Hyunchul Shim

Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity

We introduce MoNet, a novel functionally modular network for self-supervised and interpretable end-to-end learning. By leveraging its functional modularity with a latent-guided contrastive loss function, MoNet efficiently learns task-specific decision-making processes in latent space without requiri…