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Found 6 papers, 3 papers with code
Mitigating the Linguistic Gap with Phonemic Representations for Robust Multilingual Language Understanding
Approaches to improving multilingual language understanding often require multiple languages during the training phase, rely on complicated training techniques, and -- importantly -- struggle with significant performance gaps between high-resource and low-resource languages.
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.
BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning
In this work, we propose black-box visual prompting (BlackVIP), which efficiently adapts the PTMs without knowledge about model architectures and parameters.
Graph Perceiver IO: A General Architecture for Graph Structured Data
A graph has an adjacency matrix different from other dataset domains such as text and image, and it is not trivial to handle the topological information, relational information, and canonical positional information.
Learning Fair Representation via Distributional Contrastive Disentanglement
We provide a new type of contrastive loss motivated by Gaussian and Student-t kernels for distributional contrastive learning with theoretical analysis.
Geodesic Multi-Modal Mixup for Robust Fine-Tuning
Such a lack of alignment and uniformity might restrict the transferability and robustness of embeddings.
