https://github.com/xu1998hz/ssl_kba_expert_layman_style_transfer
공식 코드Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Style Transfer is a technique in computer vision and graphics that involves generating a new image by combining the content of one image with the style of another image. The goal of style transfer is to create an image that preserves the content of the original image while applying the visual style of another image.
<span style="color:grey; opacity: 0.6">( Image credit: A Neural Algorithm of Artistic Style )</span>
The "T" horizontal strip can mimic the back of a sofa with a delicate cushion or details of the uphols or appliances with the color button.
The "T" vertical strip can show a feet or arm of the sofa, shiny, yet firm.
Put "P" next to "T", your curve to delicately with the top "T." It is intertwined. The circular part of "P" can show a cushion or a curved chair and synchronize the subject of furniture.
Make sure "P" is visually relying on "T", which reflects the relationship of cohesion and balance.
"B" can be aligned as a pair of cushions or a modern chair, with mild curves with glossy and modern aesthetics.
"I" can be a symbol of a shiny furniture or a vertical light bar and completes the shapes without overburdess them.
Color palette 4:
Includes soft soil colors such as beige, top and gray shades, along with silent or silver gold tips to touch elegance.
Consider a slope effect to enhance modernity, to keep colors elegant and complex.
Use the overlap or intertwined edges that the letters meet for the symbol of unity.
The plan should allow viewers to distinguish each letter while feeling part of the same "structure".
Use delicate geometric patterns or textures that mimic fabrics or furniture materials such as wood seeds or woven fibers.
These patterns must remain minimalist and focus on highlighting the logo, while maintaining communication.
While it deals with the subject of furniture and design, this concept conveys modernity, creativity and professional. If you like, I can create a draft design for better visualization.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer
Doubly Abductive Counterfactual Inference for Text-based Image Editing
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
ALTER: Auxiliary Text Rewriting Tool for Natural Language Generation
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization
Chinese Painting Style Transfer Using Deep Generative Models
One-shot Unsupervised Domain Adaptation with Personalized Diffusion Models
Mix and match networks: encoder-decoder alignment for zero-pair image translation
Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization
Non-Local Representation based Mutual Affine-Transfer Network for Photorealistic Stylization
Soulstyler: Using Large Language Model to Guide Image Style Transfer for Target Object
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL
Precise and Generalized Robustness Certification for Neural Networks
ParaGuide: Guided Diffusion Paraphrasers for Plug-and-Play Textual Style Transfer
TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship Embeddings
Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
Zero-Shot Contrastive Loss for Text-Guided Diffusion Image Style Transfer
Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation
Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation
Generalized One-shot Domain Adaptation of Generative Adversarial Networks
AccoMontage: Accompaniment Arrangement via Phrase Selection and Style Transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.