existing QAT methods?\n2. Could the authors report the variance of the results of the proposed SLLD method in the ablation studies?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The authors provide a comprehensive evaluation of the challenges of the task, i.e., directly integrating multi-task co-training with QAT. This helps clarify the bottleneck of existing QAT methods and motivates the proposed TSQ-MTC method to address the performance degradation issue.\n\n2. The proposed TSQ-MTC method introduces two novel components, TLMAQ and SLLD, to enhance the representational ability of shared features across different tasks and preserve the information from full-precision features in deeper layers of the Transformer model. \n\n3. The experimental evaluation across the main text and appendices provides detailed insights into the effectiveness and versatility of the proposed TSQ-MTC method in two co-training data scenarios.", "weaknesses": "1. This paper referred the challenge of computational and memory overhead of co-trained models (Lines 047-049), but the computational complexity and efficiency of the proposed TSQ-MTC method are not discussed in detail. \n\n2. The authors detailedly analysis the proposed SLLD in Table 4 and Figure B. However, according to the ablation studies provided in Table 3, Table D, Table E, the proposed SLLD has minor improvements in performance given the potential for minor fluctuations in experimental results.", "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": "q1ivd4Jlm7", "reviewer": "Reviewer_icGh", "summary": "This paper presents a novel method for multi-task network quantization-aware training, addressing performance degradation caused by model quantization. The approach incorporates two techniques: (1) Task-Specific Learnable Multi-Scale Activation Quantizer (TLMAQ): Addressing the scale conflict in quantizing diverse task features, improving the quantized model's representational capabilities across tasks. (2) Structure-based Layer-by-Layer Distillation (SLLD): Strengthening full-precision model supervision over quantized models, reducing information distortion from quantization.", "questions": "Apart from the points (2) and (3) outlined in the \"Weaknesses\" section, the following are additional concerns:\n(1) While the model design is good and interesting, the model seems a little complex. Would it be possible to provide some comparative analyses regarding the model's speed and complexity?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) The proposed method effectively addresses the scale discrepancies among multi-task features, aligning with intuitive expectations.\n(2) The novelty is good, the introduction of task-aware quantization into multi-task learning represents an innovative approach.\n(3) Method is architecture-agnostic, featuring a wide range of applicability.\n(4) originality, quality, clarity, and significance: This paper demonstrates good originality, is well-written, and clearly communicates ideas. Furthermore, the application of quantization to enhance model efficiency holds significant importance for multi-task learning", "weaknesses": "(1) There is a minor error in Section 2.1 - regarding the order of description for the \"single-modal task-related data scenario\" and the \"multi-modal data scenario\" are misaligned with the order of \"former scenario\" and \"latter scenario\". \n(2) The paper only considered the low-level multi-task scenarios; however, However, it lacks effective exploration of high-level visual tasks. Specifically, Can the proposed method still maintain effectiveness when processing high-level visual multitasking data such as NYUD-v2, Pascal Context and Cityscapes.\n(3) The experiment involved a comparison of model quantization techniques, including LSQ (2019) and PAMS (2020). Nevertheless, a comparison with the most recent Quantization-Aware Training (QAT) methods is conspicuously absent.", "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)