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Found 7 papers, 2 papers with code
Photometric Transformer Networks and Label Adjustment for Breast Density Prediction
The label distillation, a type of pseudo-label techniques, is intended to mitigate the grading variation.
SRM : A Style-based Recalibration Module for Convolutional Neural Networks
Following the advance of style transfer with Convolutional Neural Networks (CNNs), the role of styles in CNNs has drawn growing attention from a broader perspective.
Ranked #129 on
Image Classification
on CIFAR-10
Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networks
Data is one of the most important factors in machine learning.
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
Real-world image recognition is often challenged by the variability of visual styles including object textures, lighting conditions, filter effects, etc.
Semantic Noise Modeling for Better Representation Learning
From the base model, we introduce a semantic noise modeling method which enables class-conditional perturbation on latent space to enhance the representational power of learned latent feature.
Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation
The unpooling-deconvolution combination helps to eliminate less discriminative features in a feature extraction stage, since output features of the deconvolution layer are reconstructed from the most discriminative unpooled features instead of the raw one.
Self-Transfer Learning for Fully Weakly Supervised Object Localization
With the help of transfer learning which adopts weight parameters of a pre-trained network, the weakly supervised learning framework for object localization performs well because the pre-trained network already has well-trained class-specific features.
