  Implicit neural representations (INR) have gained significant popularity for
signal and image representation for many end-tasks, such as superresolution, 3D
modeling, and more. Most INR architectures rely on sinusoidal positional
encoding, which accounts for high-frequency information in data. However, the
finite encoding size restricts the model's representational power. Higher
representational power is needed to go from representing a single given image
to representing large and diverse datasets. Our approach addresses this gap by
representing an image with a polynomial function and eliminates the need for
positional encodings. Therefore, to achieve a progressively higher degree of
polynomial representation, we use element-wise multiplications between features
and affine-transformed coordinate locations after every ReLU layer. The
proposed method is evaluated qualitatively and quantitatively on large datasets
like ImageNet. The proposed Poly-INR model performs comparably to
state-of-the-art generative models without any convolution, normalization, or
self-attention layers, and with far fewer trainable parameters. With much fewer
training parameters and higher representative power, our approach paves the way
for broader adoption of INR models for generative modeling tasks in complex
domains. The code is available at \url{https://github.com/Rajhans0/Poly_INR}
