  Seeing and recognizing an object whose size is much smaller than the
illumination wavelength is a challenging task for an observer placed in the far
field, due to the diffraction limit. Recent advances in near and far field
microscopy have offered several ways to overcome this limitation; however, they
often use invasive markers and require intricate equipment with complicated
image post-processing. On the other hand, a simple marker-free solution for
high-resolution imaging may be found by exploiting resonant metamaterial lenses
that can convert the subwavelength image information contained in the
near-field of the object to propagating field components that can reach the far
field. Unfortunately, resonant metalenses are inevitably sensitive to
absorption losses, which has so far largely hindered their practical
applications. Here, we solve this vexing problem and show that this limitation
can be turned into an advantage when metalenses are combined with deep learning
techniques. We demonstrate that combining deep learning with lossy metalenses
allows recognizing and imaging largely subwavelength features directly from the
far field. Our acoustic learning experiment shows that, despite being thirty
times smaller than the wavelength of sound, the fine details of images can be
successfully reconstructed and recognized in the far field, which is crucially
enabled by the presence of absorption. We envision applications in acoustic
image analysis, feature detection, object classification, or as a novel
noninvasive acoustic sensing tool in biomedical applications.
