Awesome Deep Model Compression
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Updated
Apr 13, 2021
Awesome Deep Model Compression
Model distillation of CNNs for classification of Seafood Images in PyTorch
Matching Guided Distillation (ECCV 2020)
[Master Thesis] Research project at the Data Analytics Lab in collaboration with Daedalean AI. The thesis was submitted to both ETH Zürich and Imperial College London.
The Codebase for Causal Distillation for Language Models (NAACL '22)
🚀 PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)"
The Codebase for Causal Distillation for Task-Specific Models
A framework for knowledge distillation using TensorRT inference on teacher network
Our open source implementation of MiniLMv2 (https://aclanthology.org/2021.findings-acl.188)
Repository for the publication "AutoGraph: Predicting Lane Graphs from Traffic"
Autodistill Google Cloud Vision module for use in training a custom, fine-tuned model.
Use AWS Rekognition to train custom models that you own.
Use LLaMA to label data for use in training a fine-tuned LLM.
Mechanistically interpretable neurosymbolic AI (Nature Comput Sci 2024): losslessly compressing NNs to computer code and discovering new algorithms which generalize out-of-distribution and outperform human-designed algorithms
Awesome Knowledge Distillation
Images to inference with no labeling (use foundation models to train supervised models).
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