research
∙
09/17/2022
Few-Shot Classification with Contrastive Learning
A two-stage training paradigm consisting of sequential pre-training and ...
share
research
∙
09/03/2022
Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition
The goal of fine-grained action recognition is to successfully discrimin...
research
∙
02/12/2021
Exploiting Spline Models for the Training of Fully Connected Layers in Neural Network
The fully connected (FC) layer, one of the most fundamental modules in a...
research
∙
09/11/2020
SA-Net: A deep spectral analysis network for image clustering
Although supervised deep representation learning has attracted enormous ...
research
∙
09/11/2020
Spectral Analysis Network for Deep Representation Learning and Image Clustering
Deep representation learning is a crucial procedure in multimedia analys...
research
∙
09/11/2020
An unsupervised deep learning framework via integrated optimization of representation learning and GMM-based modeling
While supervised deep learning has achieved great success in a range of ...
research
∙
09/11/2020
Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation
Machine learning models trained in one domain perform poorly in the othe...
research
∙
09/11/2020
Adversarial Learning for Zero-shot Domain Adaptation
Zero-shot domain adaptation (ZSDA) is a category of domain adaptation pr...
research
∙
08/03/2016