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fine-tuning/summary.txt

+- Data examples: colorized ILSVRC and spatial ILSVRC. Different visual domains but same target task
+
+- setting = only have a pretrained model on data X, and limited amound of annotated samples from data Y
+- baseline = fine-tuning later layers, keeping the early one frozen
+- proposed: "projecting" data Y to data X at the feature level
+
+- fine-tuning 
+  require a layer criterion selection = analyze accuracy when replacing early layers by ones finetuned on the other dataset
+  select the "turning-point layer" as the one with the highest drop in accurcy
+  ~ similar to criterion in How "transferable are features in deep neural networks?", Yosinski et al.
+- projection module: image-to-image module. Mainly useful for parametriwed transformations 
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