hyperparameter optimization is deceiving us, and how to stop it Search Results


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The properties of previous loss functions in deep facial recognition.
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The properties of previous loss functions in deep facial recognition.
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The properties of previous loss functions in deep facial recognition.
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Image Search Results


The properties of previous loss functions in deep facial recognition.

Journal: Sensors (Basel, Switzerland)

Article Title: CNN-Based Facial Expression Recognition with Simultaneous Consideration of Inter-Class and Intra-Class Variations

doi: 10.3390/s23249658

Figure Lengend Snippet: The properties of previous loss functions in deep facial recognition.

Article Snippet: Although it was intuitively appealing and more interpretable than the L-Softmax [ ], selecting the margin hyperparameter was challenging.

Techniques: Selection

The properties of previous loss functions in deep facial recognition.

Journal: Sensors (Basel, Switzerland)

Article Title: CNN-Based Facial Expression Recognition with Simultaneous Consideration of Inter-Class and Intra-Class Variations

doi: 10.3390/s23249658

Figure Lengend Snippet: The properties of previous loss functions in deep facial recognition.

Article Snippet: AM-Softmax [ ] , No , Yes , The hyperparameter selection is challenging.

Techniques: Selection