Python Ctc-loss Resources

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

RNNT loss in Pytorch - Numba JIT compiled (warprnnt_numba) Warp RNN Transducer Loss for ASR in Pytorch, ported from HawkAaron/warp-transducer and a re

Italian CLIP CLIP (Radford et al., 2021) is a multimodal model that can learn to represent images and text jointly in the same space. In this project,

Implementation of some unbalanced loss for NLP task like focal_loss, dice_loss, DSC Loss, GHM Loss et.al Summary Here is a loss implementation reposit

SCALoss PyTorch implementation of the paper "SCALoss: Side and Corner Aligned Loss for Bounding Box Regression" (AAAI 2022). Introduction IoU-based lo

MDCA Calibration This is the official PyTorch implementation for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved

Relational Surrogate Loss Learning (ReLoss) Official implementation for paper "R

"# SUPERVISED-CONTRASTIVE-LEARNING-FOR-PRE-TRAINED-LANGUAGE-MODEL-FINE-TUNING" i

Peer Loss functions This repository is the (Multi-Class & Deep Learning) Pytorch implementation of "Peer Loss Functions: Learning from Noisy Labels wi

This is the official repository of the paper: ElasticFace: Elastic Margin Loss for Deep Face Recognition Paper on arxiv: arxiv Model Log file Pretrain

ssnt-loss ℹ️ This is a WIP project. the implementation is still being tested. A pure PyTorch implementation of the loss described in "Online Segment t

R-YOLOv4 This is a PyTorch-based R-YOLOv4 implementation which combines YOLOv4 model and loss function from R3Det for arbitrary oriented object detect

SSWS-loss_function_based_on_MS-TCN Supervised Sliding Window Smoothing Loss Function Based on MS-TCN for Video Segmentation Supervised Sliding Window

Contrastive Loss Gradient Attack (CLGA) Official implementation of Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation, WWW22 Bu

Insurance-Fraud-Claims Detailed analysis on fraud claims in insurance companies, gives you information as to why huge loss take place in insurance com

Unsupervised Feature Loss (UFLoss) for High Fidelity Deep learning (DL)-based reconstruction Official github repository for the paper High Fidelity De

Image to Embedding projection in vector space. Implementation in keras and tensorflow of batch all triplet loss for one-shot/few-shot learning.

HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images Histological Image Segmentation This

Keras-Image-Embeddings-using-Contrastive-Loss Image to Embedding projection in vector space. Implementation in keras and tensorflow for custom data. B

CAMoE + Dual SoftMax Loss (DSL): Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss This is official implement of "

This is the implementation of "Training deep neural networks via direct loss minimization" published at ICML 2016 in PyTorch. The implementation targe

📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

Denoising images with Fourier Ring Correlation loss The python code accompanies the working manuscript Image quality measurements and denoising using

Orthogonal Projection Loss (ICCV'21) Kanchana Ranasinghe, Muzammal Naseer, Munawar Hayat, Salman Khan, & Fahad Shahbaz Khan Paper Link | Project Page

The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks Maxim Berman, Amal Ranne

Beta Divergence Loss - PyTorch Implementation This repository contains code for a PyTorch implementation of the beta divergence loss. Dependencies Thi

SAFL: A Self-Attention Scene Text Recognizer with Focal Loss This repository implements the SAFL in pytorch. Installation conda env create -f environm

(简体中文|English) Quick Start | Documents | Models List PaddleSpeech is an open-source toolkit on PaddlePaddle platform for a variety of critical tasks i

Taxonomizing local versus global structure in neural network loss landscapes Int

BRNet code for "Automated assessment of BI-RADS categories for ultrasound images using multi-scale neural networks with an order-constrained loss func

Introduction This project implements the optimization techniques proposed in Improving RNN Transducer Modeling for End-to-End Speech Recognition to re

Official repository of the paper Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision

Simple and Robust Loss Design for Multi-Label Learning with Missing Labels Official PyTorch Implementation of the paper Simple and Robust Loss Design

chainladder (python) chainladder - Property and Casualty Loss Reserving in Python This package gets inspiration from the popular R ChainLadder package

