July 13, 2021 Advanced, NLP, Text

Part- 19: Step by Step Guide to Master NLP – Topic Modelling using LDA (Matrix Factorization Approach)

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In the previous part of this series, we completed our discussion on LDA, in probabilistic terms. Probably, this article is the last part on Topic modelling since we covered almost all important techniques used for Topic Modelling. So, In this article, we will discuss another approach, named matrix factorization to understand the LDA which […]

July 13, 2021 Beginner, NLP, Python, Text, Word Embeddings

Word2Vec For Word Embeddings -A Beginner’s Guide

This article was published as a part of the Data Science Blogathon Why are word embeddings needed? Let us consider the two sentences – “You can scale your business.” and “You can grow your business.”. These two sentences have the same meaning. If we consider a vocabulary considering these two sentences, it will constitute of these words: {You, can, scale, grow, your, business}. A one-hot encoding of these words would create a vector of length 6. The encodings for each of […]

July 12, 2021 Machine Learning

The winning solution of the Endocv-2021 grand challange

Endocv2021-winner This is the winning solution of the Endocv-2021 grand challange. Dependencies pytorch # tested with 1.7 and 1.8 torchvision tqdm pandas numpy albumentations # for augmentations torchsummary segmentation_models_pytorch # for basic segmentaion models pyra_pytorch # pyra_pytorch.PYRADatasetFromDF is used. But this can be replaced with normal pytorch dataset. Tri-Unet Block diagram of Tri-Unet How to train Tri-Unet and other basic models to DivergentNet? # To train Tri-unet python tri_unet.py train –num_epochs 2 –device_id 0 –train_CSVs sample_CSV_files/C1.csv sample_CSV_files/C1.csv –val_CSVs sample_CSV_files/C2.csv sample_CSV_files/C3.csv […]

July 12, 2021 Segmentation

Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion

BAAF-Net This repository is for BAAF-Net introduced in the following paper: “Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion”Shi Qiu, Saeed Anwar, Nick BarnesIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021) Paper and Citation The paper can be downloaded from here (CVF) or here (arXiv).If you find our paper/codes/results are useful, please cite: @inproceedings{qiu2021semantic, title={Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion}, author={Qiu, Shi and Anwar, Saeed and […]

July 12, 2021 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 […]

July 12, 2021 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 […]

July 12, 2021 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 […]

July 12, 2021 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 […]

July 12, 2021 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 | 中文版). 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 […]

July 12, 2021 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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