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Found 5 papers, 1 papers with code
Residual Connections Harm Self-Supervised Abstract Feature Learning
We demonstrate that adding a weighting factor to decay the strength of identity shortcuts within residual networks substantially improves semantic feature learning in the state-of-the-art self-supervised masked autoencoding (MAE) paradigm.
Efficient Denoising using Score Embedding in Score-based Diffusion Models
Our method allows us to decrease the number of epochs needed to train the diffusion model.
Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries
This research provides an innovative technological solution to address the long delays in metastatic breast cancer diagnosis and the consequent disparity in patient survival outcome in developing countries.
TextDeformer: Geometry Manipulation using Text Guidance
Our key observation is that Jacobians are a representation that favors smoother, large deformations, leading to a global relation between vertices and pixels, and avoiding localized noisy gradients.
TetGAN: A Convolutional Neural Network for Tetrahedral Mesh Generation
We present TetGAN, a convolutional neural network designed to generate tetrahedral meshes.
