Convolutional Neural Networks (CNNs) suffer from different issues, such as computational complexity and the number of parameters. In recent years pruning techniques are employed to reduce the number of operations and model size in CNNs. Different pruning methods are proposed, which are based on pruning the connections, channels, and filters. Various techniques and tricks accompany pruning methods, and there is not a unifying framework to model all the pruning methods. In this paper pruning methods are investigated, and a general model which is contained the majority of pruning techniques is proposed. The advantages and disadvantages of the pruning methods can be identified, and all of them can be summarized under this model. The final goal of this model is to provide a general approach for all of the pruning methods with different structures and applications.
Istituto Italiano di Tecnologia（意大利理工学院）；Università di Genoa（热那亚大学）；NVIDIA AI Technology Center（英伟达人工智能技术中心）；Università Cattolica del Sacro Cuore（圣心天主教大学）
Grapevine winter pruning is a complex task, that requires skilled workers to execute it correctly. The complexity of this task is also the reason why it is time consuming. Considering that this operation takes about 80-120 hours/ha to be completed, and therefore is even more crucial in large-size vineyards, an automated system can help to speed up the process. To this end, this paper presents a novel multidisciplinary approach that tackles this challenging task by performing object segmentation on grapevine images, used to create a representative model of the grapevine plants. Second, a set of…
University of Tennessee（田纳西大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；Sun Yat-Sen University（中山大学）
Most existing channel pruning methods formulate the pruning task from a perspective of inefficiency reduction which iteratively rank and remove the least important filters, or find the set of filters that minimizes some reconstruction errors after pruning. In this work, we investigate the channel pruning from a new perspective with statistical modeling. We hypothesize that the number of filters at a certain layer reflects the level of 'redundancy' in that layer and thus formulate the pruning problem from the aspect of redundancy reduction. Based on both theoretic analysis and empirical studies…
Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler
East China Normal University（华东师范大学）；Xiamen University（厦门大学）；Beihang University（北京航空航天大学）；Zhongguancun Laboratory（中关村实验室）；Tencent（腾讯）；University of Sheffield（谢菲尔德大学）；University at Buffalo（布法罗大学）
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；College of Computer Science, Sichuan University（四川大学计算机学院）
Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision algorithms. However, they are still rarely deployed on battery-powered mobile devices, such as smartphones and wearable gadgets, where vision algorithms can enable many revolutionary real-world applications. The key limiting factor is the high energy consumption of CNN processing due to its high computational complexity. While there are many previous efforts that try to reduce the CNN model size or amount of computation, we find that they do not necessarily result in lower energy consumption, and th…
Spectral Evolution-Guided Token Pruning in Multimodal Large Language Models
School of Software Technology, Zhejiang University（浙江大学软件学院）；Zhejiang Key Laboratory of Digital-Intelligence Service Technology（浙江省数字化服务技术重点实验室）；The University of Queensland（昆士兰大学）；Singapore Management University（新加坡管理大学）；The University of Southern Queensland（南昆士兰大学）
RS-Prune: Training-Free Data Pruning at High Ratios for Efficient Remote Sensing Diffusion Foundation Models
Department of Earth System Science, Tsinghua University（清华大学地球系统科学系）；School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）；National Supercomputing Center in Shenzhen（深圳国家超算中心）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生学院）；Department of Geography, The University of Hong Kong（香港大学地理系）；Wangxuan Institute of Computer Technology, Peking University（北京大学王轩计算机技术研究所）
Kernel pruning methods have been proposed to speed up, simplify, and improve explanation of convolutional neural network (CNN) models. However, the effectiveness of a simplified model is often below the original one. In this letter, we present new methods based on objective and subjective relevance criteria for kernel elimination in a layer-by-layer fashion. During the process, a CNN model is retrained only when the current layer is entirely simplified, by adjusting the weights from the next layer to the first one and preserving weights of subsequent layers not involved in the process. We call…
Can Visual Input Be Compressed? A Visual Token Compression Benchmark for Large Multimodal Models
Shandong University（山东大学）；Shenzhen University of Information Technology（深圳信息职业技术大学）；Tongji University（同济大学）；University of Science and Technology of China（中国科学技术大学）；Sichuan University（四川大学）；University of Notre Dame（圣母大学）；Ludwig Maximilian University of Munich（慕尼黑路德维希-马克西米利安大学）；EB Tech Co., Ltd.（EB科技有限公司）
Efficient Unstructured Pruning of Mamba State-Space Models for Resource-Constrained Environments
Department of Computer Science, Iowa State University（计算机科学系，爱荷华州立大学）；Department of Civil, Construction and Environmental Engineering, Iowa State University（土木、建设与环境工程系，爱荷华州立大学）
LiePrune: Lie Group and Quantum Geometric Dual Representation for One-Shot Structured Pruning of Quantum Neural Networks
School of Computer, Jiangsu University of Science and Technology（江苏科技大学计算机学院）；Department of Electrical and Computer Engineering, University of Nevada, Las Vegas（内华达大学拉斯维加斯分校电子与计算机工程系）
Streaming-dLLM: Accelerating Diffusion LLMs via Suffix Pruning and Dynamic Decoding
School of information（信息学院）；Electronics, Beijing Institute of Technology, Beijing（电子学院，北京理工大学，北京）；Department of Computer Science, City University of Hong Kong, Hong Kong（计算机科学系，香港城市大学，香港）；School of Computer Science, Wuhan University, Wuhan（计算机科学学院，武汉大学，武汉）；School of Economics（经济学院）；Management, Communication University of China, Beijing（管理学院，中国传媒大学，北京）；School of Science（科学学院）；Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen（工程学院，香港中文大学（深圳），深圳）
