# Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract]

arXiv: https://arxiv.org/abs/1901.08547 · HTML: https://mlsanity.com/paper/1901.08547/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2019.

## Built on by (most cited first, 3 of 3)

- [Learning Visual Models using a Knowledge Graph as a Trainer](https://mlsanity.com/paper/2102.08747/) (2021)
- [Towards Human-Compatible XAI: Explaining Data Differentials with Concept Induction over Background Knowledge](https://mlsanity.com/paper/2209.13710/) (2022)
- [What can knowledge graph alignment gain with Neuro-Symbolic learning approaches?](https://mlsanity.com/paper/2310.07417/) (2023)

## Abstract (arXiv)

Transfer learning which aims at utilizing knowledge learned from one problem (source domain) to solve another different but related problem (target domain) has attracted wide research attentions. However, the current transfer learning methods are mostly uninterpretable, especially to people without ML expertise. In this extended abstract, we brief introduce two knowledge graph (KG) based frameworks towards human understandable transfer learning explanation. The first one explains the transferability of features learned by Convolutional Neural Network (CNN) from one domain to another through pre-training and fine-tuning, while the second justifies the model of a target domain predicted by models from multiple source domains in zero-shot learning (ZSL). Both methods utilize KG and its reasoning capability to provide rich and human understandable explanations to the transfer procedure.