knowledge-transfer

Awesome Knowledge Distillation

Pytorch implementation of various Knowledge Distillation (KD) methods.

First Principles Framework (FPF): AI-native pattern languages for systems engineering, research and management. Shared human-AI reasoning for architecture decisions, evidence, trade-offs and method engineering. FPF Core and domain frameworks (DPFs).

Official PyTorch implementation of "A Comprehensive Overhaul of Feature Distillation" (ICCV 2019)

PyContinual (An Easy and Extendible Framework for Continual Learning)

An Extensible Continual Learning Framework Focused on Language Models (LMs)

This repository is mainly dedicated for listing the recent research advancements in the application of Self-Supervised-Learning in medical images computing field

Code and dataset for ACL2018 paper "Exploiting Document Knowledge for Aspect-level Sentiment Classification"

Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons (AAAI 2019)

Code and pretrained models for paper: Data-Free Adversarial Distillation

[ECCV2022] Factorizing Knowledge in Neural Networks

A Comprehensive Survey on Knowledge Distillation

[Paper][AAAI 2023] DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning

PyTorch implementation of (Hinton) Knowledge Distillation and a base class for simple implementation of other distillation methods.

Knowledge Transfer via Dense Cross-layer Mutual-distillation (ECCV'2020)

Code for ECML/PKDD 2020 Paper --- Continual Learning with Knowledge Transfer for Sentiment Classification

[NeurIPS'23] Source code of "Data-Centric Learning from Unlabeled Graphs with Diffusion Model": A data-centric transfer learning framework with diffusion model on graphs.

Code for NeurIPS 2020 Paper --- Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks

KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multirobot Systems

[arXiv 2024] PyTorch implementation of RRD: https://arxiv.org/abs/2407.12073