text: 4.1 Predicting Representations versus Pixels 
  
  We first ablate the effect of computing the prediction 
  loss in representation space. We train a pair of ViT-L/16 
  models using either a V-JEPA feature prediction loss, 
  or a mean-squared error loss with the normalized pixel 
  values, as in masked autoencoders (He et al., 2021), and 
  perform a sweep over the learning rate and weight decay 
  schedules for both approaches. All models are pretrained 
  on VideoMix2M for 90K iterations with a batch size of 
  3072 using multi-block masking. We examine perfor- 
  mance on Kinetics-400 (K400), Something-Something-v2 
  (SSv2), and ImageNet-1K (IN1K), using a frozen back- 
  bone with an attentive probe, and report top-1 accuracy 
  using a single center view. We also examine end-to-end 
  fine-tuning performance of the models on Kinetics-400. 
  
  Results of this comparison are reported in Table 1 and 
  indicate that predicting in feature space provides a con- 
  sistent performance improvement over pixel space pre- 
  diction in both frozen evaluation of the video backbone, 
  as well as end-to-end fine-tuning. 