Learning to Teach with Dynamic Loss Functions This repo contains the simple demo for the NeurIPS-18 paper: Learning to Teach with Dynamic Loss Functio

EMLight: Lighting Estimation via Spherical Distribution Approximation (AAAI 2021) Update 12/2021: We release our Virtual Object Relighting (VOR) Datas

SFace Code of TIP2021 Paper 《SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition》. We provide both MxNet, PyTorch and Jittor versi

Hierarchical Cross-modal Talking Face Generation with Dynamic Pixel-wise Loss （ATVGnet） By Lele Chen , Ross K Maddox, Zhiyao Duan, Chenliang Xu. Unive

entmax This package provides a pytorch implementation of entmax and entmax losses: a sparse family of probability mappings and corresponding loss func

Class-balanced-loss-pytorch Pytorch implementation of the paper Class-Balanced Loss Based on Effective Number of Samples presented at CVPR'19. Yin Cui

RangeLoss Pytorch This is a Pytorch reproduction of Range Loss, which is proposed in paper 《Range Loss for Deep Face Recognition with Long-Tailed Trai

Getting Profit and Loss Make Easy From Binance I have been in Binance Automated Trading for some time and have generated a lot of transaction records,

(Preprint) Official PyTorch implementation of "How Do Vision Transformers Work?"

Parameterized AP Loss By Chenxin Tao, Zizhang Li, Xizhou Zhu, Gao Huang, Yong Liu, Jifeng Dai This is the official implementation of the Neurips 2021

MLVTK A loss surface visualization tool Simple feed-forward network trained on chess data, using elu activation and Adam optimizer Simple feed-forward

Parameterized AP Loss By Chenxin Tao, Zizhang Li, Xizhou Zhu, Gao Huang, Yong Liu, Jifeng Dai This is the official implementation of the Neurips 2021

Class-Balanced Loss Based on Effective Number of Samples Tensorflow code for the paper: Class-Balanced Loss Based on Effective Number of Samples Yin C

Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, Tengyu Ma This is the offi

Torch-template-for-deep-learning Pytorch implementations of some **classical backbone CNNs, data enhancement, torch loss, attention, visualization and

pytorch_clip_guided_loss: Pytorch implementation of the CLIP guided loss for Text-To-Image, Image-To-Image, or Image-To-Text generation. A simple libr

This repository contains the similarity metrics designed and evaluated in the paper, and instructions and code to re-run the experiments. Implementation in the deep-learning framework PyTorch

UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss This repository contains the TensorFlow implementation of the paper UnF

Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples This repository is the official implementation of "Tow

Convert ResNets weights from GluonCV to Tensorflow Abstract GluonCV released some new resnet pre-training weights and designed some new resnets (such

multi-task_losses_optimizer Implement the Pareto Optimizer and pcgrad to make a self-adaptive loss for multi-task 已经实验过了，不会有cuda out of memory情况 ##Par

BANA This is the implementation of the paper "Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation". For more inf

Proxy Anchor Loss for Deep Metric Learning Unofficial pytorch, tensorflow and mxnet implementations of Proxy Anchor Loss for Deep Metric Learning. Not

MeTAL - Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning (ICCV2021 Oral) Sungyong Baik, Janghoon Choi, Heewon Kim, Dohee Cho, Jaes

Chamfer Distance for pyTorch This is an implementation of the Chamfer Distance as a module for pyTorch. It is written as a custom C++/CUDA extension.

