26 Repositories
Python Viewpoint-Bottleneck Libraries
Adversarial-Information-Bottleneck - Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck (NeurIPS21)
NeurIPS 2021 Title: Distilling Robust and Non-Robust Features in Adversarial Exa
Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)"
BAM and CBAM Official PyTorch code for "BAM: Bottleneck Attention Module (BMVC2018)" and "CBAM: Convolutional Block Attention Module (ECCV2018)" Updat
Official code for the ICCV 2021 paper "DECA: Deep viewpoint-Equivariant human pose estimation using Capsule Autoencoders"
DECA Official code for the ICCV 2021 paper "DECA: Deep viewpoint-Equivariant human pose estimation using Capsule Autoencoders". All the code is writte
Dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
Dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
Profile and test to gain insights into the performance of your beautiful Python code
Profile and test to gain insights into the performance of your beautiful Python code View Demo - Report Bug - Request Feature QuickPotato in a nutshel
Semantic Bottleneck Scene Generation
SB-GAN Semantic Bottleneck Scene Generation Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the f
Official repository of ICCV21 paper "Viewpoint Invariant Dense Matching for Visual Geolocalization"
Viewpoint Invariant Dense Matching for Visual Geolocalization: PyTorch implementation This is the implementation of the ICCV21 paper: G Berton, C. Mas
Implementation of the paper 'Sentence Bottleneck Autoencoders from Transformer Language Models'
Introduction This repository contains the code for the paper Sentence Bottleneck Autoencoders from Transformer Language Models by Ivan Montero, Nikola
Bottleneck a collection of fast, NaN-aware NumPy array functions written in C.
Bottleneck Bottleneck is a collection of fast, NaN-aware NumPy array functions written in C. As one example, to check if a np.array has any NaNs using
TensorFlow implementation of Barlow Twins (Barlow Twins: Self-Supervised Learning via Redundancy Reduction)
Barlow-Twins-TF This repository implements Barlow Twins (Barlow Twins: Self-Supervised Learning via Redundancy Reduction) in TensorFlow and demonstrat
Official PyTorch Implementation for InfoSwap: Information Bottleneck Disentanglement for Identity Swapping
InfoSwap: Information Bottleneck Disentanglement for Identity Swapping Code usage Please check out the user manual page. Paper Gege Gao, Huaibo Huang,
Dynamic Bottleneck for Robust Self-Supervised Exploration
Dynamic Bottleneck Introduction This is a TensorFlow based implementation for our paper on "Dynamic Bottleneck for Robust Self-Supervised Exploration"
Detecting Beneficial Feature Interactions for Recommender Systems, AAAI 2021
Detecting Beneficial Feature Interactions for Recommender Systems (L0-SIGN) This is our implementation for the paper: Su, Y., Zhang, R., Erfani, S., &
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021)
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021) The implementation of Reducing Infromation Bottleneck for W
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021)
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021) The implementation of Reducing Infromation Bottleneck for W
A self-supervised 3D representation learning framework named viewpoint bottleneck.
Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck Paper Created by Liyi Luo, Beiwen Tian, Hao Zhao and Guyue Zhou from Institute for AI In
A self-supervised 3D representation learning framework named viewpoint bottleneck.
Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck Paper Created by Liyi Luo, Beiwen Tian, Hao Zhao and Guyue Zhou from Institute for AI In
Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch
Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch
Evidence enables analysts to deliver a polished business intelligence system using SQL and markdown.
Evidence enables analysts to deliver a polished business intelligence system using SQL and markdown
PyTorch implementations for our SIGGRAPH 2021 paper: Editable Free-viewpoint Video Using a Layered Neural Representation.
st-nerf We provide PyTorch implementations for our paper: Editable Free-viewpoint Video Using a Layered Neural Representation SIGGRAPH 2021 Jiakai Zha
Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
On the Bottleneck of Graph Neural Networks and its Practical Implications This is the official implementation of the paper: On the Bottleneck of Graph
Code reproduce for paper "Vehicle Re-identification with Viewpoint-aware Metric Learning"
VANET Code reproduce for paper "Vehicle Re-identification with Viewpoint-aware Metric Learning" Introduction This is the implementation of article VAN
codes for Image Inpainting with External-internal Learning and Monochromic Bottleneck
Image Inpainting with External-internal Learning and Monochromic Bottleneck This repository is for the CVPR 2021 paper: 'Image Inpainting with Externa
《Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement》(ECCV 2020) GitHub: [fig9]
Unsupervised 3D Human Pose Representation [Paper] The implementation of our paper Unsupervised 3D Human Pose Representation with Viewpoint and Pose Di
Implementation of Bottleneck Transformer in Pytorch
Bottleneck Transformer - Pytorch Implementation of Bottleneck Transformer, SotA visual recognition model with convolution + attention that outperforms
Bottleneck Transformers for Visual Recognition
Bottleneck Transformers for Visual Recognition Experiments Model Params (M) Acc (%) ResNet50 baseline (ref) 23.5M 93.62 BoTNet-50 18.8M 95.11% BoTNet-