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
this https URL .

我们提出了一种新的隐式网络类别，即多尺度深度平衡模型（MDEQ），适用于大规模高度分层的模式识别领域。这种方法可以同时解决多个特征维度的平衡点，并且可以用于多种任务和损失函数，例如使用单个 MDEQ 同时执行图像分类和语义分割。在 ImageNet 分类和 Cityscapes 数据集高分辨率图像的语义分割任务上，我们展示了此方法的有效性。