To answer this question, we begin by revisiting the forward procedure of ViTs. A sequence of positional embeddings (PEs) [51] is added to patch embeddings to preserve position information. Intuitively, simply discarding these PEs and requesting the model to reconstruct the position for each patch naturally becomes a qualified location-aware pretext task.

CARP learns prototypes in an end-to-end online fashion using gradient descent without additional non-differentiable modules to solve the cluster assignment problem. CARP optimizes a new pretext task based on random partitions of prototypes that regularizes the model and enforces consistency between views' assignments.

Self-supervised learning (SSL) aims to learn rich and meaningful representations without relying onhumanannotations.

Semisupervised learning uses the trained encoder in learning a predictive model on both labeled and unlabeleddata. Figure 3: The proposed data corruption procedure. Original feature matrix(X) consists of four samples xi,i = 1...,4, where each row/column represents a sample/feature, and the features in each sample are represented by the same color. In the experiment section of the main manuscript, we evaluate VIME and its benchmarks on 11 datasets(6genomics,2clinical,and3publicdatasets). The selected SNPs and the corresponding blood cell trait together form an independent labeled dataset.