Awesome Transformer Architecture Search:

To keep track of the large number of recent papers that look at the intersection of Transformers and Neural Architecture Search (NAS), we have created this awesome list of curated papers and resources, inspired by awesome-autodl, awesome-architecture-search, and awesome-computer-vision. Papers are divided into the following categories:

General Transformer search
Domain Specific, applied Transformer search (divided into NLP, Vision, ASR)
Insights on Transformer components or searchable parameters
Transformer Surveys

This repository is maintained by the AutoML Group Freiburg. Please feel free to pull requests or open an issue to add papers.

General Transformer Search

Title	Venue	Group
UniNet: Unified Architecture Search with Convolutions, Transformer and MLP	arxiv [Oct'21]	SenseTime
Analyzing and Mitigating Interference in Neural Architecture Search	arxiv [Aug'21]	Tsinghua, MSR
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search	ICCV'21	Sun Yat-sen University
Memory-Efficient Differentiable Transformer Architecture Search	ACL-IJCNLP'21	MSR, Peking University
Finding Fast Transformers: One-Shot Neural Architecture Search by Component Composition	arxiv [Aug'20]	Google Research
AutoTrans: Automating Transformer Design via Reinforced Architecture Search	arxiv [Sep'20]	Fudan University
NAT: Neural Architecture Transformer for Accurate and Compact Architectures	NeurIPS'19	Tencent AI
The Evolved Transformer	ICML'19	Google Brain

Domain Specific Transformer Search

Vision

Title	Venue	Group
AutoFormer: Searching Transformers for Visual Recognition	ICCV'21	MSR
GLiT: Neural Architecture Search for Global and Local Image Transformer	ICCV'21	University of Sydney
Searching for Efficient Multi-Stage Vision Transformers	ICCV'21 workshop	MIT
HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers	CVPR'21	Bytedance Inc.
Vision Transformer Architecture Search	arxiv [June'21]	SenseTime, Tsingua University

Natural Language Processing

Title	Venue	Group
AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models	ACL'21	MIT
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search	KDD'21	MSR, Tsinghua University
AutoBERT-Zero: Evolving the BERT backbone from scratch	arxiv [July'21]	Huawei Noah’s Ark Lab
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing	ACL'20	MIT

Automatic Speech Recognition

Title	Venue	Group
LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search	ICASSP'21	MSR
Darts-Conformer: Towards Efficient Gradient-Based Neural Architecture Search For End-to-End ASR	arxiv [Aug'21]	NPU, Xi'an
Improved Conformer-based End-to-End Speech Recognition Using Neural Architecture Search	arxiv [April'21]	Chinese Academy of Sciences
Evolved Speech-Transformer: Applying Neural Architecture Search to End-to-End Automatic Speech Recognition	INTERSPEECH'20	VUNO Inc.

Insights on Transformer components and interesting papers

Title	Venue	Group
Patches are All You Need ?	ICLR'22 under review	-
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows	ICCV'21 best paper	MSR
Rethinking Spatial Dimensions of Vision Transformers	ICCV'21	NAVER AI
What makes for hierarchical vision transformers	arxiv [Sept'21]	HUST
AutoAttend: Automated Attention Representation Search	ICML'21	Tsinghua University
Rethinking Attention with Performers	ICLR'21 Oral	Google
LambdaNetworks: Modeling long-range Interactions without Attention	ICLR'21	Google Research
HyperGrid Transformers	ICLR'21	Google Research
LocalViT: Bringing Locality to Vision Transformers	arxiv [April'21]	ETH Zurich
NASABN: A Neural Architecture Search Framework for Attention-Based Networks	IJCNN'20	Chinese Academy of Sciences
Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned	ACL'19	Yandex

Transformer Surveys

Title	Venue	Group
Transformers in Vision: A Survey	arxiv [Oct'21]	MBZ University of AI
Efficient Transformers: A Survey	arxiv [Sept'21]	Google Research

Misc resources

Awesome Visual Transformer

A curated list of awesome resources combining Transformers with Neural Architecture Search

Related tags

Overview

Awesome Transformer Architecture Search:

General Transformer Search

Domain Specific Transformer Search

Vision

Natural Language Processing

Automatic Speech Recognition

Insights on Transformer components and interesting papers

Transformer Surveys

Misc resources

Owner

Yash Mehta

This repository contains datasets and baselines for benchmarking Chinese text recognition.

Implementation of ResMLP, an all MLP solution to image classification, in Pytorch

Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. In CVPR 2022.

TRIQ implementation

An experiment to bait a generalized frontrunning MEV bot

Minimalistic PyTorch training loop

Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition (AGRA, ACM 2020, Oral)

Deep Learning GPU Training System

[IJCAI'21] Deep Automatic Natural Image Matting

PyTorch implementation of Histogram Layers from DeepHist: Differentiable Joint and Color Histogram Layers for Image-to-Image Translation

Remote sensing change detection tool based on PaddlePaddle

A tensorflow model that predicts if the image is of a cat or of a dog.

Explainability for Vision Transformers (in PyTorch)

This is the official repository of the paper Stocastic bandits with groups of similar arms (NeurIPS 2021). It contains the code that was used to compute the figures and experiments of the paper.

PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Implementation of average- and worst-case robust flatness measures for adversarial training.

Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression

Convert scikit-learn models to PyTorch modules

Barlow Twins and HSIC

Scalable implementation of Lee / Mykland (2012) and Ait-Sahalia / Jacod (2012) Jump tests for noisy high frequency data