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Found 7 papers, 2 papers with code
Unsupervised Visual Representation Learning by Synchronous Momentum Grouping
In this paper, we propose a genuine group-level contrastive visual representation learning method whose linear evaluation performance on ImageNet surpasses the vanilla supervised learning.
Ranked #38 on
Self-Supervised Image Classification
on ImageNet
WAD-CMSN: Wasserstein Distance based Cross-Modal Semantic Network for Zero-Shot Sketch-Based Image Retrieval
Zero-shot sketch-based image retrieval (ZSSBIR), as a popular studied branch of computer vision, attracts wide attention recently.
Multi-scale Graph Convolutional Networks with Self-Attention
Graph convolutional networks (GCNs) have achieved remarkable learning ability for dealing with various graph structural data recently.
SStaGCN: Simplified stacking based graph convolutional networks
Graph convolutional network (GCN) is a powerful model studied broadly in various graph structural data learning tasks.
Towards Robust Neural Networks via Orthogonal Diversity
In this way, the proposed DIO augments the model and enhances the robustness of DNN itself as the learned features can be corrected by these mutually-orthogonal paths.
Sparse Generalized Canonical Correlation Analysis: Distributed Alternating Iteration based Approach
To overcome this limitation, in this paper, we propose a sparse generalized canonical correlation analysis (GCCA), which could detect the latent relations of multiview data with sparse structures.
Type I Attack for Generative Models
To implement the Type I attack, we destroy the original one by increasing the distance in input space while keeping the output similar because different inputs may correspond to similar features for the property of deep neural network.
