Code for "Is Deep Image Prior in Need of a Good Education?"
Project page: https://jleuschn.github.io/docs.educated_deep_image_prior/.
Supplementary Material: https://zenodo.org/record/5574586 (includes experimental resuults)
Project page: https://jleuschn.github.io/docs.educated_deep_image_prior/.
Supplementary Material: https://zenodo.org/record/5574586 (includes experimental resuults)
FFD Source Code Provided is code that demonstrates the training and evaluation of the work presented in the paper: "On the Detection of Digital Face M
A tensorflow convolutional neural network model to detect facial expressions.
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A large-scale database for graph representation learning
EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein
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CosineAnnealingWithWarmup Formulation The learning rate is annealed using a cosine schedule over the course of learning of n_total total steps with an
Learning-based Camera Calibration A Learning-based Camera Calibration Toolbox Paper The pdf file can be found here. @misc{zhang2022learningbased,
A collection of Deep Learning images and visualizations. The project has been developed by the AI Summer team and currently contains almost 100 images.
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Struct-MDC (click the above buttons for redirection!) Official page of "Struct-MDC: Mesh-Refined Unsupervised Depth Completion Leveraging Structural R
FIERY This is the PyTorch implementation for inference and training of the future prediction bird's-eye view network as described in: FIERY: Future In
MLP-Numpy A simple modular implementation of Multi Layer Perceptron in pure Numpy. I used the Iris dataset from scikit-learn library for the experimen
Welcome to spline - the pipeline tool Important note: Since change in my job I didn't had the chance to continue on this project. My main new project
IEGAN — Official PyTorch Implementation Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation Independent Encoder for Deep
MoViNet-pytorch Pytorch unofficial implementation of MoViNets: Mobile Video Networks for Efficient Video Recognition. Authors: Dan Kondratyuk, Liangzh