text: Slow Features. One way to encourage temporally 
  adjacent representations to be predictive of each other 
  is to ensure that they vary slowly over time. Early 
  works targeting predictive features encouraged represen- 
  tations of individual video frames to be locally tempo- 
  rally invariant, while preventing representation collapse 
  by using spectral methods, as in SFA (Wiskott and Se- 
  jnowski, 2002), SSA (Kayser et al., 2001), and Simulated 
  Fixations (Zou et al., 2012). More recently, Goroshin 
  et al. (2015); Wang et al. (2010) train a siamese con- 
  volutional network to map the representations of two 
  subsequent frames to the same point, while encouraging 
  distant frames to have diverse representations via a pair- 
  wise margin loss and a triplet loss, respectively. Other 
  works (Oord et al., 2018; Surís et al., 2021; Feichtenhofer 
  et al., 2021) implement temporal invariance using noise- 
  contrastive estimation (Gutmann and Hyvärinen, 2012). 
  Our exploration in this paper goes beyond temporal in- 