This is to extend the SR-GAN and ESR-GAN to do blind super-resolution. The
problem statement is to reconstruct the high-resolution image from low
resolution (a.k.a. super-resolution), but without knowing how the low
resolution is derived from the original high-resolution image, i.e.,
blind super-resolution.
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Ledig et al (2017) Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
This is the “SR-GAN” paper. The problem of upscaling a photo with details is called “SISR” as in the title. This paper takes a 4x upscaling as an example problem and build a GAN model to do it.
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Explaining Attention Mechanism
Attention mechanism was first mentioned in Bahdanau et al (2015) paper titled “Neural Machine Translation by Jointly Learning to Align and Translate”, and Luong et al (2015) improved it with the paper “Effective Approaches to Attention-based Neural Machine Translation”. The key is to find the attention score $a_{ij}$ between two...
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Carion et al (2020) End-to-End Object Detection with Transformers
Object detection is to predict the bounding boxes and category labels for each object of interest. This paper proposed DETR (Detection Transformer) to predict all objects at once, trained end-to-end with a set loss function to perform bipartite matching between the predicted and groundtruth. It is found to perform better...
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Prokhorenkova et al (2018) CatBoost: Unbiased boosting with categorical features
CatBoost is a library for random forest. This paper describes the key feature behind it.
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