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hi iamp;#39;m sasha sax and iamp;#39;ll be introducing side tuning a baseline for network adaptation via additive side networks often instead of training from scratch weamp;#39;d like to adapt some existing network to a new task for example taking a service normal estimator and using it to do semantic segmentation we might want to do this either because of a lack of training data on a new task or to use some previously learned weights or representations as they useful prior approaches for network adaptation typically fall into two main categories freeze the weights and add parameters with the simplest approach being fixed features or update the parameters with the simplest approach being fine-tuning these two examples have markedly different characteristics and practice as fixed features donamp;#39;t get updated during training any information discarded about the features never gets recovered and this limits final performance if you only have a few samples this limited final perfor
