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Loss Functions

1162 directly classified papers

Papers per year

Papers

Light Field Spatial Super-Resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization CVPR 2020
Munchausen Reinforcement Learning NIPS 2020
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay NIPS 2020
Calibrating Deep Neural Networks using Focal Loss NIPS 2020
Comparing Objective Functions for Segmentation and Detection of Microaneurysms in Retinal Images MIDL 2020
Towards a Better Global Loss Landscape of GANs NIPS 2020
Regularizing CNN Transfer Learning With Randomised Regression CVPR 2020
Prime Sample Attention in Object Detection CVPR 2020
Learning a Unified Sample Weighting Network for Object Detection CVPR 2020
Can Cross Entropy Loss Be Robust to Label Noise? IJCAI 2020
Knowledge-Based Regularization in Generative Modeling IJCAI 2020
Self-Distillation as Instance-Specific Label Smoothing NIPS 2020
Neuron-level Structured Pruning using Polarization Regularizer NIPS 2020
Do We Need Zero Training Loss After Achieving Zero Training Error? ICML 2020
NAAGN: Noise-Aware Attention-Gated Network for Speech Enhancement INTERSPEECH 2020
Improved Natural Language Generation via Loss Truncation ACL 2020
Dice Loss for Data-imbalanced NLP Tasks ACL 2020
Equalization Loss for Long-Tailed Object Recognition CVPR 2020
Regularizing Class-Wise Predictions via Self-Knowledge Distillation CVPR 2020
Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization CVPR 2020
Rotation Consistent Margin Loss for Efficient Low-Bit Face Recognition CVPR 2020
Circle Loss: A Unified Perspective of Pair Similarity Optimization CVPR 2020
Transferring and Regularizing Prediction for Semantic Segmentation CVPR 2020
Learning to Rank for Plausible Plausibility ACL 2019
Countdown Regression: Sharp and Calibrated Survival Predictions UAI 2019