Search Results for author:
Found 4 papers, 2 papers with code
Influential Rank: A New Perspective of Post-training for Robust Model against Noisy Labels
Deep neural network can easily overfit to even noisy labels due to its high capacity, which degrades the generalization performance of a model.
Class-Attentive Diffusion Network for Semi-Supervised Classification
In contrast to the existing diffusion methods with a transition matrix determined solely by the graph structure, CAD considers both the node features and the graph structure with the design of our class-attentive transition matrix that utilizes a classifier.
Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators
We formulate the cross-modal association in Bayesian inference framework realized by a deep neural network with multiple variational auto-encoders and variational associators.
Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification
Our strategy alleviates the problem of gradient vanishing in low-level layers and robustly trains the low-level layers to fit the ReID dataset, thereby increasing the performance of ReID tasks.
