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Loss Functions

1162 directly classified papers

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Papers

Learning to Predict Trustworthiness with Steep Slope Loss NIPS 2021
Why Do Better Loss Functions Lead to Less Transferable Features? NIPS 2021
Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation IJCNLP 2021
On UMAP's True Loss Function NIPS 2021
InverseForm: A Loss Function for Structured Boundary-Aware Segmentation CVPR 2021
Speech Enhancement with Topology-Enhanced Generative Adversarial Networks (GANs) INTERSPEECH 2021
Bias Loss for Mobile Neural Networks ICCV 2021
Using Question Answering Rewards to Improve Abstractive Summarization EMNLP 2021
Regularization Strategy for Point Cloud via Rigidly Mixed Sample CVPR 2021
Mixed Cross Entropy Loss for Neural Machine Translation ICML 2021
Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection CVPR 2021
Transferable Semantic Augmentation for Domain Adaptation CVPR 2021
Equalization Loss v2: A New Gradient Balance Approach for Long-Tailed Object Detection CVPR 2021
Conditional Temporal Neural Processes with Covariance Loss ICML 2021
Improving Gradient Regularization using Complex-Valued Neural Networks ICML 2021
Rank & Sort Loss for Object Detection and Instance Segmentation ICCV 2021
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks CVPR 2021
Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning EMNLP 2021
A Second-Order Approach to Learning With Instance-Dependent Label Noise CVPR 2021
Seesaw Loss for Long-Tailed Instance Segmentation CVPR 2021
Training Over-parameterized Models with Non-decomposable Objectives NIPS 2021
Lower-Bounded Proper Losses for Weakly Supervised Classification ICML 2021
Orthogonal Projection Loss ICCV 2021
Learning Generalized Intersection Over Union for Dense Pixelwise Prediction ICML 2021
Tilting the playing field: Dynamical loss functions for machine learning ICML 2021