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Found 8 papers, 7 papers with code
HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation
Neural radiance fields (NeRFs) are a widely accepted standard for synthesizing new 3D object views from a small number of base images.
Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
In this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting.
Augmentation-aware Self-supervised Learning with Conditioned Projector
By learning to remain invariant to applied data augmentations, methods such as SimCLR and MoCo are able to reach quality on par with supervised approaches.
Hypernetwork approach to Bayesian MAML
Unfortunately, previous modifications of MAML are limited due to the simplicity of Gaussian posteriors, MAML-like gradient-based weight updates, or by the same structure enforced for universal and adapted weights.
HyperShot: Few-Shot Learning by Kernel HyperNetworks
Few-shot models aim at making predictions using a minimal number of labeled examples from a given task.
MisConv: Convolutional Neural Networks for Missing Data
Processing of missing data by modern neural networks, such as CNNs, remains a fundamental, yet unsolved challenge, which naturally arises in many practical applications, like image inpainting or autonomous vehicles and robots.
RegFlow: Probabilistic Flow-based Regression for Future Prediction
Predicting future states or actions of a given system remains a fundamental, yet unsolved challenge of intelligence, especially in the scope of complex and non-deterministic scenarios, such as modeling behavior of humans.
Estimating conditional density of missing values using deep Gaussian mixture model
We consider the problem of estimating the conditional probability distribution of missing values given the observed ones.
