3 papers
cs.CV2026
Underrepresented in Foundation Model Pretraining Data? A One-Shot Probe
Chris Vorster, Mayug Maniparambil, Noel E. O'Connor +2
Large-scale Vision-Language Foundation Models (VLFMs), such as CLIP, now underpin a wide range of computer vision research and applications. VLFMs are often adapted to various doma…
cs.CV2026
Hold-One-Shot-Out (HOSO) for Validation-Free Few-Shot CLIP Adapters
Chris Vorster, Mayug Maniparambil, Noel E. O'Connor +2
In many CLIP adaptation methods, a blending ratio hyperparameter controls the trade-off between general pretrained CLIP knowledge and the limited, dataset-specific supervision from…
cs.LG2025
Llamazip: Leveraging LLaMA for Lossless Text Compression and Training Dataset Detection
Sören Dréano, Derek Molloy, Noel Murphy
This work introduces Llamazip, a novel lossless text compression algorithm based on the predictive capabilities of the LLaMA3 language model. Llamazip achieves significant data red…