Awesome Knowledge Distillation
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Updated
May 23, 2024
Awesome Knowledge Distillation
This repository is mainly dedicated for listing the recent research advancements in the application of Self-Supervised-Learning in medical images computing field
[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.
[Paper][AAAI 2023] DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning
PyContinual (An Easy and Extendible Framework for Continual Learning)
An Extensible Continual Learning Framework Focused on Language Models (LMs)
KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multirobot Systems
Repositorio del TFG Urban Street Mapping Transfer
Knowledge transfer sessions I've presented.
Promote documentation best practices
The idea is to use pretrained deep learning algorithms such as mobilenet to predict objects in a image.
Bonan & Samo. January 2023. Paper on cross-linguistic bias in health-related content in Transformer-based language models.
Code and pretrained models for paper: Data-Free Adversarial Distillation
Code for NeurIPS 2020 Paper --- Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks
[ECCV2022] Factorizing Knowledge in Neural Networks
🧠 A DNN that uses knowledge transfer to perform localization and an RNN that studies and generates texts, built for a university Deep Learning course
Pytorch implementation of various Knowledge Distillation (KD) methods.
Code for ECML/PKDD 2020 Paper --- Continual Learning with Knowledge Transfer for Sentiment Classification
Multiple methods' implementations to transfer the knowledge between Neural Networks and save/plot/compare the results.
The Weizenbaum Institut „Knowledge Tool“ supports interdisciplinary debates by providing a workshop methodology based on predefined instructions and prefabricated workshop materials with the aim to structure and record multi-perspective exploration and analysis.
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