Pseudo Labeling is a semi-supervised learning technique where the model is trained on a combination of labeled and unlabeled data. After training on the labeled data, the model is used to predict labels for the unlabeled data. These predicted labels (pseudo labels) are then combined with the original labeled data to train the model further. Pseudo labeling is often employed to make use of additional unlabeled data and improve model performance.

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Domain-Guided Weight Modulation for Semi-Supervised Domain Generalization

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Unarguably deep learning models capable of generalizing to unseen domain data while leveraging a few labels are of great practical significance due to low developmental costs. In search of this endeavor we study the challenging problem of semi-supervised domain generalization (SSDG) where the goal is to learn a domain-generalizable model while using only a small fraction of labeled data and a relatively large fraction of unlabeled data. Domain generalization (DG) methods show subpar performance under the SSDG setting whereas semi-supervised learning (SSL) methods demonstrate relatively better performance however they are considerably poor compared to the fully-supervised DG methods. Towards handling this new but challenging problem of SSDG we propose a novel method that can facilitate the generation of accurate pseudo-labels under various domain shifts. This is accomplished by retaining the domain-level specialism in the classifier during training corresponding to each source domain. Specifically we first create domain-level information vectors on the fly which are then utilized to learn a domain-aware mask for modulating the classifier’s weights. We provide a mathematical interpretation for the effect of this modulation procedure on both pseudo-labeling and model training. Our method is plug-and-play and can be readily applied to different SSL baselines for SSDG. Extensive experiments on six challenging datasets in two different SSDG settings show that our method provides visible gains over the various strong SSL-based SSDG baselines. Our code is available at github.com/DGWM.

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AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving

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Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of safety assurance. However, objects encountered on the road exhibit a long-tailed distribution, with rare or unseen categories posing challenges to a deployed perception model. This necessitates an expensive process of continuously curating and annotating data with significant human effort. We propose to leverage recent advances in vision-language and large language models to design an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. This process operates iteratively, allowing for continuous self-improvement of the model. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method’s superior performance at a reduced cost.

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T-Cell Receptor-Peptide Interaction Prediction with Physical Model Augmented Pseudo-Labeling

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Predicting the interactions between T-cell receptors (TCRs) and peptides is crucial for the development of personalized medicine and targeted vaccine in immunotherapy. Current datasets for training deep learning models of this purpose remain constrained without diverse TCRs and peptides. To combat the data scarcity issue presented in the current datasets, we propose to extend the training dataset by physical modeling of TCR-peptide pairs. Specifically, we compute the docking energies between auxiliary unknown TCR-peptide pairs as surrogate training labels. Then, we use these extended example-label pairs to train our model in a supervised fashion. Finally, we find that the AUC score for the prediction of the model can be further improved by pseudo-labeling of such unknown TCR-peptide pairs (by a trained teacher model), and re-training the model with those pseudo-labeled TCR-peptide pairs. Our proposed method that trains the deep neural network with physical modeling and data-augmented pseudo-labeling improves over baselines in the available two datasets. We also introduce a new dataset that contains over 80,000 unknown TCR-peptide pairs with docking energy scores.

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