论文标题
多尺度深平衡模型
Multiscale Deep Equilibrium Models
论文作者
论文摘要
我们提出了一类新的隐式网络，即多尺度深度平衡模型（MDEQ），适用于大规模且高度分层的模式识别域。 MDEQ直接解决并通过隐式分化来避免存储中间状态（因此仅需要$ O（1）$内存消耗），并通过多个特征分辨率的平衡点进行逆转。这些同时学习的多分辨率功能使我们能够在各种任务和损失功能集上训练单个模型，例如使用单个MDEQ同时执行图像分类和语义分割。我们说明了这种方法对两个大规模视觉任务的有效性：来自CityScapes数据集的高分辨率图像的ImageNet分类和语义分割。在这两种情况下，MDEQs都能够匹配或超过最近竞争计算机视觉模型的性能：通过隐性深度学习方法，第一次实现了这种性能和规模。代码和预训练的模型位于https://github.com/locuslab/mdeq。
We propose a new class of implicit networks, the multiscale deep equilibrium model (MDEQ), suited to large-scale and highly hierarchical pattern recognition domains. An MDEQ directly solves for and backpropagates through the equilibrium points of multiple feature resolutions simultaneously, using implicit differentiation to avoid storing intermediate states (and thus requiring only $O(1)$ memory consumption). These simultaneously-learned multi-resolution features allow us to train a single model on a diverse set of tasks and loss functions, such as using a single MDEQ to perform both image classification and semantic segmentation. We illustrate the effectiveness of this approach on two large-scale vision tasks: ImageNet classification and semantic segmentation on high-resolution images from the Cityscapes dataset. In both settings, MDEQs are able to match or exceed the performance of recent competitive computer vision models: the first time such performance and scale have been achieved by an implicit deep learning approach. The code and pre-trained models are at https://github.com/locuslab/mdeq .
