Compressed Multi-task embeddings for Data-Efficient Downstream training and inference in Earth Observation

arXiv:2403.17886v1 Announce Type: new
Abstract: As repositories of large scale data in earth observation (EO) have grown, so have transfer and storage costs for model training and inference, expending significant resources. We introduce Neural Embedding Compression (NEC), based on the transfer of compressed embeddings to data consumers instead of raw data. We adapt foundation models (FM) through learned neural compression to generate multi-task embeddings while navigating the tradeoff between compression rate and embedding utility. We update only a small fraction …

abstract arxiv compression consumers costs cs.lg data earth earth observation embedding embeddings inference nec observation repositories resources scale storage storage costs training transfer type