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We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.
An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering Papers with code | Paper Nikola Zubić Pietro Lio University
Code for the Lovász-Softmax loss (CVPR 2018)
The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks Maxim Berman, Amal Ranne
This repository contains all source code, pre-trained models related to the paper "An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator"
An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator This is a Pytorch implementation for the paper "An Empirical Study o
I will implement Fastai in each projects present in this repository.
DEEP LEARNING FOR CODERS WITH FASTAI AND PYTORCH The repository contains a list of the projects which I have worked on while reading the book Deep Lea
Official repository for "Orthogonal Projection Loss" (ICCV'21)
Orthogonal Projection Loss (ICCV'21) Kanchana Ranasinghe, Muzammal Naseer, Munawar Hayat, Salman Khan, & Fahad Shahbaz Khan Paper Link | Project Page
Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"
Class-balanced-loss-pytorch Pytorch implementation of the paper Class-Balanced Loss Based on Effective Number of Samples presented at CVPR'19. Yin Cui
GeDML is an easy-to-use generalized deep metric learning library
GeDML is an easy-to-use generalized deep metric learning library