See also Deep Learning, Deep Nets.

See also Edge Detectors Based on Learning, Neural Nets, etc..

*Singh, A.[Abhishek]*,
*Pokharel, R.[Rosha]*,
*Principe, J.C.[Jose C.]*,

**The C-loss function for pattern classification**,

*PR(47)*, No. 1, 2014, pp. 441-453.

Elsevier DOI
**1310**

Correntropy.
For neural network classification.
BibRef

*Liao, Z.B.[Zhi-Bin]*,
*Carneiro, G.[Gustavo]*,

**A deep convolutional neural network module that promotes competition
of multiple-size filters**,

*PR(71)*, No. 1, 2017, pp. 94-105.

Elsevier DOI
**1707**

BibRef

Earlier:

**The use of deep learning features in a hierarchical classifier
learned with the minimization of a non-greedy loss function that
delays gratification**,

*ICIP15*(4540-4544)

IEEE DOI
**1512**

Deep, learning
BibRef

*Bazi, Y.[Yakoub]*,
*Rahhal, M.M.A.[Mohamad M. Al]*,
*Alhichri, H.[Haikel]*,
*Alajlan, N.[Naif]*,

**Simple Yet Effective Fine-Tuning of Deep CNNs Using an Auxiliary
Classification Loss for Remote Sensing Scene Classification**,

*RS(11)*, No. 24, 2019, pp. xx-yy.

DOI Link
**1912**

BibRef

*Yuan, Q.Y.[Qun-Yong]*,
*Xiao, N.F.[Nan-Feng]*,

**Experimental exploration on loss surface of deep neural network**,

*IJIST(30)*, No. 4, 2020, pp. 860-873.

DOI Link
**2011**

The loss function of the deep neural network is high dimensional,
nonconvex and complex.
loss surface of deep neural network,
Hessian matrix deep neural network, ensemble learning
BibRef

*Li, C.J.[Cui-Jin]*,
*Qu, Z.[Zhong]*,
*Wang, S.Y.[Sheng-Ye]*,
*Liu, L.[Ling]*,

**A method of cross-layer fusion multi-object detection and recognition
based on improved faster R-CNN model in complex traffic environment**,

*PRL(145)*, 2021, pp. 127-134.

Elsevier DOI
**2104**

Multi-object detection, Multi-object recognition, Faster R-CNN,
Weighted balanced multi-class cross entropy loss function
BibRef

*Seo, H.*,
*Bassenne, M.*,
*Xing, L.*,

**Closing the Gap Between Deep Neural Network Modeling and Biomedical
Decision-Making Metrics in Segmentation via Adaptive Loss Functions**,

*MedImg(40)*, No. 2, February 2021, pp. 585-593.

IEEE DOI
**2102**

Training, Neural networks, Measurement, Adaptation models,
Decision making, Deep learning, Harmonic analysis, Deep learning,
Segmentation
BibRef

*Martínez-Cortés, T.[Tomás]*,
*González-Díaz, I.[Iván]*,
*Díaz-de-María, F.[Fernando]*,

**Training deep retrieval models with noisy datasets:
Bag exponential loss**,

*PR(112)*, 2021, pp. 107811.

Elsevier DOI
**2102**

Image retrieval, Noise, Multiple instance learning, Loss functions
BibRef

*Zadeh, S.G.[Shekoufeh Gorgi]*,
*Schmid, M.[Matthias]*,

**Bias in Cross-Entropy-Based Training of Deep Survival Networks**,

*PAMI(43)*, No. 9, September 2021, pp. 3126-3137.

IEEE DOI
**2108**

Training, Hazards, Mathematical model, Entropy, Power measurement,
Indexes, Neural networks, Cross-entropy loss,
negative log-likelihood loss
BibRef

*Kang, J.[Jian]*,
*Fernandez-Beltran, R.[Ruben]*,
*Duan, P.[Puhong]*,
*Kang, X.D.[Xu-Dong]*,
*Plaza, A.J.[Antonio J.]*,

**Robust Normalized Softmax Loss for Deep Metric Learning-Based
Characterization of Remote Sensing Images With Label Noise**,

*GeoRS(59)*, No. 10, October 2021, pp. 8798-8811.

IEEE DOI
**2109**

Measurement, Semantics, Annotations, Feature extraction, Prototypes,
Noise measurement, Visualization, Deep metric learning, remote sensing (RS)
BibRef

*Tian, Y.[Ye]*,
*Dong, Y.X.[Yu-Xin]*,
*Yin, G.S.[Gui-Sheng]*,

**Early Labeled and Small Loss Selection Semi-Supervised Learning
Method for Remote Sensing Image Scene Classification**,

*RS(13)*, No. 20, 2021, pp. xx-yy.

