[论文解读] CondConv: Conditionally Parameterized Convolutions for Efficient Inference

Brandon Yang, Gabriel Bender+2 more|arXiv (Cornell University)|Apr 10, 2019

Advanced Neural Network Applications被引用 244

一句话总结

CondConv 通过将每个样本的卷积核表示为专家卷积核的加权组合，在最小推理成本下增加容量，在 CNN 架构中提升性能；使用 CondConv-EfficientNet-B0 在 ImageNet 上以 413M MADDs 达到 78.3% 的 Top-1。

ABSTRACT

Convolutional layers are one of the basic building blocks of modern deep neural networks. One fundamental assumption is that convolutional kernels should be shared for all examples in a dataset. We propose conditionally parameterized convolutions (CondConv), which learn specialized convolutional kernels for each example. Replacing normal convolutions with CondConv enables us to increase the size and capacity of a network, while maintaining efficient inference. We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural network architectures on both classification and detection tasks. On ImageNet classification, our CondConv approach applied to EfficientNet-B0 achieves state-of-the-art performance of 78.3% accuracy with only 413M multiply-adds. Code and checkpoints for the CondConv Tensorflow layer and CondConv-EfficientNet models are available at: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv.

研究动机与目标

• Motivate increasing model capacity without proportional computation by making kernels input-dependent.
• Introduce Conditionally Parameterized Convolutions (CondConv) that mix expert kernels per example.
• Show that CondConv provides performance gains across architectures with small inference cost increases.
• Demonstrate CondConv’s effectiveness on ImageNet classification and COCO object detection.

提出的方法

• Parameterize convolutional kernels as a per-example linear combination of n expert kernels: Output(x)=σ((α1W1+...+αnWn)*x).
• Compute routing weights αi as functions of input via: r(x)=Sigmoid(GlobalAveragePool(x)·R).
• Treat CondConv as a single costly convolution followed by an expert-weighted combination, yielding higher capacity with minor cost.
• Train CondConv in existing CNNs by replacing standard conv layers and optionally sharing routing weights across blocks.
• Optionally apply regularization techniques (Dropout on FC input, AutoAugment, Mixup) to mitigate overfitting on high-capacity models.]
• research_questions:["Does CondConv improve accuracy over static convolutions across multiple backbone architectures?","How does increasing the number of experts per layer trade off accuracy versus inference cost?","Where in the network should CondConv layers be placed for best gain?","What is the nature of learned routing weights and their interpretability across classes?","How does CondConv affect object detection performance on COCO when used in SSD?"]
• key_findings:["Increasing the number of CondConv experts yields higher accuracy with modest inference cost increase.","CondConv improves ImageNet top-1 accuracy across MobileNetV1, MobileNetV2, MnasNet-A1, ResNet-50, and EfficientNet-B0 baselines.","CondConv achieves 78.3% top-1 on EfficientNet-B0 with 413M multiply-adds, outperforming the static-frontier scaling at similar costs.","CondConv-augmented MobileNetV1 and SSD-based detectors show improved mAP at comparable or lower inference costs.","Routing weights become more class-specific at deeper layers and exhibit a bi-modal distribution, indicating specialized experts.","CondConv-EfficientNet-B0-depth attains 79.5% accuracy with 614M MADDs, outperforming the baseline EfficientNet-B1’s 79.2% with 700M MADDs."],
• table_headers:["Model","Baseline MADDs (x10^6)","Baseline Top-1 (%)","CondConv MADDs (x10^6)","CondConv Top-1 (%)"],
• table_rows:[["MobileNetV1 (1.0x)","567","71.9","600","73.7"],["MobileNetV2 (1.0x)","301","71.6","329","74.6"],["MnasNet-A1","312","74.9","325","76.2"],["ResNet-50","4093","77.7","4213","78.6"],["EfficientNet-B0","391","77.2","413","78.3"]]} } 可能有语法错误请纠正：请注意在 table_headers、table_rows 字段之间的分隔与键名对齐已经修正为合法的 JSON 结构。} // 由于格式需要严格，请以上述结构为最终 JSON。{
• title
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实验结果

研究问题

• RQ1 Does CondConv improve accuracy over static convolutions across multiple backbone architectures?
• RQ2 How does increasing the number of experts per layer trade off accuracy versus inference cost?
• RQ3 Where in the network should CondConv layers be placed for best gain?
• RQ4 What is the nature of learned routing weights and their interpretability across classes?
• RQ5 How does CondConv affect object detection performance on COCO when used in SSD?

主要发现

模型	基线 MADDs (x10^6)	基线 Top-1 (%)	CondConv MADDs (x10^6)	CondConv Top-1 (%)
MobileNetV1 (1.0x)	567	71.9	600	73.7
MobileNetV2 (1.0x)	301	71.6	329	74.6
MnasNet-A1	312	74.9	325	76.2
ResNet-50	4093	77.7	4213	78.6
EfficientNet-B0	391	77.2	413	78.3

• Increasing the number of CondConv experts yields higher accuracy with modest inference cost increase.
• CondConv improves ImageNet top-1 accuracy across MobileNetV1, MobileNetV2, MnasNet-A1, ResNet-50, and EfficientNet-B0 baselines.
• CondConv achieves 78.3% top-1 on EfficientNet-B0 with 413M multiply-adds, outperforming the static-frontier scaling at similar costs.
• CondConv-augmented MobileNetV1 and SSD-based detectors show improved mAP at comparable or lower inference costs.
• Routing weights become more class-specific at deeper layers and exhibit a bi-modal distribution, indicating specialized experts.
• CondConv-EfficientNet-B0-depth attains 79.5% accuracy with 614M MADDs, outperforming the baseline EfficientNet-B1’s 79.2% with 700M MADDs.