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Found 5 papers, 3 papers with code
Tiny and Efficient Model for the Edge Detection Generalization
Operations such as edge detection, image enhancement, and super-resolution, provide the foundations for higher level image analysis.
Ranked #1 on
Edge Detection
on UDED
Efficient texture mapping via a non-iterative global texture alignment
We present a non-iterative method for seamless texture reconstruction of a given 3D scene.
IntrinSeqNet: Learning to Estimate the Reflectance from Varying Illumination
Intrinsic image decomposition describes an image based on its reflectance and shading components.
Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet
SimpNet outperforms the deeper and more complex architectures such as VGGNet, ResNet, WideResidualNet \etc, on several well-known benchmarks, while having 2 to 25 times fewer number of parameters and operations.
Ranked #110 on
Image Classification
on CIFAR-10
Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures
Our simple 13-layer architecture outperforms most of the deeper and complex architectures to date such as VGGNet, ResNet, and GoogleNet on several well-known benchmarks while having 2 to 25 times fewer number of parameters and operations.
Ranked #10 on
Image Classification
on MNIST
