ADELT: Transpilation Between Deep Learning Frameworks

Understand

We propose the Adversarial DEep Learning Transpiler (ADELT), a novel approach to source-to-source transpilation between deep learning frameworks.

• ADELT uniquely decouples code skeleton transpilation and API keyword mapping.
• For code skeleton transpilation, it uses few-shot prompting on large language models (LLMs), while for API keyword mapping, it uses contextual embeddings from a code-specific BERT.
• These embeddings are trained in a domain-adversarial setup to generate a keyword translation dictionary.