TVRN combines invertible wavelet-based networks with a surrogate gradient approximator and compression-aware asymmetric design to improve frame-rate rescaling quality under real codecs.
The unreasonable effectiveness of deep features as a perceptual metric
3 Pith papers cite this work. Polarity classification is still indexing.
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RGT-Est transforms relative geologic time estimation into a sinusoidal space and applies pointwise, perceptual, and adversarial losses to achieve better stratigraphic consistency and horizon correlation on seismic data.
FADPNet decomposes facial features into low- and high-frequency components processed by dedicated Mamba and CNN modules to balance quality and efficiency in face super-resolution.
citing papers explorer
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TVRN: Invertible Neural Networks for Compression-Aware Temporal Video Rescaling
TVRN combines invertible wavelet-based networks with a surrogate gradient approximator and compression-aware asymmetric design to improve frame-rate rescaling quality under real codecs.
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Learning Stratigraphically Consistent Relative Geologic Time from 3D Seismic Data via Sinusoidal Mapping
RGT-Est transforms relative geologic time estimation into a sinusoidal space and applies pointwise, perceptual, and adversarial losses to achieve better stratigraphic consistency and horizon correlation on seismic data.
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FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution
FADPNet decomposes facial features into low- and high-frequency components processed by dedicated Mamba and CNN modules to balance quality and efficiency in face super-resolution.