DefangChen/SemCKD
[AAAI-2021, TKDE-2023] Official implementation for "Cross-Layer Distillation with Semantic Calibration".
This project helps machine learning engineers create smaller, more efficient 'student' neural networks that perform almost as well as larger, more complex 'teacher' networks. It takes a large, pre-trained teacher model and training data (like image datasets) as input, and outputs a compact student model that is easier to deploy. Machine learning researchers and practitioners who need to optimize model size and inference speed for deployment would find this valuable.
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Use this if you need to reduce the computational cost and memory footprint of a neural network model without significantly sacrificing its performance, especially for tasks like image classification.
Not ideal if your primary goal is to train a model from scratch without leveraging existing larger models, or if interpretability of the distillation process is critical over performance gains.
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Jul 29, 2024
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