Complete-IoU Loss and Cluster-NMS for Improving Object Detection and Instance Segmentation. Our paper is accepted by IEEE Transactions on Cybernetics

livelossplot Don't train deep learning models blindfolded! Be impatient and look at each epoch of your training! (RECENT CHANGES, EXAMPLES IN COLAB, A

Adaptive Class Suppression Loss for Long-Tail Object Detection This repo is the official implementation for CVPR 2021 paper: Adaptive Class Suppressio

Confident Semantic Ranking Loss for Part Parsing

orqviz A Python package for easily visualizing the loss landscape of Variational Quantum Algorithms by Zapata Computing Inc. orqviz provides a collect

Contrastive Feature Loss for Image Prediction We provide a PyTorch implementation of our contrastive feature loss presented in: Contrastive Feature Lo

chinese_asr_demo 一个端到端的中文语音识别模型训练、测试框架 具备数据预处理、模型训练、解码、计算wer等等功能 训练数据 训练数据采用thchs_30，

Image Quality Assessment using Contrastive Learning Pavan C. Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli and Alan C. Bovik This is the offi

Deeplab-resnet-101 Pytorch with Lovász hinge loss Train deeplab-resnet-101 with binary Jaccard loss surrogate, the Lovász hinge, as described in http:

Temporal Output Discrepancy for Active Learning PyTorch implementation of Semi-Supervised Active Learning with Temporal Output Discrepancy, ICCV 2021.

pingtest Description A personal project for testing internet stability, intended for use in Linux and Windows.

Installation: pip install -e . Installing DL2: First clone DL2 in a separate directory and install it using the following commands: git clone https:/

🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥

Chinese NER(Named Entity Recognition) using BERT(Softmax, CRF, Span)

Covid-19 Test AI (Deep Learning - NNs) Software I developed a segmentation algorithm to understand whether Covid-19 Test Photos are positive or negati

The official code for the NeurIPS 2021 paper Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

Decoupled-Contrastive-Learning This repository is an implementation for the loss function proposed in Decoupled Contrastive Loss paper. Requirements P

PyTorch implementation of MSBG hearing loss model and MBSTOI intelligibility metric This repository contains the implementation of MSBG hearing loss m

Multi-label Classification with Partial Annotations using Class-aware Selective Loss Paper | Pretrained models Official PyTorch Implementation Emanuel

An Empirical Study on GANs with Margin Cosine Loss and Relativistic Discriminator This is a Pytorch implementation for the paper "An Empirical Study o

Multi-label Classification with Partial Annotations using Class-aware Selective Loss Paper | Pretrained models Official PyTorch Implementation Emanuel

Spatial Location Constraint Prototype Loss for Open Set Recognition Official PyTorch implementation of "Spatial Location Constraint Prototype Loss for

Equalization Loss for Long-Tailed Object Recognition Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, Junjie Yan ⚠️ We re

Distribution-Balanced Loss [Paper] The implementation of our paper Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets (

Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks

CTC Decoding Algorithms Update 2021: installable Python package Python implementation of some common Connectionist Temporal Classification (CTC) decod

Multi-Objective Loss Balancing for Physics-Informed Deep Learning Code for ReLoBRaLo. Abstract Physics Informed Neural Networks (PINN) are algorithms

LESSR A PyTorch implementation of LESSR (Lossless Edge-order preserving aggregation and Shortcut graph attention for Session-based Recommendation) fro

ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss (HDCWNet) (

Focal Frequency Loss - Official PyTorch Implementation This repository provides the official PyTorch implementation for the following paper: Focal Fre

PurNet Project for the TIP 2021 Paper "Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss" Abstract Image-based salie

This is a repository for the paper of "Well-classified Examples are Underestimated in Classification with Deep Neural Networks" The implementation and

CAMoE + Dual SoftMax Loss (DSL): Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss This is official implement of "

Disturbing Target Values for Neural Network regularization: attacking the loss layer to prevent overfitting 1. Classification Task PyTorch implementat

Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes Setup virtualenv -p python3 venv source venv/bin/activate pip instal

Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.

Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

Deep-Unsupervised-Domain-Adaptation Pytorch implementation of four neural network based domain adaptation techniques: DeepCORAL, DDC, CDAN and CDAN+E.

Face Recognition in PyTorch By Alexey Gruzdev and Vladislav Sovrasov Introduction A repository for different experimental Face Recognition models such

Visualizing the Loss Landscape of Neural Nets This repository contains the PyTorch code for the paper Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer

Rule-Based-Classification-in-a-Banking-Case. A CRM department in a local bank works on classify their lost customers with their past datas. So they wa