Search Results for author:
Found 11 papers, 4 papers with code
MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction
Chinese Spelling Correction (CSC) is a task to detect and correct misspelled characters in Chinese texts.
Deep Anomaly Detection and Search via Reinforcement Learning
Semi-supervised Anomaly Detection (AD) is a kind of data mining task which aims at learning features from partially-labeled datasets to help detect outliers.
A Concept and Argumentation based Interpretable Model in High Risk Domains
Interpretability has become an essential topic for artificial intelligence in some high-risk domains such as healthcare, bank and security.
Knowledge Amalgamation for Object Detection with Transformers
Moreover, the transformer-based students excel in learning amalgamated knowledge, as they have mastered heterogeneous detection tasks rapidly and achieved superior or at least comparable performance to those of the teachers in their specializations.
Personalized Image Semantic Segmentation
In this paper, we address the problem of personalized image segmentation.
Self-Born Wiring for Neural Trees
Neural trees aim at integrating deep neural networks and decision trees so as to bring the best of the two worlds, including representation learning from the former and faster inference from the latter.
DEPARA: Deep Attribution Graph for Deep Knowledge Transferability
In this paper, we propose the DEeP Attribution gRAph (DEPARA) to investigate the transferability of knowledge learned from PR-DNNs.
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift
This strategy leads to severe meta shift issues across multiple tasks, meaning the learned prototypes or class descriptors are not stable as each task only involves their own support set.
Self-Supervised Learning For Few-Shot Image Classification
In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself.
Ranked #4 on
Few-Shot Image Classification
on Mini-ImageNet - 1-Shot Learning
(using extra training data)
Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network
However, the video length is usually long, and there are hierarchical relationships between frames across events in the video, the performance of RNN based models are decreased.
Ranked #1 on
Video Classification
on YouTube-8M
Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing
We introduce a deep learning approach to overcome the drawbacks by applying a large training data set, auto feature selection and fast, accurate labeling.