Northeastern University（东北大学）；College of William and Mary（威廉与玛丽学院）；Rice University（莱斯大学）；Michigan State University（密歇根州立大学）
With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed. Prior methods towards this goal, including model compression and network architecture search (NAS), are largely performed independently and do not fully consider compiler-level optimizations which is a must-do for mobile acceleration. In this work, we first propose (i) a general category of fine-grained structured pruning applicable to various DNN layers, and (ii) a comprehensive, compiler automatic code generation fram…
Sparse ActionGen: Accelerating Diffusion Policy with Real-time Pruning
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Department of Computer Science（计算机科学系）
The University of Hong Kong（香港大学）；MPI Informatics Saarland Informatics Campus（马克斯·普朗克信息学研究所萨尔信息学园区）；Wuhan University（武汉大学）
Motion coherence is an important clue for distinguishing true correspondences from false ones. Modeling motion coherence on sparse putative correspondences is challenging due to their sparsity and uneven distributions. Existing works on motion coherence are sensitive to parameter settings and have difficulty in dealing with complex motion patterns. In this paper, we introduce a network called Laplacian Motion Coherence Network (LMCNet) to learn motion coherence property for correspondence pruning. We propose a novel formulation of fitting coherent motions with a smooth function on a graph of c…
The cultivation of orchard meadows provides an ecological benefit for biodiversity, which is significantly higher than in intensively cultivated orchards. The goal of this research is to create a tree model to automatically determine possible pruning points for stand-alone trees within meadows. The algorithm which is presented here is capable of building a skeleton model based on a pre-segmented photogrammetric 3D point cloud. Good results were achieved in assigning the points to their leading branches and building a virtual tree model, reaching an overall accuracy of 95.19 %. This model provi…
Isfahan University of Technology（伊斯法罕理工大学）；University of Michigan（密歇根大学）
Retinal vessel information is helpful in retinal disease screening and diagnosis. Retinal vessel segmentation provides useful information about vessels and can be used by physicians during intraocular surgery and retinal diagnostic operations. Convolutional neural networks (CNNs) are powerful tools for classification and segmentation of medical images. Complexity of CNNs makes it difficult to implement them in portable devices such as binocular indirect ophthalmoscopes. In this paper a simplification approach is proposed for CNNs based on combination of quantization and pruning. Fully connecte…
MiCo: Mutual Information Coverage Optimization through Semantic Erasure Modeling for Efficient MLLM Inference
Peking University（北京大学）；University of Electronic Science and Technology of China（电子科技大学）；Nanyang Technological University（南洋理工大学）；The University of Hong Kong（香港大学）；New York University（纽约大学）
ForestPrune: High-ratio Visual Token Compression for Video Multimodal Large Language Models via Spatial-Temporal Forest Modeling
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（厦门大学多媒体可信感知与高效计算教育部重点实验室）；National University of Defense Technology（国防科技大学）
KTH Royal Institute of Technology（瑞典皇家理工学院）；Toshiba Corporate Research and Development Center（东芝公司研发中心）
This paper presents an automatic network adaptation method that finds a ConvNet structure well-suited to a given target task, e.g., image classification, for efficiency as well as accuracy in transfer learning. We call the concept target-aware transfer learning. Given only small-scale labeled data, and starting from an ImageNet pre-trained network, we exploit a scheme of removing its potential redundancy for the target task through iterative operations of filter-wise pruning and network optimization. The basic motivation is that compact networks are on one hand more efficient and should also b…
To address the limitations of existing magnitude-based pruning algorithms in cases where model weights or activations are of large and similar magnitude, we propose a novel perspective to discover parameter redundancy among channels and accelerate deep CNNs via channel pruning. Precisely, we argue that channels revealing similar feature information have functional overlap and that most channels within each such similarity group can be removed without compromising model's representational power. After deriving an effective metric for evaluating channel similarity through probabilistic modeling,…
Time-series modeling has shown great promise in recent studies using the latest deep learning algorithms such as LSTM (Long Short-Term Memory). These studies primarily focused on watershed-scale rainfall-runoff modeling or streamflow forecasting, but the majority of them only considered a single watershed as a unit. Although this simplification is very effective, it does not take into account spatial information, which could result in significant errors in large watersheds. Several studies investigated the use of GNN (Graph Neural Networks) for data integration by decomposing a large watershed…
Isfahan University of Technology（伊斯法罕理工大学）；McMaster University（麦克马斯特大学）
Convolutional neural networks (CNNs) have a large number of variables and hence suffer from a complexity problem for their implementation. Different methods and techniques have developed to alleviate the problem of CNN's complexity, such as quantization, pruning, etc. Among the different simplification methods, computation in the Fourier domain is regarded as a new paradigm for the acceleration of CNNs. Recent studies on Fast Fourier Transform (FFT) based CNN aiming at simplifying the computations required for FFT. However, there is a lot of space for working on the reduction of the computatio…
