# aberhu/knowledge-distillation-zoo

**Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/aberhu-knowledge-distillation-zoo).**

_How this analysis was created: the description and tags below were written by an AI model that read this project's README and public documentation pages; stars, license and language come straight from the GitHub API. The model does not read the source code._

1,747 stars · 268 forks · Python

## Links

- GitHub: https://github.com/AberHu/Knowledge-Distillation-Zoo
- awesome-repositories: https://awesome-repositories.com/repository/aberhu-knowledge-distillation-zoo.md

## Topics

`distillation` `kd` `kd-methods` `knowledge-distillation` `knowledge-transfer` `model-compression` `teacher-student`

## Description

Knowledge-Distillation-Zoo is a framework for neural network model compression that facilitates the transfer of learned patterns from large teacher models to smaller student architectures. It provides a modular environment for executing training pipelines designed to reduce the computational requirements of deep learning models while maintaining predictive accuracy.

The library implements knowledge transfer through both logit-based mimicry and feature-map alignment, allowing students to replicate the classification behavior and internal representations of a teacher. It supports teacher-student decoupling, where the teacher model remains frozen during the training process, and utilizes modular loss composition to balance task-specific objectives with distillation-specific penalties.

The toolkit includes a command-line interface for managing training workflows and supports configurable parameter injection to swap between different distillation strategies. It is built as a library for PyTorch, providing a structured environment for optimizing neural networks for deployment on resource-constrained hardware.

## Tags

### Artificial Intelligence & ML

- [Neural Network Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-training-frameworks.md) — Provides a modular environment for executing distillation pipelines that optimize student model performance.
- [Model Distillation](https://awesome-repositories.com/f/artificial-intelligence-ml/embedding-models/model-distillation.md) — Transfers learned patterns from a large teacher model into a smaller student model while maintaining accuracy. ([source](https://github.com/aberhu/knowledge-distillation-zoo#readme))
- [Knowledge Distillation](https://awesome-repositories.com/f/artificial-intelligence-ml/knowledge-distillation.md) — Applies loss functions and training strategies to teach compact models to mimic the performance of complex teacher models.
- [Neural Network Model Compression](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-model-compression.md) — Reduces the size and computational requirements of deep learning models by transferring knowledge from large teacher networks.
- [Deep Learning Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-toolkits.md) — Provides a set of training strategies designed to reduce the computational requirements of neural networks while maintaining accuracy.
- [Intermediate Representation Alignments](https://awesome-repositories.com/f/artificial-intelligence-ml/feature-alignment/intermediate-representation-alignments.md) — Minimizes the distance between intermediate layer activations to ensure the student learns internal representations similar to the teacher.
- [Distillation Penalty Compositions](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/architectures/neural-network-components/loss-functions/perceptual-loss/medical-segmentation-loss-functions/combined-loss-functions/distillation-penalty-compositions.md) — Combines standard task-specific objectives with distillation-specific penalty terms to balance accuracy and mimicry.
- [Teacher-Student Distillation](https://awesome-repositories.com/f/artificial-intelligence-ml/model-distillation-methods/teacher-student-distillation.md) — Calculates divergence between teacher and student output probability distributions to force mimicry of classification behavior.
- [Teacher-Student Decoupling Mechanisms](https://awesome-repositories.com/f/artificial-intelligence-ml/model-distillation-methods/teacher-student-distillation/teacher-student-decoupling-mechanisms.md) — Maintains separate model architectures where the teacher remains frozen while the student updates its weights based on teacher guidance.
- [Knowledge Distillation Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/model-distillation-pipelines/reasoning-distilled-models/knowledge-distillation-pipelines.md) — Includes a CLI-based training pipeline for student networks using pre-defined distillation methods and configurable hyperparameters. ([source](https://github.com/aberhu/knowledge-distillation-zoo#readme))
- [PyTorch Model Optimizations](https://awesome-repositories.com/f/artificial-intelligence-ml/pytorch-model-optimizations.md) — Implements training pipelines to improve the efficiency of neural networks for deployment on resource-constrained hardware.

### Software Engineering & Architecture

- [Training Pipelines](https://awesome-repositories.com/f/software-engineering-architecture/training-pipelines.md) — Provides structured training pipelines that allow for configurable distillation strategies and loss functions via parameter injection.