Popular GitHub Repositories for Losses

A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.

:art: Semantic segmentation models, datasets and losses implemented in PyTorch.

Library for Jacobian descent with PyTorch. It enables optimization of neural networks with multiple losses (e.g. multi-task learning).

Improving MMD-GAN training with repulsive loss function

ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks, published in ECCV 2018) implemented in Tensorflow 2.0+. This is an unofficial implementation. With Colab.

Torchélie is a set of utility functions, layers, losses, models, trainers and other things for PyTorch.

Implementation of "SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses"

[CVPR 2024] Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of Artifacts

The official implementation of 'Align and Attend: Multimodal Summarization with Dual Contrastive Losses' (CVPR 2023)

Generative Adversarial Network for single image super-resolution in high content screening microscopy images

Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search', accepted by ICLR 2021.

Low-dose CT via Transfer Learning from a 2D Trained Network, In IEEE TMI 2018

A collection of Models, Datasets, DataModules, Callbacks, Metrics, Losses and Loggers to better integrate pytorch-lightning with transformers.

Source code for "Towards a Deeper Understanding of Adversarial Losses under a Discriminative Adversarial Network Setting"

C++ implementation of neural networks library with Keras-like API. Contains majority of commonly used layers, losses and optimizers. Supports sequential and multi-input-output (flow) models. Supports single CPU, Multi-CPU and GPU tensor operations (using cuDNN and cuBLAS).

Easy Custom Losses for Tree Boosters using Pytorch

A Tensorflow Implementation of Brain Tumor Segmentation using Topological Loss

Implementation of VAE and Style-GAN Architecture Achieving State of the Art Reconstruction

implement Deep Feature Consisten Variational Autoencoder in Tensorflow

A project to train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances. Calibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold.