49 citations · 51 across the 9 of their papers we have counts for
9 papers
Advancing credibility and transparency in brain-to-image reconstruction research: Reanalysis of Koide-Majima, Nishimoto, and Majima (Neural Networks, 2024)
Ken Shirakawa, Yoshihiro Nagano, Misato Tanaka +2
A recent high-profile study by Koide-Majima et al. (2024) claimed a major advance in reconstructing visual imagery from brain activity using a novel variant of a generative AI-base…
Readout Representation: Redefining Neural Codes by Input Recovery
Shunsuke Onoo, Yoshihiro Nagano, Yukiyasu Kamitani
Sensory representation is typically understood through a hierarchical-causal framework where progressively abstract features are extracted sequentially. However, this causal view f…
Overcoming Output Dimension Collapse: When Sparsity Enables Zero-shot Brain-to-Image Reconstruction at Small Data Scales
Kenya Otsuka, Yoshihiro Nagano, Yukiyasu Kamitani
Advances in brain-to-image reconstruction are enabling us to externalize the subjective visual experiences encoded in the brain as images. A key challenge in this task is data scar…
Exploring internal representation of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects
Asaki Kataoka, Yoshihiro Nagano, Masafumi Oizumi
Recent advances in self-supervised learning have attracted significant attention from both machine learning and neuroscience. This is primarily because self-supervised methods do n…
Spurious reconstruction from brain activity
Ken Shirakawa, Yoshihiro Nagano, Misato Tanaka +4
Advances in brain decoding, particularly visual image reconstruction, have sparked discussions about the societal implications and ethical considerations of neurotechnology. As the…
On the Surrogate Gap between Contrastive and Supervised Losses
Han Bao, Yoshihiro Nagano, Kento Nozawa
Contrastive representation learning encourages data representation to make semantically similar pairs closer than randomly drawn negative samples, which has been successful in vari…
Statistical Mechanical Analysis of Catastrophic Forgetting in Continual Learning with Teacher and Student Networks
Haruka Asanuma, Shiro Takagi, Yoshihiro Nagano +3
When a computational system continuously learns from an ever-changing environment, it rapidly forgets its past experiences. This phenomenon is called catastrophic forgetting. While…
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning
Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita +1
Hyperbolic space is a geometry that is known to be well-suited for representation learning of data with an underlying hierarchical structure. In this paper, we present a novel hype…
Concept Formation and Dynamics of Repeated Inference in Deep Generative Models
Yoshihiro Nagano, Ryo Karakida, Masato Okada
Deep generative models are reported to be useful in broad applications including image generation. Repeated inference between data space and latent space in these models can denois…