{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dank-learning-generating-memes-using-deep","title":"Dank Learning: Generating Memes Using Deep Neural Networks","arxiv_id":"1806.04510","date":"2018-06-08","proceeding":null,"authors":["Abel L Peirson V","E Meltem Tolunay"],"abstract":"We introduce a novel meme generation system, which given any image can\nproduce a humorous and relevant caption. Furthermore, the system can be\nconditioned on not only an image but also a user-defined label relating to the\nmeme template, giving a handle to the user on meme content. The system uses a\npretrained Inception-v3 network to return an image embedding which is passed to\nan attention-based deep-layer LSTM model producing the caption - inspired by\nthe widely recognised Show and Tell Model. We implement a modified beam search\nto encourage diversity in the captions. We evaluate the quality of our model\nusing perplexity and human assessment on both the quality of memes generated\nand whether they can be differentiated from real ones. Our model produces\noriginal memes that cannot on the whole be differentiated from real ones.","url_abs":"http://arxiv.org/abs/1806.04510v1","url_pdf":"http://arxiv.org/pdf/1806.04510v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dank-learning-generating-memes-using-deep","repo_url":"https://github.com/alpv95/MemeProject","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"dank-learning-generating-memes-using-deep","repo_url":"https://github.com/alpv95/Dank-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"dank-learning-generating-memes-using-deep","repo_url":"https://github.com/gbotev1/cgmfpim","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"auxiliary-classifier","method_name":"Auxiliary Classifier"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"inception-v3","method_name":"Inception-v3"},{"method_slug":"inception-v3-module","method_name":"Inception-v3 Module"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"rmsprop","method_name":"RMSProp"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.04510","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}