Multi-Task Self-Training for Learning General Representations
This addresses the problem of creating versatile vision models for researchers and practitioners, though it is incremental as it builds on existing self-training and multi-task learning methods.
The paper tackles the challenge of training a single general model for multiple computer vision tasks by introducing multi-task self-training (MuST), which uses specialized teacher models to pseudo-label unlabeled data and trains a student model, achieving performance that outperforms specialized supervised and self-supervised models on large-scale datasets.
Despite the fast progress in training specialized models for various tasks, learning a single general model that works well for many tasks is still challenging for computer vision. Here we introduce multi-task self-training (MuST), which harnesses the knowledge in independent specialized teacher models (e.g., ImageNet model on classification) to train a single general student model. Our approach has three steps. First, we train specialized teachers independently on labeled datasets. We then use the specialized teachers to label an unlabeled dataset to create a multi-task pseudo labeled dataset. Finally, the dataset, which now contains pseudo labels from teacher models trained on different datasets/tasks, is then used to train a student model with multi-task learning. We evaluate the feature representations of the student model on 6 vision tasks including image recognition (classification, detection, segmentation)and 3D geometry estimation (depth and surface normal estimation). MuST is scalable with unlabeled or partially labeled datasets and outperforms both specialized supervised models and self-supervised models when training on large scale datasets. Lastly, we show MuST can improve upon already strong checkpoints trained with billions of examples. The results suggest self-training is a promising direction to aggregate labeled and unlabeled training data for learning general feature representations.