Personalizing Text-to-Image Generation using Textual Inversion
This paper presents an approach to personalize text-to-image generation using textual inversion. By using only a few images of a user-provided concept, the model learns to represent it through new 'words' in the embedding space of a text-to-image model. These 'words' can be composed into natural language sentences for personalized image creation.
This approach allows for more creative freedom and personalized image creation through natural language guidance. It can be useful for businesses in industries such as advertising or e-commerce in creating personalized product images and designing marketing campaigns.
Few-Shot Learning Using a Large-Scale Multilingual Seq2seq Model
This paper presents AlexaTM 20B, a 20 billion parameter multilingual sequence-to-sequence (seq2seq) model that achieves state-of-the-art performance on various tasks such as summarization, machine translation, and multilingual tasks. In zero-shot setting, AlexaTM 20B outperforms GPT3 (175B) on SuperGLUE and SQuADv2 datasets and provides SOTA on multilingual tasks such as XNLI, XCOPA, Paws-X, and XWinograd.
AlexaTM 20B is a powerful alternative to decoder-only models for Large-scale Language Model (LLM) training. It can be useful for businesses in industries such as customer support, chatbots, and language translation services that require multilingual text processing and few-shot learning capabilities.