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Found 10 papers, 3 papers with code
Large Convolutional Model Tuning via Filter Subspace
Furthermore, each filter atom can be recursively decomposed as a combination of another set of atoms, which naturally expands the number of tunable parameters in the filter subspace.
Training Bayesian Neural Networks with Sparse Subspace Variational Inference
Bayesian neural networks (BNNs) offer uncertainty quantification but come with the downside of substantially increased training and inference costs.
Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning
We also show the effectiveness of our RL fine-tuning framework on enhancing the diversity of image generation with different types of diffusion models including class-conditional models and text-conditional models e. g. StableDiffusion.
Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks
In this paper, we introduce spatiotemporal joint filter decomposition to decouple spatial and temporal learning, while preserving spatiotemporal dependency in a video.
Learning to Learn Dense Gaussian Processes for Few-Shot Learning
In contrast to sparse Gaussian processes, we define a set of dense inducing variables to be of a much larger size than the support set in each task, which collects prior knowledge from experienced tasks.
Image Generation using Continuous Filter Atoms
In this paper, we model the subspace of convolutional filters with a neural ordinary differential equation (ODE) to enable gradual changes in generated images.
Continual Learning with Filter Atom Swapping
In this paper, we first enforce a low-rank filter subspace by decomposing convolutional filters within each network layer over a small set of filter atoms.
Adaptive Convolutions with Per-pixel Dynamic Filter Atom
Applying feature dependent network weights have been proved to be effective in many fields.
Graph Convolution with Low-rank Learnable Local Filters
Recent deep models using graph convolutions provide an appropriate framework to handle such non-Euclidean data, but many of them, particularly those based on global graph Laplacians, lack expressiveness to capture local features required for representation of signals lying on the non-Euclidean grid.
AED-Net: An Abnormal Event Detection Network
It is challenging to detect the anomaly in crowded scenes for quite a long time.
