With the wide proliferation of Deep Neural Networks in high-stake
applications, there is a growing demand for explainability behind their
decision-making process. Concept learning models attempt to learn high-level
'concepts' - abstract entities that align with human understanding, and thus
provide interpretability to DNN architectures. However, in this paper, we
demonstrate that present SOTA concept learning approaches suffer from two major
problems - lack of concept fidelity wherein the models fail to learn consistent
concepts among similar classes and limited concept interoperability wherein the
models fail to generalize learned concepts to new domains for the same task.
Keeping these in mind, we propose a novel self-explaining architecture for
concept learning across domains which - i) incorporates a new concept saliency
network for representative concept selection, ii) utilizes contrastive learning
to capture representative domain invariant concepts, and iii) uses a novel
prototype-based concept grounding regularization to improve concept alignment
across domains. We demonstrate the efficacy of our proposed approach over
current SOTA concept learning approaches on four widely used real-world
datasets. Empirical results show that our method improves both concept fidelity
measured through concept overlap and concept interoperability measured through
domain adaptation performance.

利用自我解释的架构跨领域进行概念学习，提出新的概念显著网络、对比学习和基于原型的概念基准规范化方法来解决概念一致性和泛化能力的问题，并在四个实际数据集上展示了方法的有效性。

一个自说明的神经架构用于可推广的概念学习

A Self-explaining Neural Architecture for Generalizable Concept Learning

This paper introduces MultiBooth, a novel and efficient technique for
multi-concept customization in image generation from text. Despite the
significant advancements in customized generation methods, particularly with
the success of diffusion models, existing methods often struggle with
multi-concept scenarios due to low concept fidelity and high inference cost.
MultiBooth addresses these issues by dividing the multi-concept generation
process into two phases: a single-concept learning phase and a multi-concept
integration phase. During the single-concept learning phase, we employ a
multi-modal image encoder and an efficient concept encoding technique to learn
a concise and discriminative representation for each concept. In the
multi-concept integration phase, we use bounding boxes to define the generation
area for each concept within the cross-attention map. This method enables the
creation of individual concepts within their specified regions, thereby
facilitating the formation of multi-concept images. This strategy not only
improves concept fidelity but also reduces additional inference cost.
MultiBooth surpasses various baselines in both qualitative and quantitative
evaluations, showcasing its superior performance and computational efficiency.
Project Page: this https URL

介绍了一种名为 MultiBooth 的新颖高效的图像生成技术，该技术可在文本中进行多概念定制生成；通过将多概念生成过程分为单概念学习阶段和多概念整合阶段，利用多模态图像编码和高效的概念编码技术提高了概念准确性并降低了推断成本。