Search Results for author:
Found 7 papers, 5 papers with code
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts
Most existing methods perform training on multiple source domains using a single model, and the same trained model is used on all unseen target domains.
Ranked #22 on
Domain Generalization
on DomainNet
Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022
In this work, we present the winning solution for ORBIT Few-Shot Video Object Recognition Challenge 2022.
Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay
In this paper, we show through empirical results that adopting the data replay is surprisingly favorable.
MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning
The learning objective of these methods is often hand-engineered and is not directly tied to the objective (i. e. incrementally learning new classes) during testing.
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
In practice, it performs similarly to the Hungarian algorithm during inference.
Distilling the Posterior in Bayesian Neural Networks
We propose a framework, Adversarial Posterior Distillation, to distill the SGLD samples using a Generative Adversarial Network (GAN).
Adversarial Distillation of Bayesian Neural Network Posteriors
We propose a framework, Adversarial Posterior Distillation, to distill the SGLD samples using a Generative Adversarial Network (GAN).
