Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
NucNormZSL: điều chỉnh miền dựa trên chuẩn hạt nhân trong học không có dữ liệu
Tóm tắt
Khả năng của con người trong việc nhận diện các khái niệm mới đã thu hút sự chú ý đáng kể từ cộng đồng nghiên cứu. Học không có dữ liệu, còn được gọi là học không có mẫu, tìm cách xây dựng các mô hình có thể nhận diện các trường hợp lớp mới ngay cả khi không "nhìn thấy" chúng trong quá trình huấn luyện; tuy nhiên, một số mô tả về các lớp mới là cần thiết. Trong công trình này, chúng tôi đặt vấn đề học không có mẫu như một bài toán học từ điển để tìm các hàm chiếu từ không gian đặc trưng sang không gian ngữ nghĩa như những từ điển trong các miền nguồn và đích. Để có được một ánh xạ chiếu mạnh mẽ trong miền nguồn, chúng tôi giới thiệu chuẩn hạt nhân để đạt được các giải pháp bậc thấp. Hơn nữa, từ điển có bậc thấp này được sử dụng như một phương pháp điều chỉnh trong miền đích để mà kiến thức chứa trong từ điển nguồn có thể được sử dụng trong miền đích. Trong các thí nghiệm của chúng tôi, miền nguồn chứa các hình ảnh của lớp đã thấy, các giá trị thực tế của chúng và các đại diện thuộc tính, trong khi dữ liệu tương ứng cho lớp chưa thấy được chứa trong miền đích. Chúng tôi cũng sử dụng sự lan truyền nhãn như một sự thay thế cho tìm kiếm hàng xóm gần nhất trong không gian ngữ nghĩa để gán nhãn lớp. Mô hình mà chúng tôi đề xuất, NucNormZSL, đạt được kết quả tốt nhất trong các bài kiểm tra trên bộ dữ liệu Các thuộc tính lớn (LAD) và duy trì sự cạnh tranh khá với các phương pháp hiện có trên bộ dữ liệu Động vật với Các thuộc tính-2 (AWA2), Chim Caltech-UCSD (CUB) và SUN trong cài đặt thông thường và cài đặt tổng quát.
Từ khóa
#học không có dữ liệu #chuẩn hạt nhân #học máy #điều chỉnh miền #ánh xạ chiếuTài liệu tham khảo
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