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Efficient fine-tuning methods are critical to address the high computational and parameter complexity while adapting large pre-trained models to downstream tasks.
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Bernardino Romera-Paredes, Hane Aung, Nadia Bianchi-Berthouze, and Massimiliano Pontil · 2013
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Karen Simonyan and Andrew Zisserman · 2015
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Mastering the game of go with deep neural networks and tree search
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Pixel recurrent neural networks
Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Convolutional neural networks analyzed via convolutional sparse coding
Vardan Papyan, Yaniv Romano, and Michael Elad · 2017
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Neural discrete representation learning
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
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Image transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran · 2018
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Rethinking imagenet pre-training
Kaiming He, Ross Girshick, and Piotr Dollár · 2019
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Parameter-efficient transfer learning for nlp
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Learning filter basis for convolutional neural network compression
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Continual lifelong learning with neural networks: A review
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Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aaron Van den Oord, and Oriol Vinyals · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Scalable and order-robust continual learning with additive parameter decomposition
Jaehong Yoon, Saehoon Kim, Eunho Yang, and Sung Ju Hwang · 2019
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A large-scale study of representation learning with the visual task adaptation benchmark
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, et al · 2019
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Krona: Parameter efficient tuning with kronecker adapter
Ali Edalati, Marzieh Tahaei, Ivan Kobyzev, Vahid Partovi Nia, James J Clark, and Mehdi Rezagholizadeh · 2022
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The vendi score: A diversity evaluation metric for machine learning
Dan Friedman and Adji Bousso Dieng · 2022
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Cmt: Convolutional neural networks meet vision transformers
Jianyuan Guo, Kai Han, Han Wu, Yehui Tang, Xinghao Chen, Yunhe Wang, and Chang Xu · 2022
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Visual prompt tuning
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Scaling & shifting your features: A new baseline for efficient model tuning
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Language models are few-shot learners
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Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
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Inner product-based neural network similarity
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Svdiff: Compact parameter space for diffusion fine-tuning
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Multi-concept customization of text-to-image diffusion
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Dinov2: Learning robust visual features without supervision
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Visual prompt tuning for generative transfer learning
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Llama: Open and efficient foundation language models
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Efficient Adaptation of Deep Vision Models
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Object detection in 20 years: A survey
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