paper summary “Perceiver IO: A General Architecture for Structured Inputs & Outputs”

arxiv: https://arxiv.org/abs/2107.14795 Key points developing upon the Perceiver idea, Perceiver IO proposes a Perceiver like structure but where output size can be much larger and still keep overall complexity linear. (Checkout summary on Perceiver here) same with Perceiver, this work use latent array to save input information and run this through multiple self Read more…

paper summary: Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

arxiv: https://arxiv.org/abs/2103.14030 Key points multi scale feature extraction. Could think of as adoption of FPN idea. restrict transformer operation to within each window and not the entire feature map → allows to keep overall complexity linear instead of quadratic apply shifted window to allow inter-window interaction fuse relative position information in Read more…

visual code debug configuration variables

from the official docs: https://code.visualstudio.com/docs/editor/variables-reference Predefined variables The following predefined variables are supported: ${workspaceFolder} – the path of the folder opened in VS Code ${workspaceFolderBasename} – the name of the folder opened in VS Code without any slashes (/) ${file} – the current opened file ${fileWorkspaceFolder} – the current opened file’s workspace folder ${relativeFile} – Read more…

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…

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…