Technology Category
Computer Vision: Large Vision ModelsMachine Learning: Deep Neural Architectures and Foundation ModelsNatural Language Processing: Learning & Optimization for NLP
Partial Channel Network: Compute Fewer, Perform Better
🤖 AI Summary
High redundancy in deep networks impedes the simultaneous achievement of efficiency and accuracy. To address this, we propose the Partial Channel Mechanism (PCM), which dynamically partitions feature map channels and assigns heterogeneous operations—such as convolution, attention, and pooling—to distinct channel subsets, enabling fine-grained computational resource allocation. Based on PCM, we design Partial Attention Convolution (PATConv) and Dynamic Partial Convolution (DPConv), and introduce PartialNet—the first hybrid network family leveraging channel-wise sparsity. PartialNet supports learnable, adaptive channel partitioning ratios, substantially reducing both parameter count and FLOPs. On ImageNet-1K, it surpasses multiple state-of-the-art models in both top-1 accuracy and inference speed. Moreover, it achieves competitive performance on COCO object detection and instance segmentation tasks, demonstrating broad applicability and effectiveness across vision domains.
Technology Category
Application Category
📝 Abstract
Designing a module or mechanism that enables a network to maintain low parameters and FLOPs without sacrificing accuracy and throughput remains a challenge. To address this challenge and exploit the redundancy within feature map channels, we propose a new solution: partial channel mechanism (PCM). Specifically, through the split operation, the feature map channels are divided into different parts, with each part corresponding to different operations, such as convolution, attention, pooling, and identity mapping. Based on this assumption, we introduce a novel partial attention convolution (PATConv) that can efficiently combine convolution with visual attention. Our exploration indicates that the PATConv can completely replace both the regular convolution and the regular visual attention while reducing model parameters and FLOPs. Moreover, PATConv can derive three new types of blocks: Partial Channel-Attention block (PAT_ch), Partial Spatial-Attention block (PAT_sp), and Partial Self-Attention block (PAT_sf). In addition, we propose a novel dynamic partial convolution (DPConv) that can adaptively learn the proportion of split channels in different layers to achieve better trade-offs. Building on PATConv and DPConv, we propose a new hybrid network family, named PartialNet, which achieves superior top-1 accuracy and inference speed compared to some SOTA models on ImageNet-1K classification and excels in both detection and segmentation on the COCO dataset. Our code is available at https://github.com/haiduo/PartialNet.
Problem
Research questions and friction points this paper is trying to address.
Image Processing
Network Simplification
Performance Enhancement
Innovation
Methods, ideas, or system contributions that make the work stand out.
Partial Channel Mechanism (PCM)
Dynamic Partial Convolution (DPConv)
Partial Attention Convolution (PATConv)
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