Separating signal from noise: a self-distillation approach for amortized heterogeneous cryo-EM reconstruction

Cryogenic electron microscopy (cryo-EM) is a powerful method for visualizing heterogeneous biomolecular structures at high resolution, but the growing scale of cryo-EM datasets creates substantial computational challenges for 3D reconstruction. Autoencoder-based heterogeneous reconstruction methods offer a potential solution through amortized inference: once trained, an encoder could infer the conformations of new particle images with a single forward pass. In practice, however, encoders of existing methods overfit to the extremely noisy images used for training and fail to generalize to unseen particles. Here, we introduce Cryo-No-Overfit (CryoNOO), a training paradigm for learning noise-invariant representations that generalize to unseen cryo-EM images. CryoNOO leverages the reconstruction model itself as a denoiser, reconstructing and reprojecting training particles to generate denoised views that are augmented with independent noise realizations for self-supervised learning. Across synthetic and experimental datasets, we show that CryoNOO enables large-scale amortized reconstruction from a subset of particles, generalizes to conformations absent during training, and supports accurate conformation inference on unseen particles.