Research map / Detection and segmentation / Open-set and few-shot detection
Robust Learning Through Cross-Task Consistency
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add cross-task consistency constraints to standard supervised learning | UNet prediction networks · Replica | Normals perceptual error via depth: 4.69 -> 2.07 | Helped |
| Add cross-task consistency constraints to standard supervised learning | UNet prediction networks · Taskonomy test | Normals perceptual error via depth: 8.17 -> 4.32 | Helped |
| Add cross-task consistency constraints to standard supervised learning | UNet prediction networks · Taskonomy test | Depth direct ℓ1 error: 2.27 -> 2.29 | Hurt |
| Train with cross-task consistency constraints | Prediction networks · Taskonomy, Gaussian blur, pre-adaptation | Error: 53.0 -> 46.2 (+12.8%) | Helped |
| Train with cross-task consistency constraints | Prediction networks · CocoDoom, pre-adaptation | Error: 64.5 -> 54.3 (+15.8%) | Helped |
| Train with cross-task consistency constraints | Prediction networks · ApolloScape, pre-adaptation | Error: 59.1 -> 55.8 (+5.5%) | Helped |
| Use Consistency Energy as the out-of-distribution score instead of OC-NN | Out-of-distribution image detection · Taskonomy, ApolloScape, and CocoDoom | ROC-AUC: 0.51 -> 0.95 | Helped |
| Measure prediction reliability using Consistency Energy | Taskonomy test predictions · Taskonomy test | Pearson correlation between energy and supervised error: 0.67 | Helped |
| Enforce consistency explicitly rather than relying on shared representations alone | Multi-task prediction networks · Taskonomy test | Consistency Energy | Helped |
| Use consistency constraints when training with 0.25% of the data | Prediction networks · Replica and Taskonomy, 0.25% data | Perceptual errors | Helped |
Closest papers by meaning
- Test-Time Canonicalization by Foundation Models for Robust Perception (ICML 2025 · similarity 0.85)
- Quantifying Uncertainty in Error Consistency: Towards Reliable Behavioral Comparison of Classifiers (NeurIPS 2025 · similarity 0.84)
- Improved Training Technique for Latent Consistency Models (ICLR 2025 · similarity 0.84)
- RCP-Bench: Benchmarking Robustness for Collaborative Perception Under Diverse Corruptions (CVPR 2025 · similarity 0.84)
- Directional Confusions Reveal Divergent Inductive Biases Through Rate-Distortion Geometry in Human and Machine Vision (NeurIPS 2026 · similarity 0.84)
- Improved Techniques for Training Consistency Models (ICLR 2024 · similarity 0.84)
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