Research map / 3D vision / Depth and stereo
Trusted Multi-View Deep Learning with Opinion Aggregation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use opinion aggregation with evidence accumulation instead of TMC fusion | Trusted multi-view deep learning · CUB | accuracy (%): 91.00±2.36 -> 95.43±0.20 | Helped |
| Use opinion aggregation with evidence accumulation instead of TMC fusion | Trusted multi-view deep learning · HMDB | accuracy (%): 65.98±2.92 -> 88.20±0.58 | Helped |
| Use opinion aggregation with evidence accumulation instead of DUA-Nets | Trusted multi-view deep learning · Scene15 | accuracy (%): 68.23±0.11 -> 75.57±0.02 | Helped |
| Use opinion aggregation with evidence accumulation instead of DUA-Nets | Trusted multi-view deep learning · Caltech101 | accuracy (%): 93.83±0.34 -> 94.63±0.04 | Helped |
| Use opinion aggregation with evidence accumulation instead of TMC | Trusted multi-view deep learning · Handwritten | accuracy (%): 98.51±0.13 -> 99.75±0.00 | Helped |
| Use opinion aggregation with evidence accumulation instead of TMC | Trusted multi-view deep learning · Food-101 | accuracy (%): 90.21±1.20 -> 93.75±0.32 | Helped |
Closest papers by meaning
- Navigating Conflicting Views: Harnessing Trust for Learning (ICML 2025 · similarity 0.86)
- Reliable Conflictive Multi-View Learning (AAAI 2024 · similarity 0.85)
- Enhancing Testing-Time Robustness for Trusted Multi-View Classification in the Wild (CVPR 2025 · similarity 0.84)
- Enhancing Multi-View Classification Reliability with Adaptive Rejection (AAAI 2025 · similarity 0.84)
- Deep Fuzzy Multi-view Learning for Reliable Classification (ICML 2025 · similarity 0.84)
- Trusted Unified Feature-Neighborhood Dynamics for Multi-View Classification (AAAI 2025 · similarity 0.83)
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