Author: Deep Learner

Deep Learning

Official Pytorch+Lightning Implementation for NU-Wave

NU-Wave NU-Wave: A Diffusion Probabilistic Model for Neural Audio UpsamplingJunhyeok Lee, Seungu Han @ MINDsLab Inc., SNU Paper(arXiv): https://arxiv.org/abs/2104.02321 (Accepted to INTERSPEECH 2021)Audio Samples: https://mindslab-ai.github.io/nuwave Official Pytorch+Lightning Implementation for NU-Wave. Requirements Preprocessing Before running our project, you need to download and preprocess dataset to .pt files Download VCTK dataset Remove speaker p280 and p315 Modify path of downloaded dataset data:dir in hparameters.yaml run utils/wav2pt.py $ python utils/wav2pt.py Training Adjust hparameters.yaml, especially train section. train: batch_size: 18 # Dependent on GPU […]

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Deep Learning

Beyond Image to Depth: Improving Depth Prediction using Echoes

beyond-image-to-depth We address the problem of estimating depth with multi modal audio visual data. Inspired by the ability of animals, such as bats and dolphins, to infer distance of objects with echolocation, we propose an end-to-end deep learning based pipeline utilizing RGB images, binaural echoes and estimated material properties of various objects within a scene for the task of depth estimation. Requirements The code is tesed with – Python 3.6 – PyTorch 1.6.0 – Numpy 1.19.5 Dataset Replica-VisualEchoes can be […]

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Machine Learning

A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations

S2VC Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations. In this paper, we proposed S2VC which utilizes Self-Supervised pretrained representation to provide the latent phonetic structure of the utterance from the source speaker and the spectral features of the utterance from the target speaker. The following is the overall model architecture. For the audio samples, please refer to our demo page. Usage You can download the pretrained model as well […]

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Deep Learning

Deep Detail Enhancement for Any Garment

Deep-Detail-Enhancement-for-Any-Garment This repository contains the implementation of Deep Detail Enhancment for Any Garment proposed in Eurographics 2021 Ref. to [http://geometry.cs.ucl.ac.uk/projects/2021/DeepDetailEnhance/paper_docs/DeepDetailEnhance.pdf] We provide Google drive links for downloading the training data, the network checkpoint and two multi-layer garment models (Marvelouse Desigener): Training data Checkpoint MD Model ./network_train_and_run This folder contains the pytorch implemetation of deep detail enhancement network and the material classifier. In order to generalize our approach across different 2D parameterizations, we adopt a patch-based approach. Instead of operating with […]

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Polygonization

Progressively Normalized Self-Attention Network for Video Polyp Segmentation

PNS-Net This repository provides code for paper”Progressively Normalized Self-Attention Network for Video Polyp Segmentation” published at the MICCAI-2021 conference (arXiv Version | δΈ­ζ–‡η‰ˆ). If you have any questions about our paper, feel free to contact me. And if you like our PNS-Net or evaluation toolbox for your personal research, please cite this paper (BibTeX). Features Hyper Real-time Speed: Our method, named Progressively Normalized Self-Attention Network (PNS-Net), can efficiently learn representations from polyp videos with real-time speed (~140fps) on a single […]

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Dataset

Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets

This is the official PyTorch implementation for the paper Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets (ICLR 2021) : https://openreview.net/forum?id=rkQuFUmUOg3. Abstract Despite the success of recent Neural Architecture Search (NAS) methods on various tasks which have shown to output networks that largely outperform human-designed networks, conventional NAS methods have mostly tackled the optimization of searching for the network architecture for a single task (dataset), which does not generalize well across multiple tasks (datasets). Moreover, since such […]

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Graph

Self-supervised Graph-level Representation Learning with Local and Global Structure

GraphLoG This project is an implementation of ‘Self-supervised Graph-level Representation Learning with Local and Global Structure’ in PyTorch, which is accepted as Short Talk by ICML 2021. We provide the pre-training and fine-tuning codes and also the pre-trained model on chemistry domain in this repository, and a more complete code version including the biology domain will be announced on the TorchDrug platform developed by MilaGraph group. Also, we would like to appreciate the excellent work of Pretrain-GNNs which lays a […]

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Computer Vision

A technology that adds computer-generated virtual content to real-world views through devices

Augmented Reality 101 The development of areas such as computer vision, image processing, and computer graphics, allow the introduction of technologies such as Augmented Reality. Azuma defines Augmented Reality as “a technology that adds computer-generated virtual content to real-world views through devices”. Introduction The purpose of these map is to give you an idea about Augmented Reality and to guide you through the main features that surround this technology. Read complete post in AR 101 β€” Augmented Reality. Definition and […]

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Deep Learning

A Mindmap summarising Machine Learning concepts from Data Analysis to Deep Learning

Machine Learning Mindmap / Cheatsheet A Mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning. Machine Learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data. Machine Learning is as fascinating as it is broad in scope. It spans over multiple fields in Mathematics, Computer Science, and Neuroscience. This is an attempt to […]

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Deep Learning

A server-client system that facilitates interactive medical image annotation by using AI

MONAI Label MONAI Label is a server-client system that facilitates interactive medical image annotation by using AI. It is an open-source and easy-to-install ecosystem that can run locally on a machine with one or two GPUs. Both server and client work on the same/different machine. However, initial support for multiple users is restricted. It shares the same principles with MONAI. Features The codebase is currently under active development. framework for developing and deploying MONAI Label Apps to train and infer […]

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