Demonstration of transfer of knowledge and generalization with distillation

Overview

Distilling-the-Knowledge-in-a-Neural-Network

This is an implementation of a part of the paper "Distilling the Knowledge in a Neural Network" (https://arxiv.org/abs/1503.02531).

Teacher network has two hidden layers with 1200 units in each layer. It is trained on MNIST with data augmentation and achieves 108 test errors.

Student network has one hidden layer with 400 units. No regularization techniques are used to train student network except weight regularization. Without distillation, it achieves 181 test errors. With distillation, the test errors reduces to 134. This demonstrates the knowledge transfer happening from teacher to student, helping the student to generalize better.

Training and testing teacher and student network

For training teacher network, run all cells of distill_basic_teacher.ipynb. For training student network, run all cells of distill_basic_student.ipynb. Modify second cell of both notebooks according to the availability of GPU.

Tensors and neural networks in Haskell

Hasktorch Hasktorch is a library for tensors and neural networks in Haskell. It is an independent open source community project which leverages the co

hasktorch 920 Jan 04, 2023
Tools for robust generative diffeomorphic slice to volume reconstruction

RGDSVR Tools for Robust Generative Diffeomorphic Slice to Volume Reconstructions (RGDSVR) This repository provides tools to implement the methods in t

Lucilio Cordero-Grande 0 Oct 29, 2021
Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two

512x512 flowers after 12 hours of training, 1 gpu 256x256 flowers after 12 hours of training, 1 gpu Pizza 'Lightweight' GAN Implementation of 'lightwe

Phil Wang 1.5k Jan 02, 2023
TART - A PyTorch implementation for Transition Matrix Representation of Trees with Transposed Convolutions

TART This project is a PyTorch implementation for Transition Matrix Representati

Lee Sael 2 Jan 19, 2022
A higher performance pytorch implementation of DeepLab V3 Plus(DeepLab v3+)

A Higher Performance Pytorch Implementation of DeepLab V3 Plus Introduction This repo is an (re-)implementation of Encoder-Decoder with Atrous Separab

linhua 326 Nov 22, 2022
A library for low-memory inferencing in PyTorch.

Pylomin Pylomin (PYtorch LOw-Memory INference) is a library for low-memory inferencing in PyTorch. Installation ... Usage For example, the following c

3 Oct 26, 2022
这个开源项目主要是对经典的时间序列预测算法论文进行复现,模型主要参考自GluonTS,框架主要参考自Informer

Time Series Research with Torch 这个开源项目主要是对经典的时间序列预测算法论文进行复现,模型主要参考自GluonTS,框架主要参考自Informer。 建立原因 相较于mxnet和TF,Torch框架中的神经网络层需要提前指定输入维度: # 建立线性层 TensorF

Chi Zhang 85 Dec 29, 2022
[ICML 2020] Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

PG-MORL This repository contains the implementation for the paper Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Contro

MIT Graphics Group 65 Jan 07, 2023
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel

Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel This repository is the official PyTorch implementation of BSRDM w

Zongsheng Yue 69 Jan 05, 2023
Official code for our CVPR '22 paper "Dataset Distillation by Matching Training Trajectories"

Dataset Distillation by Matching Training Trajectories Project Page | Paper This repo contains code for training expert trajectories and distilling sy

George Cazenavette 256 Jan 05, 2023
Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners

Unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners This repository is built upon BEiT, thanks very much! Now, we on

Zhiliang Peng 2.3k Jan 04, 2023
Randomizes the warps in a stock pokeemerald repo.

pokeemerald warp randomizer Randomizes the warps in a stock pokeemerald repo. Usage Instructions Install networkx and matplotlib via pip3 or similar.

Max Thomas 6 Mar 17, 2022
Codes to pre-train T5 (Text-to-Text Transfer Transformer) models pre-trained on Japanese web texts

t5-japanese Codes to pre-train T5 (Text-to-Text Transfer Transformer) models pre-trained on Japanese web texts. The following is a list of models that

Kimio Kuramitsu 1 Dec 13, 2021
Video-Music Transformer

VMT Video-Music Transformer (VMT) is an attention-based multi-modal model, which generates piano music for a given video. Paper https://arxiv.org/abs/

Chin-Tung Lin 5 Jul 13, 2022
Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens

TensorLayer Community 7.1k Dec 29, 2022
EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients.

EASY - Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients. This repository is the official im

Yassir BENDOU 57 Dec 26, 2022
Walk with fastai

Shield: This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Walk with fastai What is this p

Walk with fastai 124 Dec 10, 2022
Contains modeling practice materials and homework for the Computational Neuroscience course at Okinawa Institute of Science and Technology

A310 Computational Neuroscience - Okinawa Institute of Science and Technology, 2022 This repository contains modeling practice materials and homework

Sungho Hong 1 Jan 24, 2022
Leveraging OpenAI's Codex to solve cornerstone problems in Music

Music-Codex Leveraging OpenAI's Codex to solve cornerstone problems in Music Please NOTE: Presented generated samples were created by OpenAI's Codex P

Alex 2 Mar 11, 2022
It is a simple library to speed up CLIP inference up to 3x (K80 GPU)

CLIP-ONNX It is a simple library to speed up CLIP inference up to 3x (K80 GPU) Usage Install clip-onnx module and requirements first. Use this trick !

Gerasimov Maxim 93 Dec 20, 2022