Title: Implicit Neural Representations with Periodic Activation Functions
Date: 2020
Short Title: SIREN
Organization: Stanford University
Journals/Conferences: (NeurIPS)Advances in neural information processing systems
Abstract: 本文利用周期激活函數進行隱式神經表征，稱為正弦表征網絡，其非常適合表示復雜的自然信號。
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Title: Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds
Date: 2022
Short Title: DCC-DIF
Organization: School of Software, BNRist, Tsinghua University
Journals/Conferences: (CVPR)Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Abstract: 本文將局部編碼與可學習的位置向量顯式關聯起來，這些位置向量是連續的、可動態優化的，提高了隱式網絡的表達能力。
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Title: MINER: Multiscale Implicit Neural Representation
Date: 2022
Short Title: MINER
Organization: Rice University
Journals/Conferences: (ECCV)European Conference on Computer Vision
Abstract: 本文的多尺度隱式神經表示(MINER)通過拉普拉斯金字塔進行內部表示，它提供了信號的稀疏多尺度分解，能夠捕捉跨尺度信號的正交部分。
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Title: Acorn: Adaptive Coordinate Networks for Neural Scene Representation
Date: 2022
Short Title: ACORN
Organization: Stanford University
Journals/Conferences: (ECCV)European Conference on Computer Vision
Abstract: 本文引入了一種新的混合隱-顯網絡結構和訓練策略，該策略在訓練和推理過程中根據感興趣信號的局部復雜性自適應分配資源。
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Title: Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Date: 2020
Short Title: FPE
Organization: University of California
Journals/Conferences: (NeurIPS)Advances in neural information processing systems
Abstract: 本文證明，通過一個簡單的傅里葉特征映射來傳遞輸入點，可以使多層感知器(MLP)學習低維問題域中的高頻函數。
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Title: Transformers as Meta-Learners for Implicit Neural Representations
Date: 2022
Short Title: Trans-INR
Organization: UC San Diego
Journals/Conferences: (ECCV)European Conference on Computer Vision
Abstract: 本文使用Transformers作為INR的超網絡，在這里它可以直接構建整個INR權重集，Transformers被專門用作集到集映射。
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