Tag: NeurIPS 2023
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FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning
Dipam Goswami, Yuyang Liu, Bartłomiej Twardowski, Joost van de Weijer Read Full Paper → Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting. Recent approaches to incrementally learning the classifier by freezing the feature extractor after the first task have gained much attention. In […]
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Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing
Kai Wang, Fei Yang, Shiqi Yang, Muhammad Atif Butt, Joost van de Weijer Read Full Paper → Large-scale text-to-image generative models have been a ground-breaking development in generative AI, with diffusion models showing their astounding ability to synthesize convincing images following an input text prompt. The goal of image editing research is to give users control over the generated […]