key point compared to resnet, the residual blocks are upgraded to have multiple “paths” or as the paper puts it “cardinality” which can be treated as another model architecture design hyper parameter. resnext architectures that have sufficient cardinality shows improved performance tldr: use improved residual blocks compared to resnet Different Read more…
you can apply proxy settings to python requests like this:
instlal PyYAML in python file, load file like the following the data in yaml file will be saved in data variable as a python dict.
download latest poppler package from here. unzip the downloaded file, and obtain the path of ‘poppler-xx.xx.x/Library/bin’ directory. Add this path to windows environment variable PATH.
reference: https://www.urlencoder.io/python/
good simple explanation and figure: https://www.kaggle.com/residentmario/cosine-annealed-warm-restart-learning-schedulers
arxiv: https://arxiv.org/pdf/1905.11946.pdf key point propose ‘compound scaling method’ which scales all width/depth/resolution together which is an efficient scaling method that can be applied to any existing structure introduce a new family of baseline structure called ‘EfficientNets’. The very smallest baseline structure was found by authors through NAS, and then the Read more…
https://arxiv.org/pdf/1611.08408.pdf key point use adversarial net to distinguish if input masks are from segmentation net or ground truth. this allows to add auxiliary loss, like below: where mce: multi class cross-entropy bce: binary cross entropy s(): segmentation network a(): adversarial network y: ground truth x: input s(x): segmentation network output Read more…
https://arxiv.org/abs/1912.07768 This work suggests that surrogate data need not be drawn from the original data distribution.This paper investigates the question of whether we can train a data-generating network that can produce synthetic data that effectively and efficiently teaches a target task to a learner propose new method to create synthetic Read more…