  With the advent of brain imaging techniques and machine learning tools, much
effort has been devoted to building computational models to capture the
encoding of visual information in the human brain. One of the most challenging
brain decoding tasks is the accurate reconstruction of the perceived natural
images from brain activities measured by functional magnetic resonance imaging
(fMRI). In this work, we survey the most recent deep learning methods for
natural image reconstruction from fMRI. We examine these methods in terms of
architectural design, benchmark datasets, and evaluation metrics and present a
fair performance evaluation across standardized evaluation metrics. Finally, we
discuss the strengths and limitations of existing studies and present potential
future directions.
