Rasmus Tirsgaard | Searcharxiv

arXiv2026

Semi-Supervised Learning for Molecular Graphs via Ensemble Consensus

Machine learning is transforming molecular sciences by accelerating property prediction, simulation, and the discovery of new molecules and materials. Acquiring labeled data in these domains is often costly and time-consuming, whereas large collections of unlabeled molecular data are readily available. Standard semi-supervised learning methods often rely on label-preserving augmentations, which are challenging to design in the molecular domain, where minor changes can drastically alter properties. In this work, we show that semi-supervised methods that rely on an ensemble consensus can boost predictive accuracy across a diverse range of molecular datasets, task types, and graph neural network architectures. We find that training with an ensemble consensus objective increases robustness in models and exhibits an effect similar to knowledge distillation; an individual member of an ensemble trained this way outperforms a full ensemble trained in a traditional supervised fashion in almost all cases. In addition, this type of semi-supervised training reduces calibration error.

cs.LG↗

arXiv2023

Two Views Are Better than One: Monocular 3D Pose Estimation with Multiview Consistency

Deducing a 3D human pose from a single 2D image is inherently challenging because multiple 3D poses can correspond to the same 2D representation. 3D data can resolve this pose ambiguity, but it is expensive to record and requires an intricate setup that is often restricted to controlled lab environments. We propose a method that improves the performance of deep learning-based monocular 3D human pose estimation models by using multiview data only during training, but not during inference. We introduce a novel loss function, consistency loss, which operates on two synchronized views. This approach is simpler than previous models that require 3D ground truth or intrinsic and extrinsic camera parameters. Our consistency loss penalizes differences in two pose sequences after rigid alignment. We also demonstrate that our consistency loss substantially improves performance for fine-tuning without requiring 3D data. Furthermore, we show that using our consistency loss can yield state-of-the-art performance when training models from scratch in a semi-supervised manner. Our findings provide a simple way to capture new data, e.g in a new domain. This data can be added using off-the-shelf cameras with no calibration requirements. We make all our code and data publicly available.

cs.CV↗