A Novel Method for Simultaneous Facial Expression Transfer and Audio-Driven Lip Synchronization in Talking Face Generation
A novel NeRF-based method that simultaneously combines lip-syncing to a target audio with facial expression transfer to generate high-fidelity talking faces.
A Unified Framework for Neural Computation and Learning from Continuous Data Streams
This paper proposes Hamiltonian Learning, a novel unified framework for learning with neural networks from a possibly infinite stream of data, in an online manner, without having access to future information.
An Artificial Neural Network for Image Classification Inspired by the Aversive Olfactory Learning Circuits of Caenorhabditis Elegans
This study introduces an artificial neural network (ANN) for image classification that is inspired by the aversive olfactory learning circuits of the nematode Caenorhabditis elegans, demonstrating superior performance compared to control networks.
Synchronized Talking Head Synthesis: Addressing the Challenges of Consistent Identity, Lip Movements, Facial Expressions, and Head Poses
To achieve highly synchronized and realistic speech-driven talking head synthesis, SyncTalk effectively maintains subject identity, enhances synchronization of lip movements, facial expressions, and head poses, and improves visual quality through a novel NeRF-based framework.
時間依存性を持つ多機能性のニューラルユニット: スプリングとストップウォッチ
単一の動的ニューラルユニットが、同一の動的軌道の中で、時間によって異なる非線形計算を実行できる。
Efficient Neural Architecture Search Using a Forward-and-Reverse Graph Predictor
A novel graph-based performance predictor that leverages both forward and reverse representations of neural architectures to enhance prediction accuracy, especially in data-limited settings.
Unifying Recurrent and Non-Recurrent Neural Networks: A Discrete Dynamical Systems Approach
Recurrent neural networks (RNNs) and multilayer perceptrons (MLPs) can both be represented as iterative maps, revealing a deeper relationship between these seemingly distinct neural network architectures. This perspective provides insights into the theoretical and practical aspects of neural networks.
Pointer Networks Trained Better via Evolutionary Algorithms: Improving PtrNet Performance with EAs
PtrNet can achieve better performance in solving COPs, especially on high-dimensional instances, when trained with Evolutionary Algorithms (EAs).
Trainable and Explainable Simplicial Map Neural Networks: An In-Depth Analysis
Simplicial Map Neural Networks (SMNNs) training process and explainability are explored, addressing limitations and proposing innovative solutions.
Trainable and Explainable Simplicial Map Neural Networks: Overcoming Limitations and Enhancing Training
Simplicial Map Neural Networks (SMNNs) address limitations through a new training procedure, enhancing efficiency and generalization.
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