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Found 8 papers, 3 papers with code
EasyFS: an Efficient Model-free Feature Selection Framework via Elastic Transformation of Features
Traditional model-free feature selection methods treat each feature independently while disregarding the interrelationships among features, which leads to relatively poor performance compared with the model-aware methods.
Brain-inspired Distributed Memorization Learning for Efficient Feature-free Unsupervised Domain Adaptation
Compared with gradient based artificial neural networks, biological neural networks usually show a more powerful generalization ability to quickly adapt to unknown environments without using any gradient back-propagation procedure.
Learning From the Master: Distilling Cross-Modal Advanced Knowledge for Lip Reading
To these ends, we introduce a trainable "master" network which ingests both audio signals and silent lip videos instead of a pretrained teacher.
AVR: Attention based Salient Visual Relationship Detection
To address this problem, we propose an attention based model, namely AVR, to achieve salient visual relationships based on both local and global context of the relationships.
Learning Shared Semantic Space with Correlation Alignment for Cross-modal Event Retrieval
In this paper, we propose to learn shared semantic space with correlation alignment (${S}^{3}CA$) for multimodal data representations, which aligns nonlinear correlations of multimodal data distributions in deep neural networks designed for heterogeneous data.
Cross-dataset Person Re-Identification Using Similarity Preserved Generative Adversarial Networks
Meanwhile, SimPGAN uses the similarity consistency loss, which is measured by a siamese deep convolutional neural network, to preserve the similarity of the transformed images of the same person.
Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns
Most of the proposed person re-identification algorithms conduct supervised training and testing on single labeled datasets with small size, so directly deploying these trained models to a large-scale real-world camera network may lead to poor performance due to underfitting.
T-CONV: A Convolutional Neural Network For Multi-scale Taxi Trajectory Prediction
Precise destination prediction of taxi trajectories can benefit many intelligent location based services such as accurate ad for passengers.
