Neural Network

A-CNN: Annularly Convolutional Neural Networks on Point Clouds

A-CNN Created by Artem Komarichev, Zichun Zhong, Jing Hua from Department of Computer Science, Wayne State University. Introduction Our paper (arXiV) proposes a new approach to define and compute convolution directly on 3D point clouds by the proposed annular convolution. To appear, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019 A-CNN usage We provide the code of A-CNN model that was tested with Tensorflow 1.3.0, CUDA 8.0, and python 3.6 on Ubuntu 16.04. We […]

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Unsupervised

Improving Unsupervised Image Clustering With Robust Learning

RUC This repo is the PyTorch codes for “Improving Unsupervised Image Clustering With Robust Learning (RUC)” Improving Unsupervised Image Clustering With Robust Learning Sungwon Park, Sungwon Han, Sundong Kim, Danu Kim, Sungkyu Park, Seunghoon Hong, Meeyoung Cha. Highlight RUC is an add-on module to enhance the performance of any off-the-shelf unsupervised learning algorithms. RUC is inspired by robust learning. It first divides clustered data points into clean and noisy set, then refine the clustering results. With RUC, state-of-the-art unsupervised clustering […]

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Distributed

An Out-of-Distribution Detection Score For Variational Auto-encoder

Likelihood-Regret Official implementation of Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020. Training To train the VAEs, use appropriate arguments and run this command: python train_pixel.py Evaluation To evaluate likelihood regret’s OOD detection performance, run python compute_LR.py To evaluate likelihood ratio, run python test_likelihood_ratio.py To evaluate input complexity, run python test_inputcomplexity.py Above commands will save the numpy arrays containing the OOD scores for in-distribution and OOD samples in specific location, and to compute aucroc score, run […]

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Machine Learning

Pixel-level self-paced learning for super-resolution

This is an official implementaion of the paper Pixel-level Self-Paced Learning for Super-Resolution, which has been accepted by ICASSP 2020. [arxiv][PDF] trained model files: Baidu Pan(code: v0be) Requirements This code is forked from thstkdgus35/EDSR-PyTorch. In the light of its README, following libraries are required: Python 3.6+ (Python 3.7.0 in my experiments) PyTorch >= 1.0.0 (1.1.0 in my experiments) numpy skimage imageio matplotlib tqdm Core Parts Detail code can be found in Loss.forward, which can be simplified as: # take L1 […]

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Neural Network

Block-wisely Supervised Neural Architecture Search with Knowledge Distillation

This repository provides the code of our paper: Blockwisely Supervised Neural Architecture Search with Knowledge Distillation. Illustration of DNA. Each cell of the supernet is trained independently to mimic the behavior of the corresponding teacher block. Comparison of model ranking for DNA vs. DARTS, SPOS and MnasNet under two different hyper-parameters. Our Trained Models Usage 1. Requirements Install PyTorch (pytorch.org) Install third-party requirements Download the ImageNet dataset and move validation images to labeled subfolders 2. Searching The code for supernet […]

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PyTorch

Res2Net: A New Multi-scale Backbone Architecture

Res2Net The official pytorch implemention of the paper “Res2Net: A New Multi-scale Backbone Architecture” Our paper is accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). We propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-likeconnections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the rangeof receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models,e.g. , […]

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QUESTION ANSWERING

Unsupervised Multi-hop Question Answering by Question Generation

This repository contains code and models for the paper: Unsupervised Multi-hop Question Answering by Question Generation (NAACL 2021). We propose MQA-QG, an unsupervised question answering framework that can generate human-like multi-hop training pairs from both homogeneous and heterogeneous data sources. We find that we can train a competent multi-hop QA model with only generated data. The F1 gap between the unsupervised and fully-supervised models is less than 20 in both the HotpotQA and the HybridQA dataset. Pretraining a multi-hop QA […]

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3D

Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection

groomed_nms GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection CVPR 2021 Abhinav Kumar, Garrick Brazil, Xiaoming Liu project, supp, 5min_talk, slides, demo, poster, arxiv This code is based on Kinematic-3D, such that the setup/organization is very similar. A few of the implementations, such as classical NMS, are based on Caffe. References Please cite the following paper if you find this repository useful: @inproceedings{kumar2021groomed, title={{GrooMeD-NMS}: Grouped Mathematically Differentiable NMS for Monocular {$3$D} Object Detection}, author={Kumar, Abhinav and Brazil, Garrick […]

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Point Cloud

Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds

PAConv PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds by Mutian Xu*, Runyu Ding*, Hengshuang Zhao, and Xiaojuan Qi. Introduction This repository is built for the official implementation of: PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds (CVPR2021) [arXiv] If you find our work useful in your research, please consider citing: @inproceedings{xu2021paconv, title={PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds}, author={Xu, Mutian and Ding, Runyu and Zhao, Hengshuang and Qi, Xiaojuan}, […]

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Network

Regularizing Generative Adversarial Networks under Limited Data

lecam-gan Regularizing Generative Adversarial Networks under Limited Data Implementation for our GAN regularization method. The proposed regularization 1) improves the performance of GANs under limited training data, and 2) complements the exisiting data augmentation approches. Please note that this is not an officially supported Google product. Paper Please cite our paper if you find the code or dataset useful for your research. Regularizing Generative Adversarial Networks under Limited Data Hung-Yu Tseng, Lu Jiang, Ce Liu, Ming-Hsuan Yang, Weilong Yang Computer […]

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