  In this paper, a novel multi-head multi-layer perceptron (MLP) structure is
presented for implicit neural representation (INR). Since conventional
rectified linear unit (ReLU) networks are shown to exhibit spectral bias
towards learning low-frequency features of the signal, we aim at mitigating
this defect by taking advantage of the local structure of the signals. To be
more specific, an MLP is used to capture the global features of the underlying
generator function of the desired signal. Then, several heads are utilized to
reconstruct disjoint local features of the signal, and to reduce the
computational complexity, sparse layers are deployed for attaching heads to the
body. Through various experiments, we show that the proposed model does not
suffer from the special bias of conventional ReLU networks and has superior
generalization capabilities. Finally, simulation results confirm that the
proposed multi-head structure outperforms existing INR methods with
considerably less computational cost.
