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Found 2 papers, 2 papers with code
Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting
Artificial neural networks often suffer from catastrophic forgetting, where learning new concepts leads to a complete loss of previously acquired knowledge.
A New Dataset Based on Images Taken by Blind People for Testing the Robustness of Image Classification Models Trained for ImageNet Categories
Our goal is to improve upon the status quo for designing image classification models trained in one domain that perform well on images from another domain.
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