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Models Released in 2018
Browse every generative AI model released in 2018 — 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 2017 and models from 2019.
StyleGAN
🇺🇸 NVIDIA
SPN (CelebA HQ)
🇺🇸 Google Brain
SPN (ImageNet 128)
🇺🇸 Google Brain
Vine copula (breast cancer)
🇺🇸 Massachusetts Institute of Technology (MIT)
Vine copula (crime)
🇺🇸 Massachusetts Institute of Technology (MIT)
Vine copula (wine quality)
🇺🇸 Massachusetts Institute of Technology (MIT)
DMPFold
🇨🇳 University College London (UCL)
GPipe (Transformer)
Multi-cell LSTM
🇮🇳 University of Hyderabad
Discriminator-tuned LSTM
🇷🇺 Samsung R&D Institute Russia
Fine-tuned-AWD-LSTM-DOC (fin)
🇷🇺 Samsung R&D Institute Russia
Mesh-TensorFlow Transformer 2.9B (translation)
🇺🇸 Google Brain
Mesh-TensorFlow Transformer 4.9B (language)
🇺🇸 Google Brain
MemoReader
🇰🇷 Samsung
code2vec
🇺🇸 Technion - Israel Institute of Technology
TrellisNet
🇺🇸 Carnegie Mellon University (CMU)
TrellisNet-MoS (1.4x larger) PTB
🇺🇸 Carnegie Mellon University (CMU)
BERT-Large
DeepConPred2
🇨🇳 Tsinghua University
ADP-FAIRSEQ + NGRAMRES
🇨🇳 Nara Institute of Science and Technology
BigGAN-deep 512x512
🇬🇧 Heriot-Watt University
Transformer (Adaptive Input Embeddings) WT103
🇺🇸 Facebook AI Research
LSTM+NeuralCache
🇺🇸 KU Leuven
AWD-LSTM-MoS + dynamic evaluation (PTB, 2018)
🇨🇳 Peking University
AWD-LSTM-MoS + dynamic evaluation (WT2, 2018)
🇨🇳 Peking University
Talent Search and Recommendation Systems
Transformer + Simple Recurrent Unit
🇺🇸 ASAPP
NetSurfP-2.0
🇩🇰 Technical University of Denmark
ESRGAN
🇺🇸 Chinese University of Hong Kong (CUHK)
(ensemble): AWD-LSTM-DOC (fin) × 5 (PTB)
🇯🇵 NTT Communication Science Laboratories
(ensemble): AWD-LSTM-DOC (fin) × 5 (WT2)
🇯🇵 NTT Communication Science Laboratories
AWD-LSTM-DOC (fin) (23M)
🇯🇵 NTT Communication Science Laboratories
AWD-LSTM-DOC (fin) (37M)
🇯🇵 NTT Communication Science Laboratories
Big Transformer for Back-Translation
🇺🇸 Facebook AI Research
AWD-LSTM-MoS+PDR + dynamic evaluation (PTB)
🇺🇸 IBM
AWD-LSTM-MoS+PDR + dynamic evaluation (WT2)
🇺🇸 IBM
RGC+ASQ (PTB)
🇨🇳 Tsinghua University
Dexterous In-Hand Manipulation [control policy]
🇺🇸 OpenAI
Big-Little Net
🇺🇸 IBM
Big-Little Net (speech)
🇺🇸 IBM
Big-Little Net (vision)
🇺🇸 IBM
Glow (Celeba HQ)
🇺🇸 OpenAI
RCAN
🇺🇸 Northeastern University
FTW (For The Win)
🇬🇧 DeepMind
QT-Opt
🇺🇸 Google Brain
S + I-Attention (3)
🇷🇺 National Research University Higher School of Economics
DARTS
🇬🇧 DeepMind
DARTS (second order) (PTB)
🇺🇸 Carnegie Mellon University (CMU)
Relational Memory Core
🇬🇧 DeepMind
GPT-1
🇺🇸 OpenAI
2-layer skip-LSTM + dropout tuning (PTB)
🇬🇧 DeepMind
RHN(depth=40)
🇮🇱 Ben-Gurion University
RHN+HSG(depth=40)
🇮🇱 Ben-Gurion University
aLSTM(depth-2)+RecurrentPolicy (PTB)
🇬🇧 University of Manchester
aLSTM(depth-2)+RecurrentPolicy (WT2)
🇬🇧 University of Manchester
AWD-LSTM-MoS+Noisin+dynamic evaluation (PTB)
🇺🇸 Columbia University
Dropout-LSTM+Noise(Bernoulli) (WT2)
🇺🇸 Columbia University
Dropout-LSTM+Noise(Bernoulli) - large(PTB)
🇺🇸 Columbia University
Dropout-LSTM+Noise(Laplace) - medium (WT2)
🇺🇸 Columbia University
LSTM+Noise(Beta)
🇺🇸 Columbia University
2018 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 2018 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 2017 or AI models from 2019 to compare how capabilities, context windows and open-weights availability evolved year over year.