Adaout is a practical and flexible regularization method with high generalization and interpretability

Related tags

Deep LearningAdaout
Overview

Adaout

Adaout is a practical and flexible regularization method with high generalization and interpretability.

Requirements

  • python 3.6 (Anaconda version >=5.2.0 is recommended)
  • torch (torch version >=1.1.0 is recommended)
  • torchvision (torchvision version >=0.3.0 is recommended)
  • pandas
  • numpy
  • NVIDIA GPU + CUDA CuDNN

Datasets

  • CIFAR-10, CIFAR-100, SVHN, ImageNet and others

Getting started

  • Download datasets and extract it inside data
  • Train: python train.py, python train100.py or python train_svhn.py
  • Evaluate:
    • Pretrained models for CIFAR-10 and CIFAR-100 are available at this link. Download and extract them in the save_model/resnet56_10 or save_model/resnet56_100 directory.
    • You should achieve about 94.63% accuracy on CIFAR-10, and 74.18% accuracy on CIFAR-100 datasets.
Owner
lambett
lambett
Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

Core ML Tools Use coremltools to convert machine learning models from third-party libraries to the Core ML format. The Python package contains the sup

Apple 3k Jan 08, 2023
[CVPR 2021] 'Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator'

[CVPR2021] Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator Overview This is the entire codebase for the paper

35 Dec 01, 2022
I created My own Virtual Artificial Intelligence named genesis, He can assist with my Tasks and also perform some analysis,,

Virtual-Artificial-Intelligence-genesis- I created My own Virtual Artificial Intelligence named genesis, He can assist with my Tasks and also perform

AKASH M 1 Nov 05, 2021
Convert ONNX model graph to Keras model format.

Convert ONNX model graph to Keras model format.

Grigory Malivenko 175 Dec 28, 2022
Exploration of some patients clinical variables.

Answer_ALS_clinical_data Exploration of some patients clinical variables. All the clinical / metadata data is available here: https://data.answerals.o

1 Jan 20, 2022
Library for fast text representation and classification.

fastText fastText is a library for efficient learning of word representations and sentence classification. Table of contents Resources Models Suppleme

Facebook Research 24.1k Jan 01, 2023
KITTI-360 Annotation Tool is a framework that developed based on python(cherrypy + jinja2 + sqlite3) as the server end and javascript + WebGL as the front end.

KITTI-360 Annotation Tool is a framework that developed based on python(cherrypy + jinja2 + sqlite3) as the server end and javascript + WebGL as the front end.

86 Dec 12, 2022
A Fast Knowledge Distillation Framework for Visual Recognition

FKD: A Fast Knowledge Distillation Framework for Visual Recognition Official PyTorch implementation of paper A Fast Knowledge Distillation Framework f

Zhiqiang Shen 129 Dec 24, 2022
Inkscape extensions for figure resizing and editing

Academic-Inkscape: Extensions for figure resizing and editing This repository contains several Inkscape extensions designed for editing plots. Scale P

192 Dec 26, 2022
Graph Transformer Architecture. Source code for

Graph Transformer Architecture Source code for the paper "A Generalization of Transformer Networks to Graphs" by Vijay Prakash Dwivedi and Xavier Bres

NTU Graph Deep Learning Lab 561 Jan 08, 2023
cl;asification problem using classification models in supervised learning

wine-quality-predition---classification cl;asification problem using classification models in supervised learning Wine Quality Prediction Analysis - C

Vineeth Reddy Gangula 1 Jan 18, 2022
Original Pytorch Implementation of FLAME: Facial Landmark Heatmap Activated Multimodal Gaze Estimation

FLAME Original Pytorch Implementation of FLAME: Facial Landmark Heatmap Activated Multimodal Gaze Estimation, accepted at the 17th IEEE Internation Co

Neelabh Sinha 19 Dec 17, 2022
Spherical CNNs

Spherical CNNs Equivariant CNNs for the sphere and SO(3) implemented in PyTorch Overview This library contains a PyTorch implementation of the rotatio

Jonas Köhler 893 Dec 28, 2022
Code for "Adversarial attack by dropping information." (ICCV 2021)

AdvDrop Code for "AdvDrop: Adversarial Attack to DNNs by Dropping Information(ICCV 2021)." Human can easily recognize visual objects with lost informa

Ranjie Duan 52 Nov 10, 2022
Build an Amazon SageMaker Pipeline to Transform Raw Texts to A Knowledge Graph

Build an Amazon SageMaker Pipeline to Transform Raw Texts to A Knowledge Graph This repository provides a pipeline to create a knowledge graph from ra

AWS Samples 3 Jan 01, 2022
Implementation of NÜWA, state of the art attention network for text to video synthesis, in Pytorch

NÜWA - Pytorch (wip) Implementation of NÜWA, state of the art attention network for text to video synthesis, in Pytorch. This repository will be popul

Phil Wang 463 Dec 28, 2022
Code release for "COTR: Correspondence Transformer for Matching Across Images"

COTR: Correspondence Transformer for Matching Across Images This repository contains the inference code for COTR. We plan to release the training code

UBC Computer Vision Group 360 Jan 06, 2023
Source code for "Progressive Transformers for End-to-End Sign Language Production" (ECCV 2020)

Progressive Transformers for End-to-End Sign Language Production Source code for "Progressive Transformers for End-to-End Sign Language Production" (B

58 Dec 21, 2022
The codebase for our paper "Generative Occupancy Fields for 3D Surface-Aware Image Synthesis" (NeurIPS 2021)

Generative Occupancy Fields for 3D Surface-Aware Image Synthesis (NeurIPS 2021) Project Page | Paper Xudong Xu, Xingang Pan, Dahua Lin and Bo Dai GOF

xuxudong 97 Nov 10, 2022
CrossNorm and SelfNorm for Generalization under Distribution Shifts (ICCV 2021)

CrossNorm (CN) and SelfNorm (SN) (Accepted at ICCV 2021) This is the official PyTorch implementation of our CNSN paper, in which we propose CrossNorm

100 Dec 28, 2022