With coordinates as the input and RGB pixel values as the output, a neural network can be used to represent an image, which is widely known as Implicit neural representations (INRs). Previous works on INR have mainly focused on learning an invariant image target without exploring the impact of learning strategies on learning INR. It is observed that there is a substantial variation in PSNR among different images, and our preliminary investigation shows that, in the early training stage, learning complex image content yields significantly better performance than simple image content. Inspired by this finding, we conjecture that increasing INR task complexity in the early stage of training might boost INR performance and thus propose to intentionally contaminate the target image with another complex image. Our proposed method is called Mix-INR, which adopts a two-stage training to first learn a pseudo-target image (contaminated target) and then learn the real-target image (uncontaminated target). To generate the pseudo-target image, we experiment with two contamination methods (blending and replacement), both of which show superior performance and verify our conjecture. INRs have gained popularity as a promising approach for representing a variety of data types, including images of the task complexity of the pseudo-target image, we set the contamination image from a complex natural image to a random-noise image. Moreover, we propose a dynamic contamination method to smoothly transition from the pseudo-target image to the real-target image. Experimental results demonstrate that our proposed method achieves competitive performance, which suggests that INR can be improved by manipulating the task complexity in the early stage of training.

Implicit neural representations (INRs), which leverage neural networks to represent signals by mapping coordinates to their corresponding attributes, have garnered significant attention. They are extensively utilized for image representation, with pixel coordinates as input and pixel values as output. In contrast to prior works focusing on investigating the effect of the model's inside components (activation function, for instance), this work pioneers the exploration of the effect of kernel transformation of input/output while keeping the model itself unchanged. A byproduct of our findings is a simple yet effective method that combines scale and shift to significantly boost INR with negligible computation overhead. Moreover, we present two perspectives, depth and normalization, to interpret the performance benefits caused by scale and shift transformation. Overall, our work provides a new avenue for future works to understand and improve INR through the lens of kernel transformation.