Summary of Inficond: Interactive No-code Fine-tuning with Concept-based Knowledge Distillation, by Jinbin Huang et al.
InFiConD: Interactive No-code Fine-tuning with Concept-based Knowledge Distillation
by Jinbin Huang, Wenbin He, Liang Gou, Liu Ren, Chris Bryan
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents InFiConD, a novel framework that leverages visual concepts for knowledge distillation and enables no-code fine-tuning of student models. The large-scale pre-trained models are used in various downstream tasks, but deployment is challenging due to limited computational resources. InFiConD addresses this challenge by developing a pipeline based on extracting text-aligned visual concepts from a concept corpus using multimodal models. The framework constructs highly interpretable linear student models based on visual concepts that mimic a teacher model in a response-based manner. Users can interactively fine-tune the student model by manipulating concept influences directly in the user interface. InFiConD’s human-in-the-loop and visualization-driven approach enables users to effectively create and analyze student models, understand how knowledge is transferred, and efficiently perform fine-tuning operations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary InFiConD is a new way to help machines learn from each other, even when they have limited computer power. The idea is to take big, powerful AI models and teach smaller models how to work like them, without needing special technical skills. This helps make it easier for people with different areas of expertise to work together on projects. InFiConD lets users control the process by adjusting certain settings, making it a more accessible and understandable tool. |
Keywords
» Artificial intelligence » Fine tuning » Knowledge distillation » Student model » Teacher model
