Humans use abstract concepts for understanding instead of hard features.
Recent interpretability research has focused on human-centered concept
explanations of neural networks. Concept Activation Vectors (CAVs) estimate a
model's sensitivity and possible biases to a given concept. In this paper, we
extend CAVs from post-hoc analysis to ante-hoc training in order to reduce
model bias through fine-tuning using an additional Concept Loss. Concepts were
defined on the final layer of the network in the past. We generalize it to
intermediate layers using class prototypes. This facilitates class learning in
the last convolution layer, which is known to be most informative. We also
introduce Concept Distillation to create richer concepts using a pre-trained
knowledgeable model as the teacher. Our method can sensitize or desensitize a
model towards concepts. We show applications of concept-sensitive training to
debias several classification problems. We also use concepts to induce prior
knowledge into IID, a reconstruction problem. Concept-sensitive training can
improve model interpretability, reduce biases, and induce prior knowledge.
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more details.

我们将 CAVs 从事后分析扩展到事前训练，通过使用额外的概念损失进行微调来减少模型偏差。我们还引入了概念蒸馏，使用预训练的知识模型作为教师来创建更丰富的概念。我们展示了概念敏感训练在去偏、分类问题和重建问题中的应用，可以提高模型的可解释性，减少偏差并引入先验知识。

概念提炼：利用人类中心解释提升模型性能

Concept Distillation: Leveraging Human-Centered Explanations for Model  Improvement

Vision-language models trained with contrastive learning on large-scale noisy
data are becoming increasingly popular for zero-shot recognition problems. In
this paper we improve the following three aspects of the contrastive
pre-training pipeline: dataset noise, model initialization and the training
objective. First, we propose a straightforward filtering strategy titled
Complexity, Action, and Text-spotting (CAT) that significantly reduces dataset
size, while achieving improved performance across zero-shot vision-language
tasks. Next, we propose an approach titled Concept Distillation to leverage
strong unimodal representations for contrastive training that does not increase
training complexity while outperforming prior work. Finally, we modify the
traditional contrastive alignment objective, and propose an importance-sampling
approach to up-sample the importance of hard-negatives without adding
additional complexity. On an extensive zero-shot benchmark of 29 tasks, our
Distilled and Hard-negative Training (DiHT) approach improves on 20 tasks
compared to the baseline. Furthermore, for few-shot linear probing, we propose
a novel approach that bridges the gap between zero-shot and few-shot
performance, substantially improving over prior work. Models are available at
this https URL

通过对大规模嘈杂数据进行对比学习训练的视觉 - 语言模型正在变得越来越受欢迎用于零样本识别问题，本文在数据集噪声、模型初始化和训练目标三个方面对对比预训练管道进行了改进。通过 Complexity、Action、and Text-spotting（CAT）筛选策略、概念蒸馏以及重要性采样方法，我们的 DiHT 方法极大地提高了对零样本和几样本线性探测的效果。