DOI Link
**2110**

BibRef

IEEE DOI

WWW Link. Training, Deep learning, Estimation, Rendering (computer graphics), Pattern recognition, Task analysis BibRef

*Liu, Y.F.[Yi-Fan]*,
*Chen, H.[Hao]*,
*Chen, Y.[Yu]*,
*Yin, W.[Wei]*,
*Shen, C.H.[Chun-Hua]*,

**Generic Perceptual Loss for Modeling Structured Output Dependencies**,

*CVPR21*(5420-5428)

IEEE DOI
**2111**

Training, Image segmentation, Image synthesis,
Semantics, Superresolution, Estimation
BibRef

*Yang, M.X.[Mou-Xing]*,
*Li, Y.F.[Yun-Fan]*,
*Huang, Z.Y.[Zhen-Yu]*,
*Liu, Z.[Zitao]*,
*Hu, P.[Peng]*,
*Peng, X.[Xi]*,

**Partially View-aligned Representation Learning with Noise-robust
Contrastive Loss**,

*CVPR21*(1134-1143)

IEEE DOI
**2111**

Robustness, Noise robustness, Spatiotemporal phenomena,
Image restoration, Noise measurement, Object tracking, Object recognition
BibRef

*Wang, F.[Feng]*,
*Liu, H.P.[Hua-Ping]*,

**Understanding the Behaviour of Contrastive Loss**,

*CVPR21*(2495-2504)

IEEE DOI
**2111**

Temperature distribution,
Computational modeling, Semantics, Temperature control,
Task analysis
BibRef

*Draxler, F.[Felix]*,
*Schwarz, J.[Jonathan]*,
*Schnörr, C.[Christoph]*,
*Köthe, U.[Ullrich]*,

**Characterizing the Role of a Single Coupling Layer in Affine
Normalizing Flows**,

*GCPR20*(1-14).

Springer DOI
**2110**

*Award, GCPR, HM*.
BibRef

*Schwarz, J.[Jonathan]*,
*Draxler, F.[Felix]*,
*Köthe, U.[Ullrich]*,
*Schnörr, C.[Christoph]*,

**Riemannian SOS-Polynomial Normalizing Flows**,

*GCPR20*(218-231).

Springer DOI
**2110**

BibRef

*Kobayashi, T.[Takumi]*,

**Group Softmax Loss with Discriminative Feature Grouping**,

*WACV21*(2614-2623)

IEEE DOI
**2106**

Training, Supervised learning,
Neural networks, Training data, Loss measurement
BibRef

*Chan, C.H.[Chi-Ho]*,
*Kittler, J.V.[Josef V.]*,

**Angular Sparsemax for Face Recognition**,

*ICPR21*(10473-10479)

IEEE DOI
**2105**

Loss function in deep networks training.
Additives, Databases, Face recognition,
Optimized production technology, Probability distribution,
Convolutional neural networks
BibRef

*Bechtle, S.[Sarah]*,
*Molchanov, A.[Artem]*,
*Chebotar, Y.[Yevgen]*,
*Grefenstette, E.[Edward]*,
*Righetti, L.[Ludovic]*,
*Sukhatme, G.[Gaurav]*,
*Meier, F.[Franziska]*,

**Meta Learning via Learned Loss**,

*ICPR21*(4161-4168)

IEEE DOI
**2105**

Choosing the loss function in learning.
Training, Shape, Transfer learning, Pipelines,
Reinforcement learning, Tools, meta learning, deep learning
BibRef

*Liu, L.L.[Lan-Lan]*,
*Wang, M.Z.[Ming-Zhe]*,
*Deng, J.[Jia]*,

**A Unified Framework of Surrogate Loss by Refactoring and Interpolation**,

*ECCV20*(III:278-293).

Springer DOI
**2012**

BibRef

*Zhu, Z.*,
*Wang, H.*,

**Deep Adversarial Active Learning With Model Uncertainty For Image
Classification**,

*ICIP20*(1711-1715)

IEEE DOI
**2011**

Task analysis, Uncertainty, Training, Predictive models, Data models,
Labeling, Loss measurement, Active learning, Adversarial learning,
Image classification
BibRef

*Wang, Q.*,
*Zhang, L.*,
*Wu, B.*,
*Ren, D.*,
*Li, P.*,
*Zuo, W.*,
*Hu, Q.*,

**What Deep CNNs Benefit From Global Covariance Pooling:
An Optimization Perspective**,

*CVPR20*(10768-10777)

IEEE DOI
**2008**

Optimization, Training, Task analysis, Convergence, Robustness,
Loss measurement, Stability analysis
BibRef

*Wan, W.T.[Wei-Tao]*,
*Zhong, Y.Y.[Yuan-Yi]*,
*Li, T.P.[Tian-Peng]*,
*Chen, J.S.[Jian-Sheng]*,

**Rethinking Feature Distribution for Loss Functions in Image
Classification**,

*CVPR18*(9117-9126)

IEEE DOI
**1812**

Training, Feature extraction, Probability distribution,
Neural networks, Task analysis, Euclidean distance, Loss measurement
BibRef

*Qi, C.*,
*Su, F.*,

**Contrastive-center loss for deep neural networks**,

*ICIP17*(2851-2855)

IEEE DOI
**1803**

Face recognition, Feature extraction, Neural networks,
Task analysis, Testing, Training, Visualization, Auxiliary loss,
Image classification and face recognition
BibRef

*Sajjadi, M.*,
*Javanmardi, M.*,
*Tasdizen, T.*,

**Mutual exclusivity loss for semi-supervised deep learning**,

*ICIP16*(1908-1912)

IEEE DOI
**1610**

Entropy
BibRef

*Yoo, D.G.[Dong-Geun]*,
*Kweon, I.S.[In So]*,

**Learning Loss for Active Learning**,

*CVPR19*(93-102).

IEEE DOI
**2002**

BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in

Siamese Networks .

Last update:Nov 30, 2021 at 22:19:38