Georgia Tech researchers present ZipIt: a general method for merging two arbitrary models of the same architecture that incorporates two simple strategies

The computer vision discipline has flourished under the dominance of huge models with ever-increasing numbers of parameters since AlexNet popularized deep learning. Challenges in today’s benchmarks include classification with tens of thousands of classes, precise object identification, rapid instance segmentation, realistic image production, and many other vision problems originally thought to be impossible or extremely difficult. These deep models are quite effective, but they have a potentially fatal flaw: they can only do the job they’ve been trained to do. They encounter several possible problems while trying to increase the capabilities of an existing model. They risk catastrophic forgetfulness if they try to train the model on a different assignment.

They often find that the same model does not generalize to samples outside the domain when they examine it using different data without fitting. To reduce these consequences, they can try so-called intervention tactics, although these sometimes require further training, which can be costly. A ton of finely finished templates are already available for many businesses. Despite the fact that these models often have the same basic structural foundation, there is currently no technique for combining models developed for distinct goals. Either they were forced to assemble them, which involves evaluating each model separately, or they were forced to jointly train a new model through distillation, both of which can be prohibitively expensive, especially given the current trend of constantly increasing the architecture and size of datasets.

Instead, researchers at the Georgia Institute of Technology thought it would be wonderful if they could simply zip these models together, eliminating the need for further training and allowing any duplicate features to be calculated only once. In the vision community, the concept of integrating several models into one is just starting to gain popularity. To increase accuracy and resiliency, Model Soups can incorporate several models that have been tuned using the same pre-trained initialization. With a large loss of precision, Git Re-Basin further generalizes to models trained on the same data but with different initializations. By including additional parameters and, where necessary, modifying the template batch policies, REPAIR enhances Git Re-Basin.

All these techniques, meanwhile, only merge models created for the same goal. This study pushes this line of research to its logical conclusion by integrating models with various initializations that have been developed for very different purposes. Although this is a really difficult problem, they use two simple methods to solve it. They begin by noting that previous research has focused on permutating one pattern into the other when combining them. Assuming most features between the two models are redundant, this results in a 1:1 mapping between them. They cannot rely on permutation alone, as this is not always true for models trained on various tasks. Instead, they make use of redundant parts of each model.

They generalize model merging to allow any combination of features to be squeezed both within and between each model to achieve this. On some datasets, they find that this alone increases accuracy by up to 20% over Git Re-basin plus a more robust permutation baseline that they implement. Secondly, current techniques combine the entire network. This can work for models that are quite similar and trained in the same environment, but as a network ages, the properties of models trained on different tasks become less related. They introduce partial zip, where they only close up to a certain level, to solve this problem. They then automatically create a multiheaded model by feeding the intermediate outputs of the merged model to the remaining unmerged layers of the original networks.

This can increase accuracy by over 15% while still keeping most levels merged, depending on how challenging each assignment is. They introduce ZipIt!, a universal technique to compress any number of models trained on various tasks into a single multitasking model without further training by combining both of these approaches. They can combine models with the same architecture, merge functionality within each model, and partially compress them to form a multi-task model by devising a generic graph-based technique for joining and unjoining. By integrating models trained on completely different datasets, completely distinct sets of CIFAR and ImageNet categories, they demonstrate the effectiveness of their method far surpassing previous research. Then they analyze and excise the performance of their methods in various cases. They have described their pipeline elaborately in the GitHub repo. The code and datasets were also made available.

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