3D-aware Conditional Image Synthesis
Pix2pix3D synthesizes 3D objects given a 2D label map, such as a segmentation or edge map.
Can be used to generate 3D objects and photorealistic images from 2D label maps, allowing for explicit 3D user control. Provides an interactive 3D editing demo.
LEVER: Learning to Verify Language-to-Code Generation with Execution
LEVER improves language-to-code generation by learning to verify the generated programs with their execution results.
Improves language-to-code generation by combining CodeLM decoding with verifiers trained to determine whether a program is correct based on its natural language input, program, and execution results.
Efficiency 360: Efficient Vision Transformers
Efficiency 360 compares various vision transformer models based on their performance, number of parameters, and number of floating point operations on multiple datasets.
Introduces an efficient 360 framework for vision transformers to make them more efficient for industrial applications. Compares various vision transformer models based on their performance, number of parameters, and number of floating point operations on multiple datasets.
Text-driven Visual Synthesis with Latent Diffusion Prior
Presents a generic approach using latent diffusion models as powerful image priors for various visual synthesis tasks, including text-to-3D.
Provides a more efficient and effective approach to text-to-image synthesis and visual synthesis tasks, which can be useful for businesses looking to improve their image editing and customized generation processes.
Shared Microexponents: A Little Shifting Goes a Long Way
Presents Block Data Representations, a framework for exploring and evaluating a wide spectrum of narrow-precision formats for deep learning.
Introduces an innovative framework for deep learning that enables the comparison of different quantization standards and identifies a new format called shared microexponents, which outperforms other state-of-the-art quantization approaches. This could be useful for businesses looking to optimize their machine learning models and improve their recommendation systems.