Search Results for author:
Found 8 papers, 3 papers with code
A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels
In this paper, we focus on a new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels.
Ranked #7 on
RGB Salient Object Detection
on PASCAL-S
BNS: Building Network Structures Dynamically for Continual Learning
Continual learning (CL) of a sequence of tasks is often accompanied with the catastrophic forgetting(CF) problem.
Loop closure detection using local 3D deep descriptors
We compare our L3D-based loop closure approach with recent approaches on LiDAR data and achieve state-of-the-art loop closure detection accuracy.
HRN: A Holistic Approach to One Class Learning
Existing neural network based one-class learning methods mainly use various forms of auto-encoders or GAN style adversarial training to learn a latent representation of the given one class of data.
Using the Past Knowledge to Improve Sentiment Classification
It proposes a new lifelong learning model (called L2PG) that can retain and selectively transfer the knowledge learned in the past to help learn the new task.
Text Classification with Novelty Detection
In this paper, we propose a significantly more effective approach that converts the original problem to a pair-wise matching problem and then outputs how probable two instances belong to the same class.
Feature Projection for Improved Text Classification
In this paper, we propose a novel angle to further improve this representation learning, i. e., feature projection.
On the Iteration Complexity Analysis of Stochastic Primal-Dual Hybrid Gradient Approach with High Probability
In this paper, we propose a stochastic Primal-Dual Hybrid Gradient (PDHG) approach for solving a wide spectrum of regularized stochastic minimization problems, where the regularization term is composite with a linear function.
