The relative rankings of continual learning methods are not preserved across different fine-tuning regimes defined by trainable parameter depth.
arXiv preprint arXiv:1902.07208 , year=
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GEAR aligns Ricci curvatures in latent spaces of models to create a unified transfer learning architecture, achieving 14.4% and 8.3% performance gains on 23 molecular task pairs under random and scaffold splits.
Adding register tokens to Vision Transformers eliminates high-norm background artifacts and raises state-of-the-art performance on dense visual prediction tasks.
VTAB is a 19-task benchmark that measures representation quality by few-shot adaptation performance across diverse vision domains, with a controlled large-scale comparison of popular pretraining methods.
citing papers explorer
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Fine-Tuning Regimes Define Distinct Continual Learning Problems
The relative rankings of continual learning methods are not preserved across different fine-tuning regimes defined by trainable parameter depth.
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Geometric Embedding Alignment via Curvature Matching in Transfer Learning
GEAR aligns Ricci curvatures in latent spaces of models to create a unified transfer learning architecture, achieving 14.4% and 8.3% performance gains on 23 molecular task pairs under random and scaffold splits.
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Vision Transformers Need Registers
Adding register tokens to Vision Transformers eliminates high-norm background artifacts and raises state-of-the-art performance on dense visual prediction tasks.
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A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
VTAB is a 19-task benchmark that measures representation quality by few-shot adaptation performance across diverse vision domains, with a controlled large-scale comparison of popular pretraining methods.