Model merging based on task vectors, i.e., the parameter differences between fine-tuned models and a shared base model, provides an efficient way to integrate multiple task-specific models into a multitask model without retraining. Recent works have endeavored to address the conflicts between task vectors, one of the significant challenges faced by model merging, through sparsification; however, two issues significantly limit their performance: high parameter overlap and unbalanced weight distribution. To address these issues, we propose a simple yet effective framework called CABS (Conflict-Aware and Balanced Sparsification), consisting of Conflict-Aware Sparsification (CA) and Balanced Sparsification (BS). CA reduces parameter overlap by applying masks during sequential pruning, ensuring that each task vector retains distinct, non-overlapping parameters. BS leverages $n$:$m$ pruning to preserve critical weights while maintaining an even distribution across layers. Our comprehensive experiments demonstrate that CABS outperforms state-of-the-art methods across diverse tasks and model sizes.

Deep neural network (DNN) models have become increasingly crucial components of intelligent software systems. However, training a DNN model is typically expensive in terms of both time and computational resources. To address this issue, recent research has focused on reusing existing DNN models - borrowing the concept of software reuse in software engineering. However, reusing an entire model could cause extra overhead or inherit the weaknesses from the undesired functionalities. Hence, existing work proposes to decompose an already trained model into modules, i.e., modularizing-after-training, to enable module reuse. Since the trained models are not built for modularization, modularizing-after-training may incur huge overhead and model accuracy loss. In this paper, we propose a novel approach that incorporates modularization into the model training process, i.e., modularizing-while-training (MwT). We train a model to be structurally modular through two loss functions that optimize intra-module cohesion and inter-module coupling. We have implemented the proposed approach for modularizing Convolutional Neural Network (CNN) models. The evaluation results on representative models demonstrate that MwT outperforms the existing state-of-the-art modularizing-after-training approach. Specifically, the accuracy loss caused by MwT is only 1.13 percentage points, which is less than that of the existing approach. The kernel retention rate of the modules generated by MwT is only 14.58%, with a reduction of 74.31% over the existing approach. Furthermore, the total time cost required for training and modularizing is only 108 minutes, which is half the time required by the existing approach. Our work demonstrates that MwT is a new and more effective paradigm for realizing DNN model modularization, offering a fresh perspective on achieving model reuse.

Air pollution is a major global risk to human health and environment. Particle matter (PM) with diameters less than 2.5 micrometers (PM2.5) is more harmful to human health than other air pollutants because it can penetrate deeply into lungs and damage human respiratory system. A new imagebased deep feature analysis method is presented in this paper for PM2.5concentration estimation. Firstly, low level and high level features are extracted from images and their spectrums by a deep learning neural network, and then regression models are created using the extracted deep features to estimate the PM2.5concentrations, which are future refined by the collected weather information. The proposed method was evaluated using a PM2.5dataset with 1460 photos and the experimental results demonstrated that our method outperformed other state-of-the-art methods.