Explore a lecture on deep decoders and concise image representations from untrained networks. Delve into models for natural images, including wavelets, sparsity, sparse coding, and neural networks trained on large datasets. Examine how untrained neural nets can serve as models for natural images, focusing on the deep decoder architecture. Learn about image compression techniques and how the deep decoder compares to more complex neural network architectures. Investigate the application of deep decoders in solving inverse problems, particularly in image denoising. Understand the theoretical foundations behind why deep decoders work effectively, including their ability to fit limited noise. Compare the deep decoder's performance to other state-of-the-art methods like the deep image prior. Conclude with insights on how linear upsampling, ReLUs, and linear combinations can efficiently synthesize images, followed by a summary and Q&A session.