The remarkable performance of recent stereo depth estimation models benefits
from the successful use of convolutional neural networks to regress dense
disparity. Akin to most tasks, this needs gathering training data that covers a
number of heterogeneous scenes at deployment time. However, training samples
are typically acquired continuously in practical applications, making the
capability to learn new scenes continually even more crucial. For this purpose,
we propose to perform continual stereo matching where a model is tasked to 1)
continually learn new scenes, 2) overcome forgetting previously learned scenes,
and 3) continuously predict disparities at inference. We achieve this goal by
introducing a Reusable Architecture Growth (RAG) framework. RAG leverages
task-specific neural unit search and architecture growth to learn new scenes
continually in both supervised and self-supervised manners. It can maintain
high reusability during growth by reusing previous units while obtaining good
performance. Additionally, we present a Scene Router module to adaptively
select the scene-specific architecture path at inference. Comprehensive
experiments on numerous datasets show that our framework performs impressively
in various weather, road, and city circumstances and surpasses the
state-of-the-art methods in more challenging cross-dataset settings. Further
experiments also demonstrate the adaptability of our method to unseen scenes,
which can facilitate end-to-end stereo architecture learning and practical
deployment.

通过可持续学习和架构增长，我们提出了一种可重复使用的架构增长（RAG）框架，用于连续立体匹配和动态场景适应性，实现了在各种天气、道路和城市环境中的出色表现，并超越了最难的跨数据集设置的最新方法。

可复用的持续立体匹配架构增长

Reusable Architecture Growth for Continual Stereo Matching

Continual learning aims to learn new tasks without forgetting previously
learned ones. We hypothesize that representations learned to solve each task in
a sequence have a shared structure while containing some task-specific
properties. We show that shared features are significantly less prone to
forgetting and propose a novel hybrid continual learning framework that learns
a disjoint representation for task-invariant and task-specific features
required to solve a sequence of tasks. Our model combines architecture growth
to prevent forgetting of task-specific skills and an experience replay approach
to preserve shared skills. We demonstrate our hybrid approach is effective in
avoiding forgetting and show it is superior to both architecture-based and
memory-based approaches on class incrementally learning of a single dataset as
well as a sequence of multiple datasets in image classification. Our code is
available at
https://github.com/facebookresearch/Adversarial-Continual-Learning.

本研究旨在使用混合方法来解决连续学习中遗忘的问题，该方法结合了体系结构生长来防止任务特定技能的遗忘，并采用经验回放方法来保存共享技能。研究结果表明，我们的混合方法在单个数据集和多个数据集的类增量学习中都表现优异。