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Found 7 papers, 4 papers with code
Rediscovering BCE Loss for Uniform Classification
We also propose the uniform classification accuracy as a metric to measure the model's performance in uniform classification.
UniFace: Unified Cross-Entropy Loss for Deep Face Recognition
To bridge this gap, we design a UCE (Unified Cross-Entropy) loss for face recognition model training, which is built on the vital constraint that all the positive sample-to-class similarities shall be larger than the negative ones.
Activation Template Matching Loss for Explainable Face Recognition
Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionalsion datasets?
WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification
We firstly propose general DWT and inverse DWT (IDWT) layers applicable to various orthogonal and biorthogonal discrete wavelets like Haar, Daubechies, and Cohen, etc., and then design wavelet integrated CNNs (WaveCNets) by integrating DWT into the commonly used CNNs (VGG, ResNets, and DenseNet).
Neuron segmentation using 3D wavelet integrated encoder-decoder network
Then, we design 3D WaveUNet, the first 3D wavelet integrated encoder-decoder network, to segment the nerve fibers in the cubes; the wavelets could assist the deep networks in suppressing data noises and connecting the broken fibers.
WaveSNet: Wavelet Integrated Deep Networks for Image Segmentation
In deep networks, the lost data details significantly degrade the performances of image segmentation.
Wavelet Integrated CNNs for Noise-Robust Image Classification
The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets.
