In the scenario of class-incremental learning (CIL), deep neural networks
have to adapt their model parameters to non-stationary data distributions,
e.g., the emergence of new classes over time. However, CIL models are
challenged by the well-known catastrophic forgetting phenomenon. Typical
methods such as rehearsal-based ones rely on storing exemplars of old classes
to mitigate catastrophic forgetting, which limits real-world applications
considering memory resources and privacy issues. In this paper, we propose a
novel rehearsal-free CIL approach that learns continually via the synergy
between two Complementary Learning Subnetworks. Our approach involves jointly
optimizing a plastic CNN feature extractor and an analytical feed-forward
classifier. The inaccessibility of historical data is tackled by holistically
controlling the parameters of a well-trained model, ensuring that the decision
boundary learned fits new classes while retaining recognition of previously
learned classes. Specifically, the trainable CNN feature extractor provides
task-dependent knowledge separately without interference; and the final
classifier integrates task-specific knowledge incrementally for decision-making
without forgetting. In each CIL session, it accommodates new tasks by attaching
a tiny set of declarative parameters to its backbone, in which only one matrix
per task or one vector per class is kept for knowledge retention. Extensive
experiments on a variety of task sequences show that our method achieves
competitive results against state-of-the-art methods, especially in accuracy
gain, memory cost, training efficiency, and task-order robustness. Furthermore,
to make the non-growing backbone (i.e., a model with limited network capacity)
suffice to train on more incoming tasks, a graceful forgetting implementation
on previously learned trivial tasks is empirically investigated.

本文提出了一种新的无需回放的类增量学习方法，该方法通过学习两个互补学习子网络之间的协同作用，包括联合优化可塑性 CNN 特征提取器和解析前馈分类器来实现。该方法在训练期间不需要存储过去的数据，可以掌握新的课程，同时保持对以前学习过的课程的识别和知识保留。

参数高效的增量式学习的互补学习子网络

Complementary Learning Subnetworks for Parameter-Efficient  Class-Incremental Learning

The problem of generalizing deep neural networks from multiple source domains
to a target one is studied under two settings: When unlabeled target data is
available, it is a multi-source unsupervised domain adaptation (UDA) problem,
otherwise a domain generalization (DG) problem. We propose a unified framework
termed domain adaptive ensemble learning (DAEL) to address both problems. A
DAEL model is composed of a CNN feature extractor shared across domains and
multiple classifier heads each trained to specialize in a particular source
domain. Each such classifier is an expert to its own domain and a non-expert to
others. DAEL aims to learn these experts collaboratively so that when forming
an ensemble, they can leverage complementary information from each other to be
more effective for an unseen target domain. To this end, each source domain is
used in turn as a pseudo-target-domain with its own expert providing
supervisory signal to the ensemble of non-experts learned from the other
sources. For unlabeled target data under the UDA setting where real expert does
not exist, DAEL uses pseudo-label to supervise the ensemble learning. Extensive
experiments on three multi-source UDA datasets and two DG datasets show that
DAEL improves the state of the art on both problems, often by significant
margins. The code is released at
https://github.com/KaiyangZhou/Dassl.pytorch.

本文提出了一种统一的域自适应集成学习（DAEL）框架，通过在多源场景中专用于某个领域的分类器头，以及使用 CNN 特征提取器共享的方式来解决使用多源未标记数据的多源无监督域自适应（UDA）问题和域通用性（DG）问题，来提高 DAEL 模型在 UDCA 数据集和 DG 数据集上的表现。

领域自适应集成学习

Domain Adaptive Ensemble Learning

Aesthetic image captioning (AIC) refers to the multi-modal task of generating
critical textual feedbacks for photographs. While in natural image captioning
(NIC), deep models are trained in an end-to-end manner using large curated
datasets such as MS-COCO, no such large-scale, clean dataset exists for AIC.
Towards this goal, we propose an automatic cleaning strategy to create a
benchmarking AIC dataset, by exploiting the images and noisy comments easily
available from photography websites. We propose a probabilistic
caption-filtering method for cleaning the noisy web-data, and compile a
large-scale, clean dataset "AVA-Captions", (230, 000 images with 5 captions per
image). Additionally, by exploiting the latent associations between aesthetic
attributes, we propose a strategy for training the convolutional neural network
(CNN) based visual feature extractor, the first component of the AIC framework.
The strategy is weakly supervised and can be effectively used to learn rich
aesthetic representations, without requiring expensive ground-truth
annotations. We finally show-case a thorough analysis of the proposed
contributions using automatic metrics and subjective evaluations.

本文介绍了如何通过使用基于网站提供的图片和嘈杂的评论的自动清洗策略创建一个用于美学图像标题生成的基准数据集（AVA-Captions）。同时，还介绍了一种概率的标题过滤方法，以及利用美学属性间的潜在关联性进行卷积神经网络（CNN）特征提取器的训练的策略。该策略是弱监督的，并可用于学习丰富的美学表示，无需昂贵的标注。最后，文章通过自动度量和主观评价展示了所提出贡献的全面分析。