research
∙
06/08/2023
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models
The advent of large pre-trained models has brought about a paradigm shif...
share
research
∙
02/18/2023
Closed-Loop Transcription via Convolutional Sparse Coding
Autoencoding has achieved great empirical success as a framework for lea...
research
∙
01/04/2023
Unsupervised Manifold Linearizing and Clustering
Clustering data lying close to a union of low-dimensional manifolds, wit...
research
∙
10/30/2022
Unsupervised Learning of Structured Representations via Closed-Loop Transcription
This paper proposes an unsupervised method for learning a unified repres...
research
∙
10/24/2022
Revisiting Sparse Convolutional Model for Visual Recognition
Despite strong empirical performance for image classification, deep neur...
research
∙
02/11/2022
Incremental Learning of Structured Memory via Closed-Loop Transcription
This work proposes a minimal computational model for learning a structur...
research
∙
11/12/2021
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction
This work proposes a new computational framework for learning an explici...
research
∙
04/22/2021
Fully Convolutional Line Parsing
We present a one-stage Fully Convolutional Line Parsing network (F-Clip)...
research
∙
04/16/2021
Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image
Single-image room layout reconstruction aims to reconstruct the enclosed...
research
∙
08/07/2020