Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff

This paper addresses the tradeoff between standard accuracy on clean examples and robustness against adversarial examples in deep neural networks (DNNs). Although adversarial training (AT) improves robustness, it degrades the standard accuracy, thus yielding the tradeoff. To mitigate this tradeoff, we propose a novel AT method called ARREST, which comprises three components: (i) adversarial finetuning (AFT), (ii) representation-guided knowledge distillation (RGKD), and (iii) noisy replay (NR). AFT trains a DNN on adversarial examples by initializing its parameters with a DNN that is standardly pretrained on clean examples. RGKD and NR respectively entail a regularization term and an algorithm to preserve latent representations of clean examples during AFT. RGKD penalizes the distance between the representations of the standardly pretrained and AFT DNNs. NR switches input adversarial examples to nonadversarial ones when the representation changes significantly during AFT. By combining these components, ARREST achieves both high standard accuracy and robustness. Experimental results demonstrate that ARREST mitigates the tradeoff more effectively than previous AT-based methods do.Comment: Accepted by International Conference on Computer Vision (ICCV) 202

Efectos de la edad y el sexo sobre la memoria espacial de ratas Wistar en el laberinto radial de 8 brazos

Trabajo de InvestigaciónEl presente estudio tuvo como objetivo evaluar el desempeño de 24 ratas Wistar en una tarea de memoria espacial, según las características de sexo y edad (ratas jóvenes y ratas adultas). Para este fin, se llevó a cabo una fase inicial de habituación de 10 minuto diarios en el laberinto radial de Olton, y una fase de entrenamiento de una tarea de memoria espacial durante 27 sesiones.INTRODUCCIÓN Y ASPECTOS GENERALES 1. RESUMEN 2. JUSTIFICACIÓN 3. MARCO TEÓRICO 4. MÉTODO 5. RESULTADOS 6. DISCUSIÓN Y CONCLUSIONES BIBLIOGRAFÍA ANEXOSPregradoPsicólog

Detection of base travel groups with different sensitivities to new high-speed rail services: Non-negative tensor decomposition approach

金沢大学理工研究域地球社会基盤学系How many base travel groups (models) are necessary for clarifying the long-term day-to-day dynamics of intercity travel? In the past, several travel purposes (e.g., sightseeing, business, etc.) have been assumed. However, mobile-phone location data enables us to answer the above question because of their detailed time-series information. In this study, we propose a method for deriving the basic travel groups necessary for clarifying the time-series changes by applying nonnegative tensor factorization (NTF). This method is applied to the time-series data of several long-distance travelers to the Ishikawa prefecture, to where the Hokuriku High-speed rail (HSR) has been newly extended. Based on this, the number of base travel groups necessary for predicting the effect of the new HSR is estimated as twelve, which is greater than the number used in the previous demand forecasting models. The estimated groups include components that appear to correspond to different travel purposes (e.g., sightseeing, business, and homecoming), as in previous surveys. These results confirm that the methodology proposed in this study can clearly extract groups with different elasticities, due to the traffic service. The HSR effect can be clarified by dividing it into several characteristics and detailed components. In addition, if multiple HSR effects are analyzed, a more accurate demand-forecasting model for the new HSR service can be proposed.Embargo Period 12 month