  We present an efficient, effective, and generic approach towards solving
inverse problems. The key idea is to leverage the feedback signal provided by
the forward process and learn an iterative update model. Specifically, at each
iteration, the neural network takes the feedback as input and outputs an update
on the current estimation. Our approach does not have any restrictions on the
forward process; it does not require any prior knowledge either. Through the
feedback information, our model not only can produce accurate estimations that
are coherent to the input observation but also is capable of recovering from
early incorrect predictions. We verify the performance of our approach over a
wide range of inverse problems, including 6-DOF pose estimation, illumination
estimation, as well as inverse kinematics. Comparing to traditional
optimization-based methods, we can achieve comparable or better performance
while being two to three orders of magnitude faster. Compared to deep
learning-based approaches, our model consistently improves the performance on
all metrics. Please refer to the project page for videos, animations,
supplementary materials, etc.
