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Found 5 papers, 1 papers with code
Learning Dynamic Tetrahedra for High-Quality Talking Head Synthesis
Recent works in implicit representations, such as Neural Radiance Fields (NeRF), have advanced the generation of realistic and animatable head avatars from video sequences.
Masked Reconstruction Contrastive Learning with Information Bottleneck Principle
In order to alleviate the discriminative information overfitting problem effectively, we employ the reconstruction task to regularize the discriminative task.
Ranked #23 on
Self-Supervised Image Classification
on ImageNet
Contrastive Learning
Self-Supervised Image Classification
+2
BERT-Flow-VAE: A Weakly-supervised Model for Multi-Label Text Classification
Multi-label Text Classification (MLTC) is the task of categorizing documents into one or more topics.
Multi Label Text Classification
Multi-Label Text Classification
+4
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization
For this purpose, we propose a universal framework to generate subgraphs in an auto-regressive way and then using these subgraphs to guide the learning of graph representation by Graph Neural Networks.
Blocked and Hierarchical Disentangled Representation From Information Theory Perspective
It is well known that information theory has an excellent explanatory meaning for the network, so we start to solve the disentanglement problem from the perspective of information theory.
