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Papers that changed AI, with a plain-language explainer for each.
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- 2024
SAM 2: Segment Anything in Images and Videos
arXiv
SAM 2 is a Meta FAIR foundation model that segments objects in images and videos from simple prompts such as points, boxes or masks, released together with a large video segmentation dataset and training code.
- 2024
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
ICML
Tri Dao and Albert Gu's paper introduces state space duality, a framework showing that structured state space models and attention variants are closely related, and uses it to derive Mamba-2, whose core layer runs 2 to 8 times faster than Mamba's.
- 2023
Segment Anything
arXiv 2304.02643
A 2023 paper that introduces the Segment Anything project: a promptable segmentation task, a model, and a dataset of over 1 billion masks on 11 million images, all released to advance foundation models for computer vision.
- 2022
Scalable Diffusion Models with Transformers (DiT)
arXiv
A 2022 paper by William Peebles and Saining Xie that replaces the U-Net backbone of diffusion models with a transformer operating on latent patches, showing that image quality improves predictably as compute grows and reaching a state-of-the-art FID of 2.27 on ImageNet 256×256.
- 2021
Learning Transferable Visual Models From Natural Language Supervision (CLIP)
ICML
CLIP is an OpenAI model trained on 400 million image-text pairs to map pictures and sentences into one shared space, so it can classify images zero-shot from a written description.
- 2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)
arXiv
A 2020 paper showing that a plain Transformer applied directly to sequences of image patches, with no convolution at all, can match state-of-the-art convolutional networks on image classification.
- 2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
arXiv 2010.11929 (ICLR 2021)
A 2020 paper by Alexey Dosovitskiy and colleagues showing that a pure Transformer applied directly to sequences of image patches — the Vision Transformer, or ViT — matches state-of-the-art convolutional networks on image classification when pre-trained on large amounts of data.
- 2020
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
arXiv
A 2020 paper by Mildenhall, Srinivasan, Tancik, Barron, Ramamoorthi and Ng that represents a scene as a neural radiance field: a network mapping 5D coordinates to density and colour, optimized from a sparse set of images with known camera poses.
- 2019
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (T5)
JMLR
A 2019 Google paper that frames every language task as text in, text out, using one encoder-decoder Transformer and one training setup. It also introduced the C4 dataset, and the released T5 models became common research starting points.
- 2018
A Style-Based Generator Architecture for Generative Adversarial Networks
arXiv
This 2018 paper introduces a style-based GAN generator that separates high-level attributes from stochastic variation and allows scale-specific control, improving distribution quality, interpolation, and disentanglement.
- 2018
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
NAACL
Google's 2018 BERT paper introduced an encoder-only Transformer that reads text in both directions and is pre-trained by hiding words. It set new results on eleven language tasks and made download-and-fine-tune the default workflow.
- 2018
Large Scale GAN Training for High Fidelity Natural Image Synthesis
arXiv
A 2018 paper by Andrew Brock, Jeff Donahue and Karen Simonyan that trained generative adversarial networks at the largest scale attempted at the time, introduced BigGANs and set a new state of the art in class-conditional image synthesis.
- 2018
Improving Language Understanding by Generative Pre-Training (GPT)
OpenAI
OpenAI's 2018 paper introducing GPT, a Transformer decoder pre-trained to predict the next token and then fine-tuned for specific tasks. It improved the state of the art on 9 of 12 tasks studied and set the recipe the GPT series followed.
- 2017
Progressive Growing of GANs for Improved Quality, Stability, and Variation
arXiv
A 2017 paper by Karras, Aila, Laine and Lehtinen that trains generative adversarial networks by growing the generator and discriminator from low to high resolution, improving stability, quality and variation.
- 2017
Focal Loss for Dense Object Detection
arXiv
A 2017 paper by Lin, Goyal, Girshick, He and Dollar that argues dense one-stage detectors are held back by extreme foreground-background imbalance and proposes focal loss, a reshaped cross-entropy that down-weights well-classified examples.
- 2017
Attention Is All You Need
NeurIPS
A 2017 paper that introduced the Transformer, a neural network built entirely on attention with no recurrence. It set new machine translation results and became the architecture behind almost every large language model since.
- 2017
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
arXiv
CycleGAN learns image-to-image translation between two domains without aligned pairs, combining an adversarial loss with a cycle-consistency constraint to constrain the mapping.
- 2017
Mask R-CNN
arXiv
Mask R-CNN is a 2017 framework for object instance segmentation. It detects objects and produces a high-quality mask for each instance by adding a parallel mask branch to Faster R-CNN.
- 2016
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
arXiv
A 2016 paper describing Google's neural machine translation system, GNMT: a deep LSTM encoder-decoder with attention, designed to make end-to-end translation practical by addressing rare words, parallelism and inference cost.
- 2015
Deep Residual Learning for Image Recognition
CVPR
The 2015 paper introduced residual networks, which add each layer's input back to its output through a shortcut connection. This made networks of over a hundred layers trainable and won first place in several ILSVRC and COCO tasks in 2015.