TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

Overview

[TensorFlow 2] A Simple Baseline for Bayesian Uncertainty in Deep Learning: SWA-Gaussian (SWAG)

TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

Concept

Algorithm to utilize the SWAG [1].

Equation for the weight sampling from SWAG [1].

Results

The red color and the blue color represent the initial state and current state respectively.

Variable MNIST CIFAR10

Performance

MNIST

Method Accuracy Precision Recall F1-Score
Final Epoch 0.99230 0.99231 0.99222 0.99226
Best Loss 0.99350 0.99350 0.99338 0.99344
SWAG (S = 30) 0.99310 0.99305 0.99299 0.99302
SWAG (Last Momentum) 0.99340 0.99340 0.99330 0.99335

CIFAR10

Method Accuracy Precision Recall F1-Score
Final Epoch 0.73130 0.73349 0.73130 0.73147
Best Loss 0.73240 0.73205 0.73240 0.73099
SWAG (S = 30) 0.74100 0.74622 0.74100 0.74260
SWAG (Last Momentum) 0.73490 0.73888 0.73490 0.73561

Requirements

  • Python 3.7.6
  • Tensorflow 2.3.0
  • Numpy 1.18.15
  • whiteboxlayer 0.1.15

Reference

[1] Wesley Maddox et al. (2019). A Simple Baseline for Bayesian Uncertainty in Deep Learning. arXiv preprint arXiv:1902.02476.

Owner
YeongHyeon Park
YeongHyeon Park
This repository contains the code used for Predicting Patient Outcomes with Graph Representation Learning (https://arxiv.org/abs/2101.03940).

Predicting Patient Outcomes with Graph Representation Learning This repository contains the code used for Predicting Patient Outcomes with Graph Repre

Emma Rocheteau 76 Dec 22, 2022
PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

Sharpness-aware Quantization for Deep Neural Networks Recent Update 2021.11.23: We release the source code of SAQ. Setup the environments Clone the re

Zhuang AI Group 30 Dec 19, 2022
Source code release of the paper: Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation.

GNet-pose Project Page: http://guanghan.info/projects/guided-fractal/ UPDATE 9/27/2018: Prototxts and model that achieved 93.9Pck on LSP dataset. http

Guanghan Ning 83 Nov 21, 2022
HiFi++: a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement

HiFi++ : a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement This is the unofficial implementation of Vocoder part of

Rishikesh (ऋषिकेश) 118 Dec 29, 2022
Deep Inside Convolutional Networks - This is a caffe implementation to visualize the learnt model

Deep Inside Convolutional Networks This is a caffe implementation to visualize the learnt model. Part of a class project at Georgia Tech Problem State

Jigar 61 Apr 15, 2022
Building a real-time environment using webcam frame division in OpenCV and classify cropped images using a fine-tuned vision transformers on hybryd datasets samples for facial emotion recognition.

Visual Transformer for Facial Emotion Recognition (FER) This project has the aim to build an efficient Visual Transformer for the Facial Emotion Recog

Mario Sessa 8 Dec 12, 2022
PyGCL: A PyTorch Library for Graph Contrastive Learning

PyGCL is a PyTorch-based open-source Graph Contrastive Learning (GCL) library, which features modularized GCL components from published papers, standa

PyGCL 588 Dec 31, 2022
PyTorch-centric library for evaluating and enhancing the robustness of AI technologies

Responsible AI Toolbox A library that provides high-quality, PyTorch-centric tools for evaluating and enhancing both the robustness and the explainabi

24 Dec 22, 2022
Code for the Active Speakers in Context Paper (CVPR2020)

Active Speakers in Context This repo contains the official code and models for the "Active Speakers in Context" CVPR 2020 paper. Before Training The c

43 Oct 14, 2022
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
BOVText: A Large-Scale, Multidimensional Multilingual Dataset for Video Text Spotting

BOVText: A Large-Scale, Bilingual Open World Dataset for Video Text Spotting Updated on December 10, 2021 (Release all dataset(2021 videos)) Updated o

weijiawu 47 Dec 26, 2022
Optimizing Deeper Transformers on Small Datasets

DT-Fixup Optimizing Deeper Transformers on Small Datasets Paper published in ACL 2021: arXiv Detailed instructions to replicate our results in the pap

16 Nov 14, 2022
A pytorch reprelication of the model-based reinforcement learning algorithm MBPO

Overview This is a re-implementation of the model-based RL algorithm MBPO in pytorch as described in the following paper: When to Trust Your Model: Mo

Xingyu Lin 93 Jan 05, 2023
Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal, multi-exposure and multi-focus image fusion.

U2Fusion Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal (VIS-IR, medical), multi

Han Xu 129 Dec 11, 2022
Lip Reading - Cross Audio-Visual Recognition using 3D Convolutional Neural Networks

Lip Reading - Cross Audio-Visual Recognition using 3D Convolutional Neural Networks - Official Project Page This repository contains the code develope

Amirsina Torfi 1.7k Dec 18, 2022
Explaining Hyperparameter Optimization via PDPs

Explaining Hyperparameter Optimization via PDPs This repository gives access to an implementation of the methods presented in the paper submission “Ex

2 Nov 16, 2022
Clustering with variational Bayes and population Monte Carlo

pypmc pypmc is a python package focusing on adaptive importance sampling. It can be used for integration and sampling from a user-defined target densi

45 Feb 06, 2022
CenterNet:Objects as Points目标检测模型在Pytorch当中的实现

CenterNet:Objects as Points目标检测模型在Pytorch当中的实现

Bubbliiiing 267 Dec 29, 2022
Tensorflow implementation of MIRNet for Low-light image enhancement

MIRNet Tensorflow implementation of the MIRNet architecture as proposed by Learning Enriched Features for Real Image Restoration and Enhancement. Lanu

Soumik Rakshit 91 Jan 06, 2023
Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification

Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification This repository is the official implementation of [Dealing With Misspeci

0 Oct 25, 2021