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Found 5 papers, 1 papers with code
Learn to See Faster: Pushing the Limits of High-Speed Camera with Deep Underexposed Image Denoising
The ability to record high-fidelity videos at high acquisition rates is central to the study of fast moving phenomena.
Optical Flow Regularization of Implicit Neural Representations for Video Frame Interpolation
Recent works have shown the ability of Implicit Neural Representations (INR) to carry meaningful representations of signal derivatives.
Reversible designs for extreme memory cost reduction of CNN training
This new kind of architecture enables training large neural networks on very limited memory, opening the door for neural network training on embedded devices or non-specialized hardware.
Assisting human experts in the interpretation of their visual process: A case study on assessing copper surface adhesive potency
Deep Neural Networks are often though to lack interpretability due to the distributed nature of their internal representations.
On zero-shot recognition of generic objects
We discuss how the presence of this new form of bias allows for a trivial solution to the standard benchmark and conclude on the need for a new benchmark.
