2 papers
cs.LG2026
Continual Learning in Modern Hopfield Networks with an Application to Diffusion Models
Ken Takeda, Masafumi Oizumi, Ryo Karakida
Generative models, including diffusion models, are increasingly used as foundation models and adapted through sequential fine-tuning, making continual learning an essential problem…
cs.CV2025
Investigating Fine- and Coarse-grained Structural Correspondences Between Deep Neural Networks and Human Object Image Similarity Judgments Using Unsupervised Alignment
Soh Takahashi, Masaru Sasaki, Ken Takeda +1
The learning mechanisms by which humans acquire internal representations of objects are not fully understood. Deep neural networks (DNNs) have emerged as a useful tool for investig…