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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.
PixelSNAIL (CIFAR 10)
🇺🇸 University of California (UC) Berkeley
PixelSNAIL (ImageNet)
🇺🇸 University of California (UC) Berkeley
Tacotron 2
WGAN (Wasserstein GAN)
🇺🇸 Facebook AI Research
2-layer-LSTM+Deep-Gradient-Compression
🇨🇳 Tsinghua University
AlphaZero
🇬🇧 DeepMind
PNASNet-5
🇺🇸 Johns Hopkins University
DL scaling Image
🇨🇳 Baidu
DL scaling LM
🇨🇳 Baidu
DL scaling speech
🇨🇳 Baidu
TriNet
🇩🇪 Visual Computing Institute
AWD-LSTM-MoS + dynamic evaluation (PTB, 2017)
🇺🇸 Carnegie Mellon University (CMU)
AWD-LSTM-MoS + dynamic evaluation (WT2, 2017)
🇺🇸 Carnegie Mellon University (CMU)
VQ-VAE
🇬🇧 DeepMind
DCN+
🇺🇸 Salesforce Research
Fraternal dropout + AWD-LSTM 3-layer (PTB)
🇺🇸 University of Montreal / Université de Montréal
Fraternal dropout + AWD-LSTM 3-layer (WT2)
🇺🇸 Jagiellonian University
S-Norm
🇺🇸 University of Washington
PhraseCond
🇺🇸 Carnegie Mellon University (CMU)
ProgressiveGAN
🇺🇸 NVIDIA
LRSO-GAN
🇨🇳 University of Technology Sydney
AlphaGo Master
🇬🇧 DeepMind
AlphaGo Zero
🇬🇧 DeepMind
Rainbow DQN
🇬🇧 DeepMind
AWD-LSTM+WT+Cache+IOG (PTB)
🇯🇵 NTT Communication Science Laboratories
AWD-LSTM+WT+Cache+IOG (WT2)
🇯🇵 NTT Communication Science Laboratories
AWD-LSTM + dynamic eval (PTB)
🇺🇸 University of Edinburgh
AWD-LSTM + dynamic eval (WT2)
🇺🇸 University of Edinburgh
LSTM + dynamic eval
🇺🇸 University of Edinburgh
ISS
🇺🇸 Duke University
PyramidNet
🇰🇷 Korea Advanced Institute of Science and Technology (KAIST)
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (PTB)
🇮🇱 Ben-Gurion University
GL-LWGC-AWD-MoS-LSTM + dynamic evaluation (WT2)
🇺🇸 Ben-Gurion University of the Negev
D-LSRC(100)+KN5 (PTB)
🇺🇸 Saarland University
GRU + p-tHSM (pretrain via Brown) (PTB)
🇨🇳 Beihang University
GRU + p-tHSM (pretrain via Brown) (WT2)
🇨🇳 Beihang University
Libratus
🇺🇸 Carnegie Mellon University (CMU)
Adversarial Joint Adaptation Network (ResNet)
🇨🇳 Tsinghua University
NeuMF (Pinterest)
🇨🇳 Shandong University
Cutout-regularized net
🇨🇦 University of Guelph
EI-REHN-1000D
🇰🇷 Korea Advanced Institute of Science and Technology (KAIST)
EI-REHN-1200D (PTB)
🇰🇷 Korea Advanced Institute of Science and Technology (KAIST)
OpenAI TI7 DOTA 1v1
🇺🇸 OpenAI
AWD-LSTM - 3-layer LSTM (tied) + continuous cache pointer (PTB)
🇺🇸 Salesforce Research
AWD-LSTM - 3-layer LSTM (tied) + continuous cache pointer (WT2)
🇺🇸 Salesforce Research
RetinaNet-R101
🇺🇸 Facebook AI Research
GSM
🇨🇳 Peking University
ConvS2S (ensemble of 8 models)
🇺🇸 Meta AI
PSPNet
🇺🇸 Chinese University of Hong Kong (CUHK)
4 layer Densely Connected LSTM 14M (PTB)
🇧🇪 Ghent University
Densely Connected LSTM + Var. Dropout
🇧🇪 Ghent University
AWD-LSTM
🇬🇧 DeepMind
JFT
🇺🇸 Google Research
DeepLoc
🇩🇰 Technical University of Denmark
NoisyNet-Dueling
🇬🇧 DeepMind
DeepLabV3
HRA
🇺🇸 Maluuba
Transformer
🇺🇸 Google Research
EDSR
🇺🇸 Seoul National University
Reading Twice for NLU
🇬🇧 DeepMind
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.