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Found 5 papers, 3 papers with code
Robust Neural Posterior Estimation and Statistical Model Criticism
In this work we revisit neural posterior estimation (NPE), a class of algorithms that enable black-box parameter inference in simulation models, and consider the implication of a simulation-to-reality gap.
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference
In this work, we provide the first comprehensive study of the behaviour of neural SBI algorithms in the presence of various forms of model misspecification.
Scalable learning for bridging the species gap in image-based plant phenotyping
This study is applicable to use of synthetic data for automating the measurement of phenotypic traits.
Temporally Coherent Embeddings for Self-Supervised Video Representation Learning
The proposed method exploits inherent structure of unlabeled video data to explicitly enforce temporal coherency in the embedding space, rather than indirectly learning it through ranking or predictive proxy tasks.
Ranked #37 on
Self-Supervised Action Recognition
on UCF101
Deep Leaf Segmentation Using Synthetic Data
Our proposed approach achieves 90% leaf segmentation score on the A1 test set outperforming the-state-of-the-art approaches for the CVPPP Leaf Segmentation Challenge (LSC).
