Unsupervised Collection

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🔮Psychic

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“文本挖掘和预处理工具（文本清洗、新词发现、情感分析、实体识别链接、关键词抽取、知识抽取、句法分析等），无监督或弱监督方法”

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🔮Psychic

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“(CVPR'20 Oral) Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild”

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🔮Psychic

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“Pytorch version of SfmLearner from Tinghui Zhou et al.”

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🔮Psychic

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“Machine learning movie recommending system”

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🔮Psychic

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“Official PyTorch implementation of SynDiff described in the paper (https://arxiv.org/abs/2207.08208).”

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🔮Psychic

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“A Non-Autoregressive Transformer based Text-to-Speech, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate TTS”

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🔮Psychic

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“CVPR2018: Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatio-temporal Patterns”

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🔮Psychic

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“This project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning.”

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🔮Psychic

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“Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model”

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🔮Psychic

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“Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"”

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🔮Psychic

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“DualGAN-tensorflow: tensorflow implementation of DualGAN”

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🔮Psychic

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“A high performance impermentation of Unsupervised Image Segmentation by Backpropagation - Asako Kanezaki”

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⚙️Steel

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“Dynamically get the suggested clusters in the data for unsupervised learning.”

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🔮Psychic

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“Official implementation of SuperSimpleNet [ICPR 2024, JIMS 2025]”

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🔮Psychic

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“(ECCV 2022) Code for Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency”

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🔮Psychic

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“A Non-Autoregressive End-to-End Text-to-Speech (text-to-wav), supporting a family of SOTA unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate E2E-TTS”

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🔮Psychic

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“[ICCV 2021] Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation”

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🔮Psychic

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“This repository tries to provide unsupervised deep learning models with Pytorch”