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Found 8 papers, 4 papers with code
Elastic Aggregation for Federated Optimization
Federated learning enables the privacy-preserving training of neural network models using real-world data across distributed clients.
Rethinking skip connection model as a learnable Markov chain
In this work, we deep dive into the model's behaviors with skip connections which can be formulated as a learnable Markov chain.
OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework
Federated Learning alleviates these problems by decentralizing model training, thereby removing the need for data transfer and aggregation.
Potential Convolution: Embedding Point Clouds into Potential Fields
In this work, rather than defining a continuous or discrete kernel, we directly embed convolutional kernels into the learnable potential fields, giving rise to potential convolution.
AReLU: Attention-based Rectified Linear Unit
Adding the attention module with a rectified linear unit (ReLU) results in an amplification of positive elements and a suppression of negative ones, both with learned, data-adaptive parameters.
Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation
To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified representation for a large variety of instances of a certain object category.
Enhancement Mask for Hippocampus Detection and Segmentation
Detection and segmentation of the hippocampal structures in volumetric brain images is a challenging problem in the area of medical imaging.
Deformable Object Tracking with Gated Fusion
In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance.
