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Register a new userEfficient Content-Based Sparse Attention with Routing Transformers
الاهتمام المستند إلى المحتوى المستند إلى المحتوى مع محولات التوجيه
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تم اعتماد اهتمام الذات مؤخرا لمجموعة واسعة من مشاكل النمذجة التسلسلية. على الرغم من فعاليته، فإن اهتمام الذات يعاني من حساب التربيعي ومتطلبات الذاكرة فيما يتعلق بطول التسلسل. تركز النهج الناجحة للحد من هذا التعقيد على حضور النوافذ المنزلق المحلية أو مجموعة صغيرة من المواقع مستقلة عن المحتوى. يقترح عملنا تعلم أنماط الانتباه ديناميكية متناثرة تتجنب تخصيص الحساب والذاكرة لحضور المحتوى غير المرتبط باستعلام الفائدة. يبني هذا العمل على سطرين من الأبحاث: فهو يجمع بين مرونة النمذجة للعمل المسبق على اهتمام متمرد للمحتوى مع مكاسب الكفاءة من الأساليب القائمة على الاهتمام المحلي والزموني المتناثر. نموذجنا، محول التوجيه، ينفذ عن النفس مع وحدة توجيه متناثرة تعتمد على الوسائل K عبر الإنترنت مع تقليل التعقيد العام للانتباه إلى O (N1.5D) من O (N2D) لطول التسلسل N وبعد المخفي D. نظرا لأن نموذجنا يتفوق على نماذج انتباه متناثرة قابلة للمقارنة على نمذجة اللغة على Wikitext-103 (15.8 مقابل 18.3 حيرة)، وكذلك على جيل الصورة على Imagenet-64 (3.43 مقابل 3.44 بت / خافت) أثناء استخدام طبقات أقل من الاهتمام الذاتي. بالإضافة إلى ذلك، وضعنا مجموعة جديدة من مجموعة جديدة من مجموعة بيانات PG-19 التي تم إصدارها حديثا، والحصول على اختبار حيرة من 33.2 مع نموذج محول توجيه 22 طبقة مدرب على تسلسل الطول 8192. نحن نفتح المصدر لتحويل التوجيه في Tensorflow.1
Self-attention has recently been adopted for a wide range of sequence modeling problems. Despite its effectiveness, self-attention suffers from quadratic computation and memory requirements with respect to sequence length. Successful approaches to reduce this complexity focused on attending to local sliding windows or a small set of locations independent of content. Our work proposes to learn dynamic sparse attention patterns that avoid allocating computation and memory to attend to content unrelated to the query of interest. This work builds upon two lines of research: It combines the modeling flexibility of prior work on content-based sparse attention with the efficiency gains from approaches based on local, temporal sparse attention. Our model, the Routing Transformer, endows self-attention with a sparse routing module based on online k-means while reducing the overall complexity of attention to O(n1.5d) from O(n2d) for sequence length n and hidden dimension d. We show that our model outperforms comparable sparse attention models on language modeling on Wikitext-103 (15.8 vs 18.3 perplexity), as well as on image generation on ImageNet-64 (3.43 vs 3.44 bits/dim) while using fewer self-attention layers. Additionally, we set a new state-of-the-art on the newly released PG-19 data-set, obtaining a test perplexity of 33.2 with a 22 layer Routing Transformer model trained on sequences of length 8192. We open-source the code for Routing Transformer in Tensorflow.1
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هدفنا من خلال هذه الدراسة في إطار المشروع الفصلي للسنة الرابعة إلى إلقاء الضوء على استرجاع الصور من مجموعة كبيرة بالاعتماد على محتوى صورة هدف , و قمنا بتدعيم هذه الدراسة بتطبيق ضمن بيئة الماتلاب لبرنامج بحث عن الصور المشابهة لصورة مدخلة .
و قد تركز
بحثنا على ميزتين هامتين يكاد لا يخلو منها أي نظام بحث عن الصور بالاعتماد على المحتوى و هما ميزتي الهيستوغرام اللوني و بنية الصورة texture , ووضحنا الخطوات التي يتم في ضوئها عملية الاسترجاع بدءاً من تحليل الصورة و استخلاص شعاع الواصفات الخاص فيها , و مطابقته مع أشعة الميزات الخاصة بالصور الموجودة في قاعدة البيانات ليتم ترتيب الصور بحسب مدى تشابهها من الصورة الهدف .
و تطرقت الدراسة إلى استخدام الفضاء اللوني HMMD كبديل للفضاء اللوني RGB لاستخراج واصفات البنية اللونية على اعتبار أنه نموذج لوني موجه بالمستخدم user oriented و بالتالي نضمن أن نحصل على نتائج أفضل ترضي المستخدم .
وقمنا بتدعيم الدراسة بعدد من الأشكال و الأمثلة و المخططات التي توضح محتوى الدراسة النظرية و ما قمنا بعمله في التطبيق ضمن بيئة الماتلاب .
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