deep learning for computer vision previously trained networkrunning the new data through itand training new classifier on top of the output (see figure prediction prediction prediction trained classifier trained classifier new classifier (randomly initializedtrained convolutional base trained convolutional base trained convolutional base (frozeninput input input figure swapping classifiers while keeping the same convolut ional base why only reuse the convolutional basecould you reuse the densely connected classifier as wellin generaldoing so should be avoided the reason is that the representations learned by the convolutional base are likely to be more generic and therefore more reusablethe feature maps of convnet are presence maps of generic concepts over picturewhich is likely to be useful regardless of the computer-vision problem at hand but the representations learned by the classifier will necessarily be specific to the set of classes on which the model was trained--they will only contain information about the presence probability of this or that class in the entire picture additionallyrepresentations found in densely connected layers no longer contain any information about where objects are located in the input imagethese layers get rid of the notion of spacewhereas the object location is still described by convolutional feature maps for problems where object location mattersdensely connected features are largely useless note that the level of generality (and therefore reusabilityof the representations extracted by specific convolution layers depends on the depth of the layer in the model layers that come earlier in the model extract localhighly generic feature maps (such as visual edgescolorsand textures)whereas layers that are higher up extract more-abstract concepts (such as "cat earor "dog eye"so if your new dataset differs lot from the dataset on which the original model was trainedyou may be better off using only the first few layers of the model to do feature extractionrather than using the entire convolutional base licensed to