Disentangled Distilled Encoder Achieves Out-of-Distribution Reasoning with Rademacher Guarantees
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Reasoning about images that differ significantly from those used during training remains a major challenge for artificial intelligence systems, particularly when identifying multiple characteristics within a single image. Zahra Rahiminasab, Michael Yuhas, and Arvind Easwaran, all from Nanyang Technological University, address this problem by developing a new framework that compresses the size of these reasoning systems without sacrificing their accuracy. Their work introduces a disentangled distilled encoder, which streamlines the process of identifying key image characteristics, and crucially, provides mathematical guarantees that this streamlined system maintains its ability to accurately reason about unfamiliar images. This achievement represents a significant step towards deploying sophisticated image analysis tools on devices with limited computational resources, opening up new possibilities for applications in fields such as robotics and mobile computing. Disentangled Learning for Robust OOD Detection This research investigates methods for building robust and efficient neural networks capable of learning disentangled representations and detecting out-of-distribution (OOD) data, crucial for safety-critical applications. Disentangled representations separate underlying factors of variation within data, improving generalization and interpretability, while OOD detection identifies inputs significantly different from the training data. Model compression reduces network size, enabling deployment on resource-constrained devices.
The team developed a disentangled distilled student encoder, a student network trained using distillation from a larger teacher model, alongside adaptation and isolation losses to encourage disentanglement. A significant aspect of the work involves controlling Rademacher complexity (RC), a measure of a model’s ability to fit random noise, which is crucial for generalization. Techniques like singular value clipping and tensor train decomposition were employed to bound the RC. The study demonstrates effective disentanglement through the convergence of adaptation and isolation losses, and controlling Rademacher complexity proves crucial for building models that generalize well and reliably detect OOD inputs. Evaluation on a Jetson Nano demonstrates the potential for deploying these models on resource-constrained devices. This work combines practical implementation with theoretical analysis, providing a solid foundation for future research. Disentangled Distillation for Compact Model Compression This study pioneers a disentangled distilled encoder (DDE) framework designed to compress models for deployment on devices with limited resources while maintaining the ability to reason about out-of-distribution data. Researchers addressed model size by formalizing the compression process as a constrained optimization problem, leveraging student-teacher distillation. This involves training a smaller “student” network to mimic a larger, pre-trained “teacher” network, with constraints to preserve disentanglement in the latent space. To ensure disentanglement, the team incorporated specific constraints into the optimization process, building upon theoretical foundations in Rademacher complexity. This mathematical framework provides guarantees that the learned latent dimensions correspond to meaningful, independent factors of variation within the data. The DDE framework was rigorously tested using the CARLA driving simulator dataset and evaluated on a Jetson Nano. The evaluation process involved careful analysis of the trade-off between model size, performance on in-distribution data, and the ability to accurately reason about out-of-distribution samples. Researchers meticulously assessed the disentanglement of the latent space, verifying that individual latent dimensions capture distinct characteristics of the input images. This demonstrates a significant advancement in model compression techniques, enabling the deployment of sophisticated machine learning models on a wider range of devices. Disentangled Compression for Robust Out-of-Distribution Reasoning This work presents a disentangled distilled encoder (DDE) framework designed to compress models for out-of-distribution (OOD) reasoning, particularly for deployment on resource-constrained devices like the NVIDIA Jetson Nano. The research addresses a critical challenge in safety-critical cyber-physical systems, where incorrect predictions on unfamiliar data can have severe consequences.
The team formalized the training process as a constrained optimization problem, enabling the compression of a larger model into a smaller one while actively preserving disentanglement. A key innovation lies in the Adaptability and Isolation constraints enforced during knowledge distillation. Adaptability ensures that information regarding changes in generative factors is effectively transferred from the teacher to the student model, while Isolation maintains the distinction between representative and unrepresentative latent dimensions for each factor. This approach differs from previous methods, instead utilizing a weakly supervised “match-pairing” technique where only groups of samples sharing the same value for a given factor are needed during training. Theoretical guarantees for the solution’s optimality were established by analyzing parameterization and empirical gaps, and bounding expected loss functions using Rademacher complexity.
The team demonstrated the preservation of OOD performance with a student model trained on the CARLA dataset and successfully evaluated on a Jetson Nano platform. This delivers a practical solution for deploying robust OOD reasoning capabilities in real-world applications with limited computational resources.
Disentangled Distillation Compresses Reasoning Models Effectively This research presents a novel framework, the disentangled distilled encoder (DDE), which successfully reduces the size of out-of-distribution (OOD) reasoning models while maintaining the critical property of disentangled latent spaces.
The team achieved this by formalizing model compression as a constrained optimization problem, ensuring that the resulting smaller models retain meaningful representations of image characteristics. Theoretical guarantees, based on Rademacher complexity, underpin the approach and validate the preservation of disentanglement during the distillation process. Empirical evaluation, conducted using the CARLA dataset and deployed on a Jetson Nano device, demonstrates the effectiveness of DDE in reducing both model size and inference time with minimal performance loss on OOD tasks. Future work will explore additional compression techniques, such as pruning, and investigate the influence of temporal dependencies on defining disentanglement. 👉 More information 🗞 Disentangled and Distilled Encoder for Out-of-Distribution Reasoning with Rademacher Guarantees 🧠 ArXiv: https://arxiv.org/abs/2512.10522 Tags: