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← Optimization & Theory
Deep Learning
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Optimization & Theory
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Loss Functions
122 directly classified papers
Papers per year
2012: 1
2016: 1
2017: 3
2018: 7
2019: 15
2020: 20
2021: 18
2022: 18
2023: 11
2024: 16
2025: 12
Papers
Revisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection
CVPR 2022
Balanced MSE for Imbalanced Visual Regression
CVPR 2022
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration
CVPR 2022
Geometry-Aware Guided Loss for Deep Crack Recognition
CVPR 2022
Active Boundary Loss for Semantic Segmentation
AAAI 2022
Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones Is Enough
AAAI 2022
Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked Positives
AAAI 2022
Learning from Noisy Labels with Complementary Loss Functions
AAAI 2021
Log-Likelihood-Ratio Cost Function as Objective Loss for Speaker Verification Systems
INTERSPEECH 2021
Learning Modulated Loss for Rotated Object Detection
AAAI 2021
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints
AAAI 2021
From Label Smoothing to Label Relaxation
AAAI 2021
A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and its Applications to Regularization
AAAI 2021
BERTTune: Fine-Tuning Neural Machine Translation with BERTScore
ACL 2021
Style-Aware Normalized Loss for Improving Arbitrary Style Transfer
CVPR 2021
Metadata Normalization
CVPR 2021
Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection
CVPR 2021
Depth Completion With Twin Surface Extrapolation at Occlusion Boundaries
CVPR 2021
Generic Perceptual Loss for Modeling Structured Output Dependencies
CVPR 2021
Understanding and Simplifying Perceptual Distances
CVPR 2021
An Alternative Probabilistic Interpretation of the Huber Loss
CVPR 2021
A Sliced Wasserstein Loss for Neural Texture Synthesis
CVPR 2021
Bounds all around: training energy-based models with bidirectional bounds
NIPS 2021
$\alpha$-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression
NIPS 2021
Searching Parameterized AP Loss for Object Detection
NIPS 2021
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