TransBoost: Improving the Best ImageNet Performance using Deep Transduction Supplementary Material

Department of Computer Science Department of Computer Science Technion - Israel Institute of Technology Technion - Israel Institute of Technology omer.be@cs.technion.ac.il guy.b@cs.technion.ac.il In general TransBoost is particularly useful when we are able to accumulate a test set of instances and then finetune a specialized model to predict their labels. This setting has numerous use cases in various application fields including: Medicine Medical diagnosis is one possible meaningful use case. In this case, medical records can be gathered on a daily or weekly basis. TransBoost can then be used to finetune transductive models on top of existing inductive models in order to provide more reliable results for these specific records.