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Models Released in 2017
Browse every generative AI model released in 2017 — large language models, image generators, code models and more — ordered by release date. Compare their specs and rankings on our benchmarks page, or put any two side by side with compare. See also models from 2016 and models from 2018.
PointNet++
🇺🇸 Stanford University
Inflated 3D ConvNet
🇬🇧 DeepMind
SRGAN
Low-Cost Collaborative Network
🇨🇳 National University of Singapore
Mnemonic Reader
🇨🇳 Fudan University
DeepLab (2017)
🇺🇸 Johns Hopkins University
Tacotron
WGAN-GP
🇺🇸 Courant Institute of Mathematical Sciences
Mask R-CNN
🇺🇸 Facebook AI Research
AlexNet + coordinating filters
🇺🇸 University of Pittsburgh
Prototypical networks
🇺🇸 University of Toronto
Variational Lossy Autoencoder (VLAE) MNIST
🇺🇸 University of California (UC) Berkeley
SEST
🇺🇸 Carnegie Mellon University (CMU)
DnCNN
🇨🇳 Harbin Institute of Technology
VDCNN (on Amazon Review Full dataset)
🇺🇸 Facebook AI Research
MoE-Multi
🇺🇸 Jagiellonian University
PixelCNN++
🇺🇸 OpenAI
OR-WideResNet
🇺🇸 Duke University
DeepStack
🇺🇸 University of Alberta
2017 was another fast-moving year for generative AI. The models listed above span large language models (LLMs), image and video generators, code models and more, each with its own parameter count, context window, licensing and availability. To understand how the models of 2017 actually perform, compare their scores on our benchmarks page, and use compare to evaluate any two releases side by side.
Tracking AI by release year makes it easy to see how the frontier moves. Browse AI models from 2016 or AI models from 2018 to compare how capabilities, context windows and open-weights availability evolved year over year.