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Found 8 papers, 7 papers with code
Revisiting the Parameter Efficiency of Adapters from the Perspective of Precision Redundancy
Current state-of-the-art results in computer vision depend in part on fine-tuning large pre-trained vision models.
Masked Image Modeling with Local Multi-Scale Reconstruction
The lower layers are not explicitly guided and the interaction among their patches is only used for calculating new activations.
Contrastive Prototypical Network with Wasserstein Confidence Penalty
This results in our CPN (Contrastive Prototypical Network) model, which combines the prototypical loss with pairwise contrast and outperforms the existing models from this paradigm with modestly large batch size.
Rethinking Minimal Sufficient Representation in Contrastive Learning
It significantly improves the performance of several classic contrastive learning models in downstream tasks.
What Makes for Good Representations for Contrastive Learning
Therefore, we assume the task-relevant information that is not shared between views can not be ignored and theoretically prove that the minimal sufficient representation in contrastive learning is not sufficient for the downstream tasks, which causes performance degradation.
Cross-Domain Few-Shot Classification via Adversarial Task Augmentation
However, when there exists the domain shift between the training tasks and the test tasks, the obtained inductive bias fails to generalize across domains, which degrades the performance of the meta-learning models.
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Cross-Domain Few-Shot
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Few-shot Learning with LSSVM Base Learner and Transductive Modules
The performance of meta-learning approaches for few-shot learning generally depends on three aspects: features suitable for comparison, the classifier ( base learner ) suitable for low-data scenarios, and valuable information from the samples to classify.
Fast Structured Decoding for Sequence Models
However, these models assume that the decoding process of each token is conditionally independent of others.
