Generalist segmentation models are increasingly favored for diverse tasks
involving various objects from different image sources. Task-Incremental
Learning (TIL) offers a privacy-preserving training paradigm using tasks
arriving sequentially, instead of gathering them due to strict data sharing
policies. However, the task evolution can span a wide scope that involves
shifts in both image appearance and segmentation semantics with intricate
correlation, causing concurrent appearance and semantic forgetting. To solve
this issue, we propose a Comprehensive Generative Replay (CGR) framework that
restores appearance and semantic knowledge by synthesizing image-mask pairs to
mimic past task data, which focuses on two aspects: modeling image-mask
correspondence and promoting scalability for diverse tasks. Specifically, we
introduce a novel Bayesian Joint Diffusion (BJD) model for high-quality
synthesis of image-mask pairs with their correspondence explicitly preserved by
conditional denoising. Furthermore, we develop a Task-Oriented Adapter (TOA)
that recalibrates prompt embeddings to modulate the diffusion model, making the
data synthesis compatible with different tasks. Experiments on incremental
tasks (cardiac, fundus and prostate segmentation) show its clear advantage for
alleviating concurrent appearance and semantic forgetting. Code is available at
this https URL

任务增量学习中的综合生成回放框架通过合成图像 - 掩模对来还原外观和语义知识，解决了出现同时外观和语义遗忘的问题。

综合生成回放的任务增量分割：具有并行外观和语义遗忘

Comprehensive Generative Replay for Task-Incremental Segmentation with  Concurrent Appearance and Semantic Forgetting

Contrastive representation learning has emerged as a promising technique for
continual learning as it can learn representations that are robust to
catastrophic forgetting and generalize well to unseen future tasks. Previous
work in continual learning has addressed forgetting by using previous task data
and trained models. Inspired by event models created and updated in the brain,
we propose a new mechanism that takes place during task boundaries, i.e., when
one task finishes and another starts. By observing the redundancy-inducing
ability of contrastive loss on the output of a neural network, our method
leverages the first few samples of the new task to identify and retain
parameters contributing most to the transfer ability of the neural network,
freeing up the remaining parts of the network to learn new features. We
evaluate the proposed methods on benchmark computer vision datasets including
CIFAR10 and TinyImagenet and demonstrate state-of-the-art performance in the
task-incremental, class-incremental, and domain-incremental continual learning
scenarios.

通过观察对神经网络输出的对比损失的冗余引发能力，我们的方法利用新任务的前几个样本来识别和保留对神经网络传递能力最有贡献的参数，从而使网络的其余部分能够学习新特征，并在诸如 CIFAR10 和 TinyImagenet 等基准计算机视觉数据集上证明了在任务增量、类别增量和领域增量持续学习场景中的最先进性能。

前瞻性选择性可塑性实现视觉任务的持续学习

Look-Ahead Selective Plasticity for Continual Learning of Visual Tasks

It is common to have continuous streams of new data that need to be
introduced in the system in real-world applications. The model needs to learn
newly added capabilities (future tasks) while retaining the old knowledge (past
tasks). Incremental learning has recently become increasingly appealing for
this problem. Task-incremental learning is a kind of incremental learning where
task identity of newly included task (a set of classes) remains known during
inference. A common goal of task-incremental methods is to design a network
that can operate on minimal size, maintaining decent performance. To manage the
stability-plasticity dilemma, different methods utilize replay memory of past
tasks, specialized hardware, regularization monitoring etc. However, these
methods are still less memory efficient in terms of architecture growth or
input data costs. In this study, we present a simple yet effective adjustment
network (SAN) for task incremental learning that achieves near state-of-the-art
performance while using minimal architectural size without using memory
instances compared to previous state-of-the-art approaches. We investigate this
approach on both 3D point cloud object (ModelNet40) and 2D image (CIFAR10,
CIFAR100, MiniImageNet, MNIST, PermutedMNIST, notMNIST, SVHN, and FashionMNIST)
recognition tasks and establish a strong baseline result for a fair comparison
with existing methods. On both 2D and 3D domains, we also observe that SAN is
primarily unaffected by different task orders in a task-incremental setting.

研究了一种基于简单有效的调整网络（SAN）的增量学习方法，达到了接近最先进的性能，使用最小的架构大小，而不需要使用先前最先进方法中的内存实例，对 3D 点云对象（ModelNet40）和 2D 图像（CIFAR10，CIFAR100，MiniImageNet，MNIST，PermutedMNIST，notMNIST，SVHN 和 FashionMNIST）识别任务进行了研究，并为公平比较现有方法建立了强有力的基准结果。在 2D 和 3D 领域，我们还观察到 SAN 在任务增量设置中基本不受不同任务顺序的影响。

重新思考任务增量学习基线

Rethinking Task-Incremental Learning Baselines

Task-incremental learning involves the challenging problem of learning new
tasks continually, without forgetting past knowledge. Many approaches address
the problem by expanding the structure of a shared neural network as tasks
arrive, but struggle to grow optimally, without losing past knowledge. We
present a new framework, Learn to Bind and Grow, which learns a neural
architecture for a new task incrementally, either by binding with layers of a
similar task or by expanding layers which are more likely to conflict between
tasks. Central to our approach is a novel, interpretable, parameterization of
the shared, multi-task architecture space, which then enables computing
globally optimal architectures using Bayesian optimization. Experiments on
continual learning benchmarks show that our framework performs comparably with
earlier expansion based approaches and is able to flexibly compute multiple
optimal solutions with performance-size trade-offs.

本文提出了一种增量式学习新任务的框架 Learn to Bind and Grow，该框架通过绑定与类似任务的层或扩展更可能发生冲突的层来学习新任务的神经网络架构；该框架通过一种全新而可解释的参数化方法，使用贝叶斯优化来计算全局最优架构，并在连续学习基准测试上展示了可比较的性能。