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2021

Super Resolution: Adobe Photoshop versus Leading Deep Neural Networks.

Super Resolution is a technique that enhances the quality of an image by increasing its apparent resolution, effectively imagining the detail present in a higher-resolution version. Traditional methods like bicubic interpolation often result in blurred images when upscaling. Recent advancements have introduced more sophisticated approaches, including Adobe Camera Raw's Super Resolution and deep learning models such as the Information Distillation Network (IDN).

Rapid prototyping of network architectures using Super-Convergence using Cyclical Learning Rate schedules.

Super-convergence, achieved through cyclical learning rates, is a powerful yet underutilized technique in deep learning that significantly accelerates model training. By varying the learning rate between high and low boundaries, models can converge in a fraction of the time typically required. This method facilitates rapid prototyping of network architectures, optimization of loss functions, and experimentation with data augmentation, all while reducing training time by orders of magnitude.