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Found 9 papers, 2 papers with code
Robust Preference Optimization with Provable Noise Tolerance for LLMs
The preference alignment aims to enable large language models (LLMs) to generate responses that conform to human values, which is essential for developing general AI systems.
INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection
Knowledge hallucination have raised widespread concerns for the security and reliability of deployed LLMs.
Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection
Most existing OOD detection methods focused on exploring advanced training skills or training-free tricks to prevent the model from yielding overconfident confidence score for unknown samples.
Uncertainty-aware Unsupervised Multi-Object Tracking
The resulting accurate pseudo-tracklets boost learning the feature consistency.
Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection
The likelihood maps generated by the SLV module are used to supervise the feature learning of the backbone network, encouraging the network to attend to wider and more diverse areas of the image.
Dynamic Supervisor for Cross-dataset Object Detection
The application of cross-dataset training in object detection tasks is complicated because the inconsistency in the category range across datasets transforms fully supervised learning into semi-supervised learning.
SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection
In this paper, we propose a spatial likelihood voting (SLV) module to converge the proposal localizing process without any bounding box annotations.
HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation
In particular, our proposed HoMM can perform arbitrary-order moment tensor matching, we show that the first-order HoMM is equivalent to Maximum Mean Discrepancy (MMD) and the second-order HoMM is equivalent to Correlation Alignment (CORAL).
Towards Self-similarity Consistency and Feature Discrimination for Unsupervised Domain Adaptation
Recent advances in unsupervised domain adaptation mainly focus on learning shared representations by global distribution alignment without considering class information across domains.
