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lpips-pytorch Description Developing perceptual distance metrics is a major topic in recent image processing problems. LPIPS[1] is a state-of-the-art
DSFNet: Dynamic and Static Fusion Network for Moving Object Detection in Satellite Videos Algorithm Introduction DSFNet: Dynamic and Static Fusion Net
CLIPImageClassifier CLIPImageClassifier wraps clip image model from transformers. CLIPImageClassifier is initialized with the argument classes, these
El-Gamal-on-EC El-Gamal on Elliptic Curve (Python) References: https://docsdrive.com/pdfs/ansinet/itj/2005/299-306.pdf https://arxiv.org/ftp/arxiv/pap
PyDeepFakeDet An integrated and scalable library for Deepfake detection research. Introduction PyDeepFakeDet is an integrated and scalable Deepfake de
Simple Tar Dataset An unopinionated replacement for PyTorch's Dataset and ImageFolder classes, for datasets stored as uncompressed Tar archives. Just
Object-Shadow-Generation-Dataset-DESOBA Object Shadow Generation is to deal with the shadow inconsistency between the foreground object and the backgr
Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking Part-Aware Measurement for Robust Multi-View Multi-Human 3D P
Machine Unlearning Papers 2021 Brophy and Lowd. Machine Unlearning for Random Forests. In ICML 2021. Bourtoule et al. Machine Unlearning. In IEEE Symp
Versatile Generative Language Model This is the implementation of the paper: Exploring Versatile Generative Language Model Via Parameter-Efficient Tra
Deep Residual Networks with 1K Layers By Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Microsoft Research Asia (MSRA). Table of Contents Introduc
OnsagerNet Learning hidden low dimensional dyanmics using a Generalized Onsager Principle and neural networks This is the original pyTorch implemenati
Unsupervised Learning of Video Representations using LSTMs Code for paper Unsupervised Learning of Video Representations using LSTMs by Nitish Srivast
PyTorch Neural Turing Machine (NTM) PyTorch implementation of Neural Turing Machines (NTM). An NTM is a memory augumented neural network (attached to
Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR2018) By Zilong Huang, Xinggang Wang, Jiasi Wang, Wenyu Liu and J
marvl-code [WIP] This is the implementation of the approaches described in the paper: Fangyu Liu*, Emanuele Bugliarello*, Edoardo M. Ponti, Siva Reddy
U Net Lowered with Keras Complete U-net Implementation with keras Original Paper Link : https://arxiv.org/abs/1505.04597 Special Implementations : The
ELECTRA Introduction ELECTRA is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using
GraphMLTutorialNLDL22 Tutorial NLDL22: Introduction to Graph Machine Learning, with Jupyter notebooks This tutorial takes place during the conference
Flexible-CLmser: Regularized Feedback Connections for Biomedical Image Segmentation The skip connections in U-Net pass features from the levels of enc
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