#Self-supervised learning

Delve into implementations of diverse transformer models like seq2seq, encoder-only, and decoder-only architectures for AI and deep learning enthusiasts. This open-source inspired repository offers insights into transformer advancements, attention mechanisms, and position embeddings. It serves as a valuable reference for programming skill enhancement and AI model development. Note: Intended for self-study, not formal educational assignments.

This project delivers an extensive review of Masked Image Modeling (MIM) and associated techniques in self-supervised representation learning, presenting them in their historical sequence of development. It covers essential topics such as MIM for Transformers, contrastive learning, and applications in various modalities. The analysis includes the progression of self-supervised learning across diverse modalities, underscoring its pivotal role since 2018 in areas like NLP and Computer Vision. Contributions and revisions from the community are welcomed, along with resources such as curated paper lists and formats for academic citations. This is an essential resource for researchers and enthusiasts exploring the developments and practical applications in MIM.

Discover how self-supervised AI models developed by Meta AI accurately predict canopy heights from global satellite imagery. This innovative method provides critical data for forest research and management and boasts implementation ease through PyTorch tools, making it an essential asset for ecological monitoring.