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Found 8 papers, 2 papers with code
Taming Lookup Tables for Efficient Image Retouching
Existing enhancement models often optimize for high performance while falling short of reducing hardware inference time and power consumption, especially on edge devices with constrained computing and storage resources.
Learning Spatially Collaged Fourier Bases for Implicit Neural Representation
Existing approaches to Implicit Neural Representation (INR) can be interpreted as a global scene representation via a linear combination of Fourier bases of different frequencies.
Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution
Conventional super-resolution (SR) schemes make heavy use of convolutional neural networks (CNNs), which involve intensive multiply-accumulate (MAC) operations, and require specialized hardware such as graphics processing units.
Lite it fly: An All-Deformable-Butterfly Network
Most deep neural networks (DNNs) consist fundamentally of convolutional and/or fully connected layers, wherein the linear transform can be cast as the product between a filter matrix and a data matrix obtained by arranging feature tensors into columns.
A Spectral Perspective towards Understanding and Improving Adversarial Robustness
Deep neural networks (DNNs) are incredibly vulnerable to crafted, imperceptible adversarial perturbations.
Frequency Regularization for Improving Adversarial Robustness
Deep neural networks are incredibly vulnerable to crafted, human-imperceptible adversarial perturbations.
Multimodal Transformer for Automatic 3D Annotation and Object Detection
By enriching the sparse point clouds, our method achieves 4. 48\% and 4. 03\% better 3D AP on KITTI moderate and hard samples, respectively, versus the state-of-the-art autolabeler.
What Do Adversarially trained Neural Networks Focus: A Fourier Domain-based Study
We are hopeful this work can shed light on the design of more robust neural networks.
