Firstly,when all the operands (i.e., weights, activations, errors and gradients) for general matrix multiplication (GEMM) and convolution computations are reduced to 8 bits, most DNNs suffer noticeable accuracy degradation (e.g., Figure 1(a)).

To address this issue, several studies have been conducted and have shown promising results.

Current test-time adaptation (TT A) approaches aim to adapt a machine learning model to environments that change continuously.

Below, we will introduce the details of each stage separately. In practical applications of image steganography, it is common to hide a single subject in an image, and this is also a problem that our method excels at solving. We employed two methods to obtain "Prompt1" and "Prompt2": an ChatGPT to generate the modified "Prompt2". The specific process of generating "Prompt2" is shown in Fig. A.1. We present examples from the Stego260 dataset in Fig. A.2, where each example consists of an image We show images from three categories: humans, animals, and general objects.

Natural images captured by mobile devices often suffer from multiple types of degradation, such as noise, blur, and low light.

Low-complexity single-image super-resolution based on nonnegative neighbor embedding.

Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images.

Under Display Camera (UDC) is a novel imaging system that mounts a digital camera lens beneath a display panel with the panel covering the camera.