Search Results for author:
Found 7 papers, 3 papers with code
FED: Fast and Efficient Dataset Deduplication Framework with GPU Acceleration
Dataset deduplication plays a crucial role in enhancing data quality, ultimately improving training performance and efficiency of LLMs.
RADIO: Reference-Agnostic Dubbing Video Synthesis
One of the most challenging problems in audio-driven talking head generation is achieving high-fidelity detail while ensuring precise synchronization.
WINE: Wavelet-Guided GAN Inversion and Editing for High-Fidelity Refinement
Recent advanced GAN inversion models aim to convey high-fidelity information from original images to generators through methods using generator tuning or high-dimensional feature learning.
Zero-shot Blind Image Denoising via Implicit Neural Representations
Recent denoising algorithms based on the "blind-spot" strategy show impressive blind image denoising performances, without utilizing any external dataset.
Scaling Neural Tangent Kernels via Sketching and Random Features
To accelerate learning with NTK, we design a near input-sparsity time approximation algorithm for NTK, by sketching the polynomial expansions of arc-cosine kernels: our sketch for the convolutional counterpart of NTK (CNTK) can transform any image using a linear runtime in the number of pixels.
Random Features for the Neural Tangent Kernel
We combine random features of the arc-cosine kernels with a sketching-based algorithm which can run in linear with respect to both the number of data points and input dimension.
2018 Robotic Scene Segmentation Challenge
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models.
