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Found 22 papers, 5 papers with code
Language-Guided Audio-Visual Learning for Long-Term Sports Assessment
Long-term sports assessment is a challenging task in video understanding since it requires judging complex movement variations and action-music coordination.
OpenViewer: Openness-Aware Multi-View Learning
However, deploying these models in real-world scenarios presents two primary openness challenges.
StegFormer: Rebuilding the Glory of the Autoencoder-Based Steganography
Image hiding aims to conceal one or more secret images within a cover image of the same resolution.
ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks
Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method.
FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework
Although federated learning offers a promising solution, the prevalent non-IID problems and high communication costs present significant challenges, particularly pronounced in collaborations with graph data distributed among different participants.
Bridging Trustworthiness and Open-World Learning: An Exploratory Neural Approach for Enhancing Interpretability, Generalization, and Robustness
As researchers strive to narrow the gap between machine intelligence and human through the development of artificial intelligence technologies, it is imperative that we recognize the critical importance of trustworthiness in open-world, which has become ubiquitous in all aspects of daily life for everyone.
High-Similarity-Pass Attention for Single Image Super-Resolution
Based on these findings, we introduced a concise yet effective soft thresholding operation to obtain high-similarity-pass attention (HSPA), which is beneficial for generating a more compact and interpretable distribution.
AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing
In light of this, we propose an Alternating Graph-regularized Neural Network (AGNN) composed of Graph Convolutional Layer (GCL) and Graph Embedding Layer (GEL).
Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs
Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks. One challenge of heterogeneous graph learning is the design of learnable meta-paths, which significantly influences the quality of learned embeddings. Thus, in this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically studies meta-paths containing multi-hop neighbors from an adaptive aggregation of multi-order adjacency matrices.
Properties and Potential Applications of Random Functional-Linked Types of Neural Networks
Random functional-linked types of neural networks (RFLNNs), e. g., the extreme learning machine (ELM) and broad learning system (BLS), which avoid suffering from a time-consuming training process, offer an alternative way of learning in deep structure.
Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network
In this work, we study the problem of partitioning a set of graphs into different groups such that the graphs in the same group are similar while the graphs in different groups are dissimilar.
Multi-view Graph Convolutional Networks with Differentiable Node Selection
Due to the powerful capability to gather information of neighborhood nodes, in this paper, we apply Graph Convolutional Network (GCN) to cope with heterogeneous-graph data originating from multi-view data, which is still under-explored in the field of GCN.
Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning
In practical applications, multi-view data depicting objectives from assorted perspectives can facilitate the accuracy increase of learning algorithms.
Defense against Backdoor Attacks via Identifying and Purifying Bad Neurons
The opacity of neural networks leads their vulnerability to backdoor attacks, where hidden attention of infected neurons is triggered to override normal predictions to the attacker-chosen ones.
Unsupervised Deep Discriminant Analysis Based Clustering
This work presents an unsupervised deep discriminant analysis for clustering.
Wasserstein Adversarial Learning based Temporal Knowledge Graph Embedding
In order to deal with this issue, several temporal knowledge graph embedding (TKGE) approaches have been proposed to integrate temporal and structural information in recent years.
Enhance transferability of adversarial examples with model architecture
Transferability of adversarial examples is of critical importance to launch black-box adversarial attacks, where attackers are only allowed to access the output of the target model.
Efficient Deep Embedded Subspace Clustering
The proposed method is out of the self-expressive framework, scales to the sample size linearly, and is applicable to arbitrarily large datasets and online clustering scenarios.
Object Tracking by Least Spatiotemporal Searches
This paper proposes a strategy named IHMs (Intermediate Searching at Heuristic Moments): each step we figure out which moment is the best to search according to a heuristic indicator, then at that moment search locations one by one in descending order of predicted appearing probabilities, until a search hits; iterate this step until we get the object's current location.
HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions
To address these two problems, we propose in this paper a novel GAN-based model, HDR-GAN, for synthesizing HDR images from multi-exposed LDR images.
Drug-Drug Interaction Prediction with Wasserstein Adversarial Autoencoder-based Knowledge Graph Embeddings
Recently, several knowledge graph embedding approaches have received increasing attention in the DDI domain due to their capability of projecting drugs and interactions into a low-dimensional feature space for predicting links and classifying triplets.
A novel particle swarm optimizer with multi-stage transformation and genetic operation for VLSI routing
Firstly, in order to be able to handle two types of SMT problems at the same time, an effective edge-vertex encoding strategy is proposed.
