Showing 1-20 of 24 projects
A comprehensive repository covering papers, codes, datasets, tutorials, and applications for transfer learning, domain adaptation, and more.
Open-source speech toolkit with state-of-the-art ASR, TTS, translation, and audio processing capabilities.
A flexible, modular PyTorch library for deep metric learning and embeddings, useful for vibe coders building AI-powered apps.
A self-supervised learning framework for computer vision tasks with a focus on contrastive learning.
A pre-training toolbox and benchmark for vision AI models, including self-supervised learning and state-of-the-art architectures.
A Python library for self-supervised learning on images, with a focus on computer vision and AI-powered tooling.
A toolkit for self-supervised speech pre-training and representation learning.
A Python library for graph deep learning research, focused on 3D graphs, explainable ML, and self-supervised learning.
A simple vision transformer baseline for human pose estimation, with pre-trained models and advanced capabilities.
An all-in-one computer vision toolkit for developers building AI-powered applications.
A repository of papers on pretraining and self-supervised learning for graph neural networks.
A self-supervised video representation learning model for video understanding tasks.
A library of self-supervised methods for visual representation learning powered by PyTorch Lightning.
A self-supervised learning framework for learning general human representations from unlabeled images.
Unsupervised image classification library using contrastive learning and SCAN algorithm.
A library for self-supervised learning on graphs, providing contrastive, generative, and predictive pretext tasks.
A PyTorch implementation of a BERT-style pretraining method for convolutional networks, enabling more efficient self-supervised learning.
A PyTorch-based library for semi-supervised learning in computer vision and deep learning.
All-in-one training for vision models with pretraining, fine-tuning, and distillation capabilities.
A comprehensive list of contrastive self-supervised learning papers for machine learning researchers.
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