4 papers
Natural Attribute-based Shift Detection
Jeonghoon Park, Jimin Hong, Radhika Dua +4
Despite the impressive performance of deep networks in vision, language, and healthcare, unpredictable behaviors on samples from the distribution different than the training distri…
Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation
Sanghun Jung, Jungsoo Lee, Daehoon Gwak +2
Identifying unexpected objects on roads in semantic segmentation (e.g., identifying dogs on roads) is crucial in safety-critical applications. Existing approaches use images of une…
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment
Jeonghoon Park, Kyungmin Jo, Daehoon Gwak +3
We evaluate the out-of-distribution (OOD) detection performance of self-supervised learning (SSL) techniques with a new evaluation framework. Unlike the previous evaluation methods…
Neural Ordinary Differential Equations for Intervention Modeling
Daehoon Gwak, Gyuhyeon Sim, Michael Poli +3
By interpreting the forward dynamics of the latent representation of neural networks as an ordinary differential equation, Neural Ordinary Differential Equation (Neural ODE) emerge…