Transferring Deep Learning Technology to Industry

Abstract

Deep learning has enabled the wide-spread deployment of computer vision beyond laboratory settings. Consequently, computer vision has become the main bridge for intelligent systems observing the real, physical world. Yet, a fundamental barrier persists: the substantial volume of precisely labeled training data required for effective supervised learning. Especially in industrial settings, where public datasets rarely match domain-specific requirements, the combined effort of data capture, cleansing, and annotation can account for up to 80% of project costs, constraining deployment agility and accessibility. This data hunger manifests distinctly across tasks. For example, semantic segmentation can demand pixel-perfect masks, while anomaly detection often requires extensive normal examples and, in some cases, defective examples, which are rare by nature. Some failure cases might even be entirely unknown while the inspection pipeline is being built. This dissertation develops and evaluates methodologies that reduce dependency on extensively labeled datasets. The central research objective is to demonstrate that strategic approaches, leveraging sparse supervision, pre-trained multimodal knowledge, and transferable embeddings, can achieve competitive performance while substantially reducing annotation requirements, thereby making deep learning more practical for resource-constrained deployment. Three empirical investigations address complementary aspects of data efficiency. First, a “Lazy Labels” technique enables U-Net architectures to learn semantic segmentation from sparse point or scribble annotations by zero-masking unlabeled pixels during loss computation, requiring modifications only to label tensor handling. Second, Multimodal Large Language Models (MLLMs), including GPT-4V and Qwen-VL-Chat, are evaluated for visual anomaly detection through a training-free Visual Question Answering (VQA) framework, using natural language prompts with zero to a few reference images. Third, a comparative study contrasts pre-trained foundation model embeddings (MegaDescriptor) against task-specific training for visual re-identification, examining both frozen and fine-tuned transfer learning under standard and one-shot conditions. The sparse segmentation experiments demonstrate that models trained with point annotations covering ≈1% of pixels achieve performance within 1− −3 F1-score percentage points of fully supervised baselines, while producing spatially coherent dense predictions. For anomaly detection, GPT-4V achieves F1 = 0.92 in a one-shot configuration on MVTec AD without task-specific training, approaching specialized detector performance. In re-identification, fine-tuned pre-trained embeddings reach 91.6% top-1 accuracy under full supervision and notably outperform from-scratch training in data-limited one-shot scenarios, validating their superior data efficiency. The findings demonstrate that introducing prior structure, through strategic sparse supervision, pre-trained multimodal knowledge, or transferable representations, consistently compensates for limited labeled data across diverse vision tasks. While each approach operates within specific constraints, including sparse label distribution, sensitivity to foundational capabilities, and domain similarity, they collectively demonstrate viable pathways for reducing the data bottleneck. The work contributes observations and validated techniques for practitioners: zero-masking for direct sparse supervision, prompt-based MLLM deployment for rapid baselines, and transfer learning strategies optimized for data scarcity. These methodologies advance the practical deployment of computer vision in contexts where extensive annotation remains prohibitive.