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(shù)進(jìn)行隱式神經(jīng)表征，稱為正弦表征網(wǎng)絡(luò)，其非常適合表示復(fù)雜的自然信號(hào)。
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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(xué)習(xí)的位置向量顯式關(guān)聯(lián)起來，這些位置向量是連續(xù)的、可動(dòng)態(tài)優(yōu)化的，提高了隱式網(wǎng)絡(luò)的表達(dá)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(jīng)表示(MINER)通過拉普拉斯金字塔進(jìn)行內(nèi)部表示，它提供了信號(hào)的稀疏多尺度分解，能夠捕捉跨尺度信號(hào)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(wǎng)絡(luò)結(jié)構(gòu)和訓(xùn)練策略，該策略在訓(xùn)練和推理過程中根據(jù)感興趣信號(hào)的局部復(fù)雜性自適應(yīng)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(gè)簡單的傅里葉特征映射來傳遞輸入點(diǎn)，可以使多層感知器(MLP)學(xué)習(xí)低維問題域中的高頻函數(shù)。
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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(wǎng)絡(luò)，在這里它可以直接構(gòu)建整個(gè)INR權(quán)重集，Transformers被專門用作集到集映射。
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