199 citations · 202 across the 12 of their papers we have counts for
12 papers
ConceptFlow: Hierarchical and Fine-grained Concept-Based Explanation for Convolutional Neural Networks
Xinyu Mu, Hui Dou, Furao Shen +1
Concept-based interpretability for Convolutional Neural Networks (CNNs) aims to align internal model representations with high-level semantic concepts, but existing approaches larg…
RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment
Suorong Yang, Peijia Li, Furao Shen +1
Modern deep architectures often rely on large-scale datasets, but training on these datasets incurs high computational and storage overhead. Real-world datasets often contain subst…
SPAT: Sensitivity-based Multihead-attention Pruning on Time Series Forecasting Models
Suhan Guo, Jiahong Deng, Mengjun Yi +2
Attention-based architectures have achieved superior performance in multivariate time series forecasting but are computationally expensive. Techniques such as patching and adaptive…
Interactive Instance Annotation with Siamese Networks
Xiang Xu, Ruotong Li, Mengjun Yi +3
Annotating instance masks is time-consuming and labor-intensive. A promising solution is to predict contours using a deep learning model and then allow users to refine them. Howeve…
Explaining Model Overfitting in CNNs via GMM Clustering
Hui Dou, Xinyu Mu, Mengjun Yi +3
Convolutional Neural Networks (CNNs) have demonstrated remarkable prowess in the field of computer vision. However, their opaque decision-making processes pose significant challeng…
Estimating the treatment effect over time under general interference through deep learner integrated TMLE
Suhan Guo, Furao Shen, Ni Li
Understanding the effects of quarantine policies in populations with underlying social networks is crucial for public health, yet most causal inference methods fail here due to the…
Embedding Space Allocation with Angle-Norm Joint Classifiers for Few-Shot Class-Incremental Learning
Dunwei Tu, Huiyu Yi, Tieyi Zhang +3
Few-shot class-incremental learning (FSCIL) aims to continually learn new classes from only a few samples without forgetting previous ones, requiring intelligent agents to adapt to…
EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classification
Suorong Yang, Furao Shen, Jian Zhao
Data augmentation (DA) has been widely used to improve the generalization of deep neural networks. While existing DA methods have proven effective, they often rely on augmentation…
AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation
Suorong Yang, Peijia Li, Xin Xiong +2
Data augmentation (DA) is widely employed to improve the generalization performance of deep models. However, most existing DA methods employ augmentation operations with fixed or r…
Not All Data Matters: An End-to-End Adaptive Dataset Pruning Framework for Enhancing Model Performance and Efficiency
Suorong Yang, Hongchao Yang, Suhan Guo +2
While deep neural networks have demonstrated remarkable performance across various tasks, they typically require massive training data. Due to the presence of redundancies and bias…
A Simple Geometric-Aware Indoor Positioning Interpolation Algorithm Based on Manifold Learning
Suorong Yang, Geng Zhang, Jian Zhao +1
Interpolation methodologies have been widely used within the domain of indoor positioning systems. However, existing indoor positioning interpolation algorithms exhibit several inh…
Image Data Augmentation for Deep Learning: A Survey
Suorong Yang, Weikang Xiao, Mengchen Zhang +3
Deep learning has achieved remarkable results in many computer vision tasks. Deep neural networks typically rely on large amounts of training data to avoid overfitting. However, la…