New: connect Claude & other AIs to GenAIList over MCP — research the catalog and contribute to the shared knowledge base. Learn how →
Models Released in 2016
Browse every generative AI model released in 2016 — 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 2015 and models from 2017.
EnhanceNet
🇨🇳 Max Planck Institute for Intelligent Systems
GCNN-14
🇺🇸 Facebook AI Research
GCRN-M1, dropout
🇨🇭 Ecole Polytechnique F´ed´erale de Lausanne (EPFL)
3DMM-CNN
🇺🇸 University of Southern California
Diabetic Retinopathy Detection Net
🇺🇸 UT Austin
HR-ResNet101
🇺🇸 Carnegie Mellon University (CMU)
LSTM (PTB)
🇺🇸 Facebook AI Research
LSTM (WT103)
🇺🇸 Facebook AI Research
LSTM (WT2)
🇺🇸 Facebook AI Research
Neural cache model (size=2000)
🇺🇸 Facebook AI Research
GAN-Advancer
🇺🇸 OpenAI
Layer-Norm Fast Weights RNN
🇺🇸 University of Toronto
Elastic weight consolidation
🇬🇧 DeepMind
PointNet
🇺🇸 Stanford University
Image-to-image cGAN
🇺🇸 University of California (UC) Berkeley
RefineNet
🇦🇺 University of Adelaide
PolyNet
🇺🇸 Chinese University of Hong Kong (CUHK)
DAC-CSR
🇨🇳 Jiangnan University
ResNeXt-101 (64×4d)
🇺🇸 University of California San Diego
ResNeXt-50
🇺🇸 University of California San Diego
Deeply-recursive ConvNet
🇺🇸 Seoul National University
DTN (Domain Transfer Network)
🇺🇸 Facebook AI Research
DLDL (PASCAL)
🇺🇸 University of Oxford
BIDAF
🇺🇸 University of Washington
NAS with base 8 and shared embeddings
🇺🇸 Google Brain
NASv3 (CIFAR-10)
🇺🇸 Google Brain
VD-LSTM+REAL Large
🇺🇸 Salesforce Research
VD-LSTM+REAL Medium
🇺🇸 Stanford University
VD-LSTM+REAL Small
🇺🇸 Stanford University
SPIDER2
🇨🇳 Griffith University
Differentiable neural computer
🇺🇸 Google DeepMind
GAWWN
🇺🇸 University of Michigan
Xception
GNMT
Pointer Sentinel-LSTM (WT2)
🇺🇸 MetaMind Inc
Pointer Sentinel-LSTM (medium)
🇺🇸 MetaMind Inc
Zoneout + Variational LSTM (PTB)
🇺🇸 MetaMind Inc
Zoneout + Variational LSTM (WT2)
🇺🇸 MetaMind Inc
Knowledge distillation student model
🇺🇸 Harvard University
Wide Residual Network
🇫🇷 Université Paris-Est
MS-CNN
🇺🇸 IBM
ResNet-200
🇺🇸 Microsoft Research Asia
Stacked hourglass network
🇺🇸 University of Michigan
TSN
🇨🇭 ETH Zurich
Youtube recommendation model
MS-ensemble-speech-recognition
🇺🇸 Microsoft
WaveNet
🇺🇸 Google DeepMind
LF-MMI
🇺🇸 Johns Hopkins University
Multi-task Cascaded CNN
🇨🇳 Chinese Academy of Sciences
DenseNet-264
🇨🇳 Tsinghua University
SimpleNet
🇫🇷 Sensifai
Attend-Infer-Repeat
🇺🇸 Google DeepMind
Layer Normalization: Draw
🇺🇸 University of Toronto
Layer Normalization: Handwriting sequence generation
🇺🇸 University of Toronto
Layer Normalization: Skip Thoughts
🇺🇸 University of Toronto
Layer Normalization: The Attentive Reader
🇺🇸 University of Toronto
Order embeddings with layer norm
🇺🇸 University of Toronto
Character-enriched word2vec
🇺🇸 Facebook AI Research
VD-RHN
🇨🇭 ETH Zurich
Variational RHN + WT (PTB)
🇨🇭 ETH Zurich
2016 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 2016 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 2015 or AI models from 2017 to compare how capabilities, context windows and open-weights availability evolved year over year.