The paradigm of machine intelligence moves from purely supervised learning to
a more practical scenario when many loosely related unlabeled data are
available and labeled data is scarce. Most existing algorithms assume that the
underlying task distribution is stationary. Here we consider a more realistic
and challenging setting in that task distributions evolve over time. We name
this problem as Semi-supervised meta-learning with Evolving Task diStributions,
abbreviated as SETS. Two key challenges arise in this more realistic setting:
(i) how to use unlabeled data in the presence of a large amount of unlabeled
out-of-distribution (OOD) data; and (ii) how to prevent catastrophic forgetting
on previously learned task distributions due to the task distribution shift. We
propose an OOD Robust and knowleDge presErved semi-supeRvised meta-learning
approach (ORDER), to tackle these two major challenges. Specifically, our ORDER
introduces a novel mutual information regularization to robustify the model
with unlabeled OOD data and adopts an optimal transport regularization to
remember previously learned knowledge in feature space. In addition, we test
our method on a very challenging dataset: SETS on large-scale non-stationary
semi-supervised task distributions consisting of (at least) 72K tasks. With
extensive experiments, we demonstrate the proposed ORDER alleviates forgetting
on evolving task distributions and is more robust to OOD data than related
strong baselines.

本文提出了一种半监督元学习方法（SETS）来解决不断演化的任务分布和大量的未标记数据存在时的元学习问题，并通过引入相互信息正则化来稳健地利用未标记外部分布数据以及采用最优传输正则化来在特征空间记住已学习的知识。在大规模非稳态半监督任务分布数据集上的测试中，实验证明了本文方法相对于相关强基线具有更好的鲁棒性。

大规模非静态任务分布下的低遗忘元学习

Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task  Distributions

We investigate the generalization capabilities of neural signed distance
functions (SDFs) for learning 3D object representations for unseen and
unlabeled point clouds. Existing methods can fit SDFs to a handful of object
classes and boast fine detail or fast inference speeds, but do not generalize
well to unseen shapes. We introduce a two-stage semi-supervised meta-learning
approach that transfers shape priors from labeled to unlabeled data to
reconstruct unseen object categories. The first stage uses an episodic training
scheme to simulate training on unlabeled data and meta-learns initial shape
priors. The second stage then introduces unlabeled data with disjoint classes
in a semi-supervised scheme to diversify these priors and achieve
generalization. We assess our method on both synthetic data and real collected
point clouds. Experimental results and analysis validate that our approach
outperforms existing neural SDF methods and is capable of robust zero-shot
inference on 100+ unseen classes. Code can be found at
this https URL

本文研究了使用神经代表距离函数（SDF）进行三维对象表示学习的泛化能力，提出了一个半监督元学习方法，可将形状先验从有标签的数据传输到无标签的数据中，用于重建未知的对象类别，并实现了 100 多个未见过的类别的强大的零 - shot 推理。

GenSDF: 通用有符号距离函数的两阶段学习

GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions

Generalising deep models to new data from new centres (termed here domains)
remains a challenge. This is largely attributed to shifts in data statistics
(domain shifts) between source and unseen domains. Recently, gradient-based
meta-learning approaches where the training data are split into meta-train and
meta-test sets to simulate and handle the domain shifts during training have
shown improved generalisation performance. However, the current fully
supervised meta-learning approaches are not scalable for medical image
segmentation, where large effort is required to create pixel-wise annotations.
Meanwhile, in a low data regime, the simulated domain shifts may not
approximate the true domain shifts well across source and unseen domains. To
address this problem, we propose a novel semi-supervised meta-learning
framework with disentanglement. We explicitly model the representations related
to domain shifts. Disentangling the representations and combining them to
reconstruct the input image allows unlabeled data to be used to better
approximate the true domain shifts for meta-learning. Hence, the model can
achieve better generalisation performance, especially when there is a limited
amount of labeled data. Experiments show that the proposed method is robust on
different segmentation tasks and achieves state-of-the-art generalisation
performance on two public benchmarks.

本文提出了一种新颖的半监督元学习框架，其中包括解缠结和显式对与领域位移相关的表示进行建模，对于医学图像分割问题，这项技术表现良好，尤其在有限的标注数据情况下具有更好的一般化性能和鲁棒性。