AI Technology & Industry Review
In a new paper Zipper: A Multi-Tower Decoder Architecture for Fusing Modalities, a Google DeepMind research team introduces Zipper, a multi-tower decoder architecture. This architecture can flexibly combine multimodal generative models from independently pre-trained unimodal decoders and can be reused and repurposed in new multimodal combinations.
A research team from Google DeepMind and University of British Columbia presents Genie, the first generative interactive environment capable of seamlessly generating a diverse array of controllable virtual worlds based on textual prompts, synthetic images, photographs, and even sketches.
In a new paper Improving Image Generation with Better Captions, a research team from OpenAI and Microsoft introduces DALL-E 3, a cutting-edge text-to-image generation system that is benchmarked for its prowess in prompt following, coherence, and aesthetics, demonstrating its competitive edge against existing counterparts.
A NNAISENSE research team introduces a novel class of generative models known as Bayesian Flow Networks (BFNs). These BFNs combine the power of Bayesian inference with neural networks in an iterative modeling process, enabling successful application to continuous, discretized, and discrete data while maintaining competitive performance.
In a paper titled “Generative Image Dynamics,” a Google research team introduces an innovative approach to model natural oscillation dynamics using a single static image. This approach yields photo-realistic animations derived from a lone image, surpassing the performance of previous methods by a substantial margin.
In a new paper SpeechX: Neural Codec Language Model as a Versatile Speech Transformer, a Microsoft research team presents SpeechX, a versatile, robust, and extensible speech generation model that is capable to address zero-shot TTS and various speech transformation tasks, handling both clean and noisy signals.
In the new paper Shap·E: Generating Conditional 3D Implicit Functions, an OpenAI research team proposes Shap·E, a conditional generative model that leverages implicit neural representations to produce complex and diverse 3D assets.
In the new paper Generative Agents: Interactive Simulacra of Human Behavior, a team from Stanford University and Google Research presents agents that draw on generative models to simulate both individual and emergent group behaviours that are humanlike and based on their changing experiences and environment.
In the new paper Unlocking de Novo Antibody Design With Generative Artificial Intelligence, researchers from Absci Corporation leverage the power of generative artificial intelligence for de novo antibody design in a zero-shot and controllable manner, dramatically reducing time and resource requirements for the task.
In the new paper Muse: Text-To-Image Generation via Masked Generative Transformers, a Google Research team introduces Muse, a transformer-based text-to-image synthesis model that leverages masked image modelling to achieve state-of-the-art performance while being significantly faster than diffusion or autoregressive models.
A research team from Mila, McGill University, Université de Montréal, DeepMind and Microsoft proposes GFlowNet, a novel flow network-based generative method that can turn a given positive reward into a generative policy that samples with a probability proportional to the return.v
It’s known that deep neural network models — mostly discriminative models — are prone to making poor predictions with high confidence when they encounter data distribution scenarios that vary too much from their training data.