StyleCLIP combines the generative power of StyleGAN with CLIP’s joint image-text embedding to enable intuitive text-based image manipulation.
Background replacement is an important task in video production, special effects and live streaming.
Only vision transformers (ViT) have been able to achieve state-of-the-art performance on ImageNet without using convolution.
Self-attention models, specifically Transformers have taken the computer vision field by storm be it OpenAI’s DALL-E or Google’s ViT models. This creates a need for
Localized Randomized Affine Shadowsampling (LoRAS) locally approximates the manifold by generating a random convex combination of noisy minority class data points.
NeX is a new scene representation based on MPI that models view-dependent effects by performing basis expansion on the pixel representation.
ResNeSt architecture combines the channel-wise attention with multi-path representation into a single unified Split-Attention block.
The basic premise of BigGAN is simple; scale-up GAN training to benefit from larger models and larger batches.
Lux is a Python package that aims to make data exploration easier and quicker with its simple one-line syntax and visualization recommendations.
CoreMLTools is a framework created by Apple that allows you to convert models from third-party libraries to the Core ML format.
Anycost GAN supports fast, responsive previews during image editing by executing the generator at a wide range of computational costs.
Spleeter is a source separation Python library created by the Deezer R&D team for various types of source separation tasks.
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