The Governor Architecture: Avoiding Catastrophic Forgetting in Robot Learning

The governor architecture is a new method for avoiding catatrophic forgetting in neural networks that is particularly useful in online robot learn- ing. The governor architecture uses a categorizer to identify events and excise long sequences of repetitive data that cause catastrophic forgetting in neural networks trained on robot-based tasks. We examine the performance of several variations of the governor architecture on a number of re- lated localization tasks using a simulated robot. The results show that governed networks perform far better than ungoverned networks. Governored networks are able to reliably and robustly prevent catastrophic forgetting in robot learning tasks

The Governor Architecture: Avoiding Catastrophic Forgetting in Robot Learning

The governor architecture is a new method for avoiding catatrophic forgetting in neural networks that is particularly useful in online robot learn- ing. The governor architecture uses a categorizer to identify events and excise long sequences of repetitive data that cause catastrophic forgetting in neural networks trained on robot-based tasks. We examine the performance of several variations of the governor architecture on a number of re- lated localization tasks using a simulated robot. The results show that governed networks perform far better than ungoverned networks. Governored networks are able to reliably and robustly prevent catastrophic forgetting in robot learning tasks

PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision Transformers

Data-free quantization can potentially address data privacy and security concerns in model compression, and thus has been widely investigated. Recently, PSAQ-ViT designs a relative value metric, patch similarity, to generate data from pre-trained vision transformers (ViTs), achieving the first attempt at data-free quantization for ViTs. In this paper, we propose PSAQ-ViT V2, a more accurate and general data-free quantization framework for ViTs, built on top of PSAQ-ViT. More specifically, following the patch similarity metric in PSAQ-ViT, we introduce an adaptive teacher-student strategy, which facilitates the constant cyclic evolution of the generated samples and the quantized model (student) in a competitive and interactive fashion under the supervision of the full-precision model (teacher), thus significantly improving the accuracy of the quantized model. Moreover, without the auxiliary category guidance, we employ the task- and model-independent prior information, making the general-purpose scheme compatible with a broad range of vision tasks and models. Extensive experiments are conducted on various models on image classification, object detection, and semantic segmentation tasks, and PSAQ-ViT V2, with the naive quantization strategy and without access to real-world data, consistently achieves competitive results, showing potential as a powerful baseline on data-free quantization for ViTs. For instance, with Swin-S as the (backbone) model, 8-bit quantization reaches 82.13 top-1 accuracy on ImageNet, 50.9 box AP and 44.1 mask AP on COCO, and 47.2 mIoU on ADE20K. We hope that accurate and general PSAQ-ViT V2 can serve as a potential and practice solution in real-world applications involving sensitive data. Code is released and merged at: https://github.com/zkkli/PSAQ-ViT.Comment: Accepted by TNNLS 202

Context Aided Tracking with Adaptive Hyperspectral Imagery

A methodology for the context-aided tracking of ground vehicles in remote airborne imagery is developed in which a background model is inferred from hyperspectral imagery. The materials comprising the background of a scene are remotely identified and lead to this model. Two model formation processes are developed: a manual method, and method that exploits an emerging adaptive, multiple-object-spectrometer instrument. A semi-automated background modeling approach is shown to arrive at a reasonable background model with minimal operator intervention. A novel, adaptive, and autonomous approach uses a new type of adaptive hyperspectral sensor, and converges to a 66% correct background model in 5% the time of the baseline {a 95% reduction in sensor acquisition time. A multiple-hypothesis-tracker is incorporated, which utilizes background statistics to form track costs and associated track maintenance thresholds. The context-aided system is demonstrated in a high- fidelity tracking testbed, and reduces track identity error by 30%

Interactive and life-long learning for identification and categorization tasks

Abstract (engl.) This thesis focuses on life-long and interactive learning for recognition tasks. To achieve these targets the separation into a short-term memory (STM) and a long-term memory (LTM) is proposed. For the incremental build up of the STM a similarity-based one-shot learning method was developed. Furthermore two consolidation algorithms were proposed enabling the incremental learning of LTM representations. Based on the Learning Vector Quantization (LVQ) network architecture an error-based node insertion rule and a node dependent learning rate are proposed to enable life-long learning. For learning of categories additionally a forward-feature selection method was introduced to separate co-occurring categories. In experiments the performance of these learning methods could be shown for difficult visual recognition problems

Measurements, Models, Systems and Design

531 s.