- V-JEPA 2 is Meta’s self-supervised video encoder, trained without labels to predict masked - spatio-temporal regions. We want to open up its latent space and understand how it reacts to the - visual world — and, more interestingly, to things that don’t belong in it. Starting from a frozen - pretrained encoder, we build an interactive demo that embeds short clips and surfaces structure, - similarity, and drift over time. On top of this, we explore anomaly detection as a concrete - application: can the embedding space tell a banana from a pair of scissors in a rock-paper-scissors - game, a boat on a highway, or an abnormal beat in an ECG recording? + Can V-JEPA learn meaningful representations for ECG-based arrhythmia detection? In this project, we investigate whether a V-JEPA encoder, originally designed for video understanding, can capture clinically relevant patterns from ECG signals. We transform ECG recordings into video-like inputs and use the pretrained V-JEPA encoder to generate latent representations. These representations are then evaluated by training a range of downstream predictors for arrhythmia classification. By comparing performance across predictors, we assess the quality and transferability of V-JEPA features for cardiac signal analysis.