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Found 10 papers, 4 papers with code
Multilingual Content Moderation: A Case Study on Reddit
Content moderation is the process of flagging content based on pre-defined platform rules.
DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method
Joint 2D cardiac segmentation and 3D volume reconstruction are fundamental to building statistical cardiac anatomy models and understanding functional mechanisms from motion patterns.
Modular Adaptation for Cross-Domain Few-Shot Learning
Adapting pre-trained representations has become the go-to recipe for learning new downstream tasks with limited examples.
DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images
Cardiac tagging magnetic resonance imaging (t-MRI) is the gold standard for regional myocardium deformation and cardiac strain estimation.
Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning
Few-Shot Learning (FSL) aims to improve a model's generalization capability in low data regimes.
PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data
The 3D volumetric shape of the heart's left ventricle (LV) myocardium (MYO) wall provides important information for diagnosis of cardiac disease and invasive procedure navigation.
Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection
The approach projects the label embedding vectors into a low-dimensional space to induce better inter-label relationships and explicitly facilitate information transfer from seen labels to unseen labels, while simultaneously learning a max-margin multi-label classifier with the projected label embeddings.
Multi-Label Image Classification
Multi-label zero-shot learning
+1
Progressive Ensemble Networks for Zero-Shot Recognition
The ensemble network is built by learning multiple image classification functions with a shared feature extraction network but different label embedding representations, which enhance the diversity of the classifiers and facilitate information transfer to unlabeled classes.
Deep Triplet Ranking Networks for One-Shot Recognition
Despite the breakthroughs achieved by deep learning models in conventional supervised learning scenarios, their dependence on sufficient labeled training data in each class prevents effective applications of these deep models in situations where labeled training instances for a subset of novel classes are very sparse -- in the extreme case only one instance is available for each class.
Zero-Shot Classification With Discriminative Semantic Representation Learning
The proposed approach aims to identify a set of common high-level semantic components across the two domains via non-negative sparse matrix factorization, while enforcing the representation vectors of the images in this common component-based space to be discriminatively aligned with the attribute-based label representation vectors.
