Deep Internal Learning

Deep internal learning focuses on training neural networks using limited or single input data, contrasting with traditional deep learning's reliance on large labeled datasets. Current research explores this paradigm using diffusion models for tasks like video inpainting and adapting vision transformers for efficient inference through techniques like early exiting, often leveraging the inherent structure within the input data itself. This approach holds significant promise for applications where labeled data is scarce or computationally expensive to acquire, potentially advancing fields like signal processing, computer vision, and even neuroscience modeling by offering more efficient and adaptable AI systems.