Search Results for author:
Found 4 papers, 2 papers with code
Towards Calibrated Robust Fine-Tuning of Vision-Language Models
Robust fine-tuning aims to ensure performance on out-of-distribution (OOD) samples, which is sometimes compromised by pursuing adaptation on in-distribution (ID) samples.
PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibration
Document-level relation extraction (DocRE) aims to extract relations of all entity pairs in a document.
TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation
In this paper, we identify that CBN and TBN are in a trade-off relationship and present a new test-time normalization (TTN) method that interpolates the statistics by adjusting the importance between CBN and TBN according to the domain-shift sensitivity of each BN layer.
AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain
During the fine-tuning phase of transfer learning, the pretrained vocabulary remains unchanged, while model parameters are updated.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
