Various embodiments of the present disclosure include systems, methods, and non-transitory computer storage media configured to identify a set of training content items, each of the set of training content items comprising video content. A category may be assigned to each of the set of training content items. A plurality of variations may be provided to the each of the set of training content items. A first content recognition module may be trained in an unsupervised process to associate the plurality of variations of the each of the set of training content items with the category assigned to the each of the set of training content items. A classification layer may be generated based on the training the first content recognition module in the unsupervised process. A second content recognition module may be trained in a supervised process based on the classification layer.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method comprising: identifying, by a computing system, a set of training content items, each of the set of training content items comprising video content; assigning, by the computing system, a category to each of the set of training content items; providing, by the computing system, a plurality of variations to the each of the set of training content items; training, by the computing system, a first content recognition module in an unsupervised process to associate the plurality of variations of the each of the set of training content items with the category assigned to the each of the set of training content items; generating, by the computing system, a classification layer based on the training the first content recognition module in the unsupervised process; and training, by the computing system, a second content recognition module in a supervised process based on the classification layer. 2. The method of claim 1, wherein the first content recognition module comprises a first convolutional neural network, and the second content recognition module comprises a second convolutional neural network. 3. The method of claim 1, wherein the plurality of variations comprises a variation of an object in the video content. 4. The method of claim 1, wherein training the content recognition module in the supervised process comprises performing a backpropagation on the second content recognition module based on the classification layer. 5. The method of claim 1, wherein training the content recognition module in the supervised process comprises associating each of the set of training content items with a semantic sequence corresponding to the category assigned to the each of the set of training content items. 6. The method of claim 1, wherein the plurality of variations comprises at least one geometric variation of the each of the set of training content items. 7. The method of claim 1, wherein the plurality of variations comprises at least one of a rotation, a translation, a rescaling, a color change, a geometric modification, or a filtering of the each of the set of training content items. 8. The method of claim 1, wherein the plurality of variations comprises a variation of a perspective, lighting, or motion of an object. 9. The method of claim 1, further comprising using the second content recognition module to classify a set of evaluation content items. 10. The method of claim 9, wherein the set of evaluation content items corresponds to content items uploaded by users of a social networking system. 11. A system comprising: at least one processor; a memory storing instructions configured to instruct the at least one processor to perform: identifying, by a computing system, a set of training content items, each of the set of training content items comprising video content; assigning, by the computing system, a category to each of the set of training content items; providing, by the computing system, a plurality of variations to the each of the set of training content items; training, by the computing system, a first content recognition module in an unsupervised process to associate the plurality of variations of the each of the set of training content items with the category assigned to the each of the set of training content items; generating, by the computing system, a classification layer based on the training the first content recognition module in the unsupervised process; and training, by the computing system, a second content recognition module in a supervised process based on the classification layer. 12. The system of claim 11, wherein the first content recognition module comprises a first convolutional neural network, and the second content recognition module comprises a second convolutional neural network. 13. The system of claim 11, wherein the plurality of variations comprises a variation of an object in the video content. 14. The system of claim 11, wherein training the content recognition module in the supervised process comprises performing a backpropagation on the second content recognition module based on the classification layer. 15. The system of claim 11, wherein training the content recognition module in the supervised process comprises associating each of the set of training content items with a semantic sequence corresponding to the category assigned to the each of the set of training content items. 16. A computer storage medium storing computer-executable instructions that, when executed, cause a computer system to perform a computer-implemented method comprising: identifying, by a computing system, a set of training content items, each of the set of training content items comprising video content; assigning, by the computing system, a category to each of the set of training content items; providing, by the computing system, a plurality of variations to the each of the set of training content items; training, by the computing system, a first content recognition module in an unsupervised process to associate the plurality of variations of the each of the set of training content items with the category assigned to the each of the set of training content items; generating, by the computing system, a classification layer based on the training the first content recognition module in the unsupervised process; and training, by the computing system, a second content recognition module in a supervised process based on the classification layer. 17. The computer storage medium of claim 16, wherein the first content recognition module comprises a first convolutional neural network, and the second content recognition module comprises a second convolutional neural network. 18. The computer storage medium of claim 16, wherein the plurality of variations comprises a variation of an object in the video content. 19. The computer storage medium of claim 16, wherein training the content recognition module in the supervised process comprises performing a backpropagation on the second content recognition module based on the classification layer. 20. The computer storage medium of claim 16, wherein training the content recognition module in the supervised process comprises associating each of the set of training content items with a semantic sequence corresponding to the category assigned to the each of the set of training content items.