most citedGenerative Adversarial Networks as Variational Training of Energy Based Models

18 citations · 25 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2023

Dense Affinity Matching for Few-Shot Segmentation

Hao Chen, Yonghan Dong, Zheming Lu +4

Few-Shot Segmentation (FSS) aims to segment the novel class images with a few annotated samples. In this paper, we propose a dense affinity matching (DAM) framework to exploit the…

cs.CV20231 cited

Multi-Content Interaction Network for Few-Shot Segmentation

Hao Chen, Yunlong Yu, Yonghan Dong +3

Few-Shot Segmentation (FSS) is challenging for limited support images and large intra-class appearance discrepancies. Most existing approaches focus on extracting high-level repres…

cs.LG20164 cited

Structural Correspondence Learning for Cross-lingual Sentiment Classification with One-to-many Mappings

Nana Li, Shuangfei Zhai, Zhongfei Zhang +1

Structural correspondence learning (SCL) is an effective method for cross-lingual sentiment classification. This approach uses unlabeled documents along with a word translation ora…

cs.LG20162 cited

S3Pool: Pooling with Stochastic Spatial Sampling

Shuangfei Zhai, Hui Wu, Abhishek Kumar +4

Feature pooling layers (e.g., max pooling) in convolutional neural networks (CNNs) serve the dual purpose of providing increasingly abstract representations as well as yielding com…

cs.LG201618 cited

Generative Adversarial Networks as Variational Training of Energy Based Models

Shuangfei Zhai, Yu Cheng, Rogerio Feris +1

In this paper, we study deep generative models for effective unsupervised learning. We propose VGAN, which works by minimizing a variational lower bound of the negative log likelih…