paper summary: “Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization”

arxiv: https://arxiv.org/pdf/1703.06868.pdf key points arbitrary style transfer in real time use adaptive instance normalization(AdaIN) layers which aligns the mean and variance of content features allows to control content-style trade-off, style interpolation, color/spatial control previous works optimization approach using backprop of network to minimize style loss and content loss. This can be Read more…

few shot learning good articles

https://towardsdatascience.com/advances-in-few-shot-learning-a-guided-tour-36bc10a68b77 great brief summary over matching networks, prototypical networks, model-agnostic meta-learning(MAML). well, the first two topics are well explained but MAML section needs a lot more thinking to understand. Also, I think MAML is closer to the topic of meta-learning rather than few shot learning. https://arxiv.org/pdf/2008.06365.pdf “An Overview of Deep Read more…

paper summary: “Aggregated Residual Transformations for Deep Neural Networks” (ResNext Paper)

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…

paper review: “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”

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…

paper review: “Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data”

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…

paper review: “High-Performance Large-Scale Image Recognition Without Normalization”

arxiv: https://arxiv.org/pdf/2102.06171v1.pdf key points introduce NF nets which combines multiple ideas to avoid using batch norm to get on-par performance but along with just using a bunch of non-BN techniques, this paper introduces adaptive gradient clipping(AGC) to make it actually train well to reach comparable results matching that of using Read more…