#Self-supervised Learning

Discover a curated compilation of research papers on self-supervised learning in graph neural networks (GNNs), organized by year. This resource includes widely-cited studies and offers access to related code for further exploration. Stay informed on recent developments, suggest additions, or report errors. Topics covered include community detection, representation learning, and anomaly detection, with insights into pioneering techniques and applications.

This article introduces a major advancement in computational pathology with the UNI model, a self-supervised model trained on a vast dataset of over 100 million histopathology images across 20 different tissue types. It addresses the complexities of annotating high-resolution whole-slide images and excels in 34 clinical tasks, including resolution-agnostic tissue classification and few-shot classification of up to 108 cancer types. Learn how UNI enhances data efficiency and adapts to clinical workflows, surpassing existing models in performance across diverse diagnostic challenges.

TensorFlow Similarity provides advanced algorithms for metric learning, covering self-supervised, similarity, and contrastive learning methods. It supports the process of training and evaluating models through integrated tools like losses, metrics, and samplers. This library is engineered for simplicity in use, backed by distributed training capabilities and multi-modal embedding support. Users can leverage examples available on Google Colab and thorough documentation to efficiently implement and refine models for image clustering and retrieval.

ZMM-TTS is a framework that utilizes self-supervised discrete speech representations for multilingual and multispeaker text-to-speech synthesis. The model integrates text-based and speech-based self-supervised learning models to improve speech naturalness and speaker similarity in high-resource languages. It effectively performs zero-shot speech synthesis in low-resource languages, providing high intelligibility and speaker resemblance without prior data. Discover the pre-trained models for six languages and the MM6 dataset, created for balanced multilingual training. Explore speech synthesis in different languages with ZMM-TTS.

This PyTorch-based self-supervised framework excels in generating unconditional images at 256x256 resolution on ImageNet. It closes the traditional gap between unconditional and class-conditional generation, enhancing self-representation generation techniques. Latest updates feature enhanced FID evaluation via the ADM suite and new training scripts for DiT-XL with RCG. Utilizing GPUs for efficient training, the framework also offers pre-trained weights and flexible customization options with various pixel generators such as MAGE, DiT, ADM, and LDM. Visit the project's repository for detailed setup and evaluation guidance for image generation projects.