Binary Stochastic Neurons in PyTorch

Last update: Nov 21, 2022

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Overview

Binary Stochastic Neurons in PyTorch

Results

Model	Epoch	Accuracy
BinaryNet-Deterministic-REINFORCE-False	41	0.8086
BinaryNet-Deterministic-REINFORCE-True	33	0.8128
BinaryNet-Deterministic-ST-False	94	0.972
BinaryNet-Deterministic-ST-True	63	0.9709
BinaryNet-Stochastic-REINFORCE-False	19	0.5937
BinaryNet-Stochastic-REINFORCE-True	97	0.7095
BinaryNet-Stochastic-ST-False	59	0.9748
BinaryNet-Stochastic-ST-True	89	0.9717
NonBinaryNet-None-None-False	76	0.9734
NonBinaryNet-None-None-True	50	0.9714

Owner

Onur Kaplan

GitHub Repository

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Binary Stochastic Neurons in PyTorch

Related tags

Overview

Binary Stochastic Neurons in PyTorch

Results

Owner

Onur Kaplan

Activating More Pixels in Image Super-Resolution Transformer

Official Pytorch Implementation of GraphiT

Contains code for Deep Kernelized Dense Geometric Matching

This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data

An AutoML Library made with Optuna and PyTorch Lightning

Lightweight plotting to the terminal. 4x resolution via Unicode.

ZSL-KG is a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representation from common sense knowledge graphs.

Studying Python release adoptions by looking at PyPI downloads

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Robust Instance Segmentation through Reasoning about Multi-Object Occlusion [CVPR 2021]

Official implementation of Rich Semantics Improve Few-Shot Learning (BMVC, 2021)

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Try out deep learning models online on Google Colab

Pocsploit is a lightweight, flexible and novel open source poc verification framework

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