EnsLoss: Stochastic Calibrated Loss Ensembles for Preventing Overfitting in Classification
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Updated
Sep 11, 2024 - Python
EnsLoss: Stochastic Calibrated Loss Ensembles for Preventing Overfitting in Classification
optimizer & lr scheduler & loss function collections in PyTorch
[PyTorch] Implementation of MS-SSIM + L1 loss proposed in "Loss Functions for Neural Networks for Image Processing" for Greyscale Images
Repository for the paper "Advancing Time Series Forecasting: Variance-Aware Loss Functions in Transformers"
logWMSE, an audio quality metric & loss function with support for digital silence target. Useful for training and evaluating audio source separation systems.
Collection of audio-focused loss functions in PyTorch
TensorFlow implementations of losses for sequence to sequence machine learning models
Creating a Neural Network from scratch.
CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy
Efficient implementation of object condensation losses for use in various projects
Rooted logistic loss to accelerate neural network traning and LLM quantization
This repository is the implementation of the paper, "Score-balanced Loss for Multi-aspect Pronunciation Assessment" (Interspeech 2023).
Soft-DTW loss function for Keras/TensorFlow
Implementation of our ACL 2020 paper: Structured Tuning for Semantic Role Labeling
The code for L3AM loss with Pytorch
[CVPR 2021] This repository is the official implementation of paper: "PML: Progressive Margin Loss for Long-tailed Age Classification"
C3Net: Demoireing Network Attentive in Channel, Color and Concatenation (CVPRW 2020)
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.
RULSTM Dissertation Research, Architecture used for RULSTM experimentation, mainly with loss functions, sequence completion pretraining and anticipation times.
"This program trains a model using 'SVM' or 'Softmax' and predicts the input data. Loss history and predicted tags are displayed as results."
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