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Found 12 papers, 3 papers with code
Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation
In Mesh2NeRF, we propose an analytic solution to directly obtain ground-truth radiance fields from 3D meshes, characterizing the density field with an occupancy function featuring a defined surface thickness, and determining view-dependent color through a reflection function considering both the mesh and environment lighting.
Fully-fused Multi-Layer Perceptrons on Intel Data Center GPUs
We compare our approach to a similar CUDA implementation for MLPs and show that our implementation on the Intel Data Center GPU outperforms the CUDA implementation on Nvidia's H100 GPU by a factor up to 2. 84 in inference and 1. 75 in training.
LDM3D-VR: Latent Diffusion Model for 3D VR
Our models are fine-tuned from existing pretrained models on datasets containing panoramic/high-resolution RGB images, depth maps and captions.
A Low-cost Strategic Monitoring Approach for Scalable and Interpretable Error Detection in Deep Neural Networks
We present a highly compact run-time monitoring approach for deep computer vision networks that extracts selected knowledge from only a few (down to merely two) hidden layers, yet can efficiently detect silent data corruption originating from both hardware memory and input faults.
Large-Scale Application of Fault Injection into PyTorch Models -- an Extension to PyTorchFI for Validation Efficiency
Transient or permanent faults in hardware can render the output of Neural Networks (NN) incorrect without user-specific traces of the error, i. e. silent data errors (SDE).
BEA: Revisiting anchor-based object detection DNN using Budding Ensemble Architecture
The proposed loss functions in BEA improve the confidence score calibration and lower the uncertainty error, which results in a better distinction of true and false positives and, eventually, higher accuracy of the object detection models.
Exploring Resiliency to Natural Image Corruptions in Deep Learning using Design Diversity
We compare ensembles created with diverse model architectures trained either independently or through a Neural Architecture Search technique and evaluate the correlation of prediction-based and attribution-based diversity to the final ensemble accuracy.
Evaluation of Confidence-based Ensembling in Deep Learning Image Classification
The key idea is to create successive model experts for samples that were difficult (not necessarily incorrectly classified) by the preceding model.
Reliable Multimodal Trajectory Prediction via Error Aligned Uncertainty Optimization
Reliable uncertainty quantification in deep neural networks is very crucial in safety-critical applications such as automated driving for trustworthy and informed decision-making.
Hardware faults that matter: Understanding and Estimating the safety impact of hardware faults on object detection DNNs
The evaluation of several representative object detection models shows that even a single bit flip can lead to a severe silent data corruption event with potentially critical safety implications, with e. g., up to (much greater than) 100 FPs generated, or up to approx.
Towards a Safety Case for Hardware Fault Tolerance in Convolutional Neural Networks Using Activation Range Supervision
Convolutional neural networks (CNNs) have become an established part of numerous safety-critical computer vision applications, including human robot interactions and automated driving.
A Plausibility-based Fault Detection Method for High-level Fusion Perception Systems
Trustworthy environment perception is the fundamental basis for the safe deployment of automated agents such as self-driving vehicles or intelligent robots.
