Louis Bouchard has compiled a great list of research papers covering AI breakthroughs that were published during last year. His introductions and links to the papers and even to code make this a great resource.
I first reported on What's AI, as Loius Bouchard is also known, with his curated list of AI research papers for 2021 and it is good to see he has continued with his mission to explain artificial intelligence in simple terms and share the new research state and applications for everyone.
Of course, 2022 was the year of ChatGPT and DALL·E 2 but that doesn't preclude quality research in other areas too, as seen by examining his list of 32 papers:
 Resolution-robust Large Mask Inpainting with Fourier Convolutions
 Stitch it in Time: GAN-Based Facial Editing of Real Videos
 NeROIC: Neural Rendering of Objects from Online Image Collections
Towards real-world blind face restoration with generative facial prior
 D-Net for Learned Multi-Modal Alignment
 Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
 Hierarchical Text-Conditional Image Generation with CLIP Latents
 MyStyle: A Personalized Generative Prior
 OPT: Open Pre-trained Transformer Language Models
 BlobGAN: Spatially Disentangled Scene Representations
 A Generalist Agent
 Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
 Dalle mini
 No Language Left Behind: Scaling Human-Centered Machine Translation
 Dual-Shutter Optical Vibration Sensing
 Make-a-scene: Scene-based text-to-image generation with human priors
 BANMo: Building Animatable 3D Neural Models from Many Casual Videos
 High-resolution image synthesis with latent diffusion models
 Panoptic Scene Graph Generation
 An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
 Expanding Language-Image Pretrained Models for General Video Recognition
 Make-a-video: text-to-video generation without text-video data
 Robust Speech Recognition via Large-Scale Weak Supervision
 DreamFusion: Text-to-3D using 2D Diffusion
 Imagic: Text-Based Real Image Editing with Diffusion Models
 eDiffi: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
 InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images
 Galactica: A Large Language Model for Science
 Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition
 ChatGPT: Optimizing Language Models for Dialogue
 Production-Ready Face Re-Aging for Visual Effects
The 2022 AI Recap on the louisbouchard.ai website includes short explanations behind the research of each paper and videos explaining the concepts. There's a lot to go through but from that list I singled out a few most notable:
Stitch it in Time: GAN-Based Facial Editing of Real Videos Add effects to videos such as smiles or make the subjects look younger or older;all that automatically by using AI-based algorithms, something that previously required expensive software and hardware to do.
Hierarchical Text-Conditional Image Generation with CLIP Latents OpenAI's new model DALL·E 2 is amazing. DALL·E could generate images from text inputs but DALL·E 2 goes beyond that by even editing those images to make them look even better.
MyStyle: A Personalized Generative Prior This new model by Google Research and Tel-Aviv University is incredible. You can create very realistic deepfakes which makes the disinformation campaigns even more scary!
OPT: Open Pre-trained Transformer Language Models GPT-3 is a model developed by OpenAI that you can access through a paid API but have no access to the model itself, but Meta's new model OPT is GPT-3's closest competitor and open source too!
No Language Left Behind: Scaling Human-Centered Machine Translation Meta AI’s most recent model, called “No Language Left Behind” does exactly that: translates across 200 different languages with state-of-the-art quality. A single model can handle 200 languages.
Galactica: A Large Language Model for Science Galactica is a large language model with a size comparable to GPT-3, but specialized on scientific knowledge. The model can write whitepapers, reviews, Wikipedia pages, and code. It knows how to cite and how to write equations.
And more. You can literally spend hours going through that list watching the videos and skimming through the papers. If you however don't feel like delving that deep, there's also a short 8 minutes video that goes through all of the research quickly.
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