Code for Unsupervised Learning of Compositional Energy Concepts

This is the pytorch code for the paper Unsupervised Learning of Compositional Energy Concepts. Demo Please download a pretrained model at this link and then execute the following code to test a pretrained CelebA-HQ 128×128 COMET model python demo.py im_path=im0.png Please utilize the following command to run global factor decomposition on CelebA-HQ (or other datasets) python train.py –exp=celebahq –batch_size=12 –gpus=1 –cuda –train –dataset=celebahq –step_lr=500.0 You may further run the code on high-resolution 128×128 images below python train.py –exp=celebahq_128    

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Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows

CFLOW-AD CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing FlowsWACV 2022 preprint:https://arxiv.org/abs/2107.12571 Abstract Unsupervised anomaly detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly examples are completely missing in the train data. While recently proposed models for such data setup achieve high accuracy metrics, their complexity is a limiting factor for real-time processing. In this paper, we propose a real-time model and analytically derive its relationship to prior methods. Our CFLOW-AD model […]

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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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Getting started with NLP using NLTK Library

1010010   01101001   01110100   01101000   01101001  01101011   01100001 Did you understand the above binary code? If yes, then you’re a computer. If no, then you’re a Human. 🙂 I know it’s a difficult task for us to understand binary code just like computers because binary code is a Machine Understandable Language. Likewise, even computers don’t understand human language. So, how to make computers understand human language? The answer is Natural Language Processing. With the help of NLP, we can teach computers […]

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Let’s Understand How does a chatbot work ?

Introduction A technology that makes the interaction between humans and machines in natural language possible, is an Artificial Intelligence Chatbot! They act like a typical search engine but with more enhanced features. Applications of Artificial Intelligence Chatbots are spread over various domains including eCommerce, healthcare, education, travel, automation, finance, hospitality, insurance, and so on. The chatbots are domain-specific and do what they are intended for.  The applications in their domain include: answering customer queries, booking services like flights, movie tickets, […]

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Topic Modeling and Latent Dirichlet Allocation(LDA) using Gensim and Sklearn : Part 1

This article was published as a part of the Data Science Blogathon Introduction Let’s say you have a client who has a publishing house. Your client comes to you with two tasks: one he wants to categorize all the books or the research papers he receives weekly on a common theme or a topic and the other task is to encapsulate large documents into smaller bite-sized texts. Is there any technique and tool available that can do both of these two […]

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Implementing Transformers in NLP Under 5 Lines Of Codes

This article was published as a part of the Data Science Blogathon Introduction Today, we will see a gentle introduction to the transformers library for executing state-of-the-art models for complex NLP tasks. Applying state-of-the-art Natural Language Processing models has never been more straightforward. Hugging Face has revealed a compelling library called transformers that allow us to perform and use a broad class of state-of-the-art NLP models in a specific way. Today we are operating to install and use the transformers library […]

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Part 10: Step by Step Guide to Master NLP – Named Entity Recognition

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 article, we discussed semantic analysis, which is a level of NLP tasks. In that article, we discussed the techniques of Semantic analysis in which we discussed one technique named entity extraction, which is very important to understand in NLP. So, In this article, we will deep dive into the entity extraction […]

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Natural Language Processing Step by Step Guide

This article was published as a part of the Data Science Blogathon Overview Basic understanding of Natural Language Processing. Learn Various Techniques used for the implementation of NLP. Understand how to use NLP for text mining. Prerequisite You must have a basic knowledge of Python. As we know every piece of data has some meaning in its position. Most important is that text data is getting generated in various formats like reviews, SMS, emails, and many more for every moment. The […]

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Build your own NLP based search engine Using BM25

Introduction Ever wondered how these search engines like Google and Yahoo work. And ever thought about how can they scan all through the internet and return relevant results in just About 5,43,00,000 results (0.004seconds). Well, they work on the concept of Crawling and Indexing. Crawling: Automated bots looks for pages that are new or updated. And stores the key information like — URL, title, keywords, and so on from the pages to be used later. Indexing: Data captured from crawling is analyzed […]

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