论文标题
学习学习可扩展输入图像的参数化分类网络
Learning to Learn Parameterized Classification Networks for Scalable Input Images
论文作者
论文摘要
相对于输入分辨率变化，卷积神经网络（CNN）没有可预测的识别行为。这样可以防止针对特定模型的不同输入图像分辨率部署的可行性。为了在运行时实现高效且灵活的图像分类，我们采用元学习者来生成主网络的卷积权重，以进行各种输入量表，并维持每个尺度的私有化批处理标准化层。为了提高培训表现，我们进一步利用了基于不同输入分辨率的模型预测的知识蒸馏。与单独训练的模型相比，学到的元网络可以动态参数化主网络以任意大小的输入图像的作用，其精度始终如一。对成像网的广泛实验表明，我们的方法在自适应推理过程中实现了提高的准确性效率折衷。通过切换可执行输入分辨率，我们的方法可以满足不同资源约束环境中快速适应的要求。代码和型号可在https://github.com/d-li14/san上找到。
Convolutional Neural Networks (CNNs) do not have a predictable recognition behavior with respect to the input resolution change. This prevents the feasibility of deployment on different input image resolutions for a specific model. To achieve efficient and flexible image classification at runtime, we employ meta learners to generate convolutional weights of main networks for various input scales and maintain privatized Batch Normalization layers per scale. For improved training performance, we further utilize knowledge distillation on the fly over model predictions based on different input resolutions. The learned meta network could dynamically parameterize main networks to act on input images of arbitrary size with consistently better accuracy compared to individually trained models. Extensive experiments on the ImageNet demonstrate that our method achieves an improved accuracy-efficiency trade-off during the adaptive inference process. By switching executable input resolutions, our method could satisfy the requirement of fast adaption in different resource-constrained environments. Code and models are available at https://github.com/d-li14/SAN.
