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LegoNet: Alternating Model Blocks for Medical Image Segmentation
Since the emergence of convolutional neural networks (CNNs), and later vision transformers (ViTs), the common paradigm for model development has always been using a set of identical block types with varying parameters/hyper-parameters.
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction
The material genetic engineering program aims to establish the relationship between material composition/process, organization, and performance to realize the reverse design of materials, thereby accelerating the research and development of new materials.
Class Knowledge Overlay to Visual Feature Learning for Zero-Shot Image Classification
The approach also applies a semi-supervised learning process to re-train knowledge-to-visual model.
Multi-Knowledge Fusion for New Feature Generation in Generalized Zero-Shot Learning
Suffering from the semantic insufficiency and domain-shift problems, most of existing state-of-the-art methods fail to achieve satisfactory results for Zero-Shot Learning (ZSL).
Cross Knowledge-based Generative Zero-Shot Learning Approach with Taxonomy Regularization
CKL enables more relevant semantic features to be trained for semantic-to-visual feature embedding in ZSL, while Taxonomy Regularization (TR) significantly improves the intersections with unseen images with more generalized visual features generated from generative network.
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control
Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment.
