473 Open source projects that are alternatives of or similar to Mmt

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Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

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Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)

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Here is the official implementation of the model KD3A in paper "KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation".

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code for our TPAMI 2021 paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"

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The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

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PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations by T. Chen et al.

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Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation [Inoue+, CVPR2018].

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Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)

A PyTorch implementation for Adversarial Discriminative Domain Adaptation

(NeurIPS 2020 oral) Code for "Deep Transformation-Invariant Clustering" paper

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Official Tensorflow implementation of "Symmetrical Synthesis for Deep Metric Learning" (AAAI 2020)

Code for "Real-time self-adaptive deep stereo" - CVPR 2019 (ORAL)

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Official source code of "Batch DropBlock Network for Person Re-identification and Beyond" (ICCV 2019)

Meta Self-learning for Multi-Source Domain Adaptation： A Benchmark

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AutoDIAL Caffe Implementation

Pytorch implementation of β-VAE

ICCV2017 Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro

CorEx or "Correlation Explanation" discovers a hierarchy of informative latent factors. This reference implementation has been superseded by other versions below.

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(ICCV 2021) Code for "Unsupervised Layered Image Decomposition into Object Prototypes" paper

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A toolbox for domain adaptation and semi-supervised learning. Contributions welcome.