Figure 1: Overview of the Transformer block used in the PromptIR framework. As mentioned in section 3.1.2 Bias-free convolutions are utilized within this submodule. After MDT A Module the features are processed through the GDFN module. Our method effectively removes haze to produce visually better images.

This paper introduces a structured memory which can be easily integrated into a neural network. The memory is very large by design and significantly increases the capacity of the architecture, by up to a billion parameters with a negligible computational overhead.

In the "modern era", such model-parallel training techniques trace their roots back to AlexNet [14] and early influential systems such as DistBelief [6] and Project Adam [3].

Ethical questions are thus not sufficiently prominent in this work to warrant a dedicated discussion section. In general, we believe, this work will have an overall positive impact asitcan help shed light into theblack-box that isdeep learning.

In the process, it outperforms adversarial training, the most noteworthy defense mechanism,byawidemargin.