https://github.com/susunghong/seg-sdxl
Improving Sample Quality of Diffusion Models Using Self-Attention Guidance
Papers with Code 아카이브
Diversity
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Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/suu990901/InfoEntropy-Loss
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
https://github.com/suu990901/LLaMA-InfoEntropy-Loss
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
https://github.com/swansonk14/chemprop
Multi-Objective Molecule Generation using Interpretable Substructures
https://github.com/tabtoyou/kollava
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
https://github.com/taey16/pix2pixBEGAN.pytorch
https://github.com/taeyoung96/gril-calib
S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM
https://github.com/taki0112/DRIT-Tensorflow
Diverse Image-to-Image Translation via Disentangled Representations
https://github.com/taki0112/SDIT-Tensorflow
https://github.com/taki0112/StarGAN_v2-Tensorflow
https://github.com/taki0112/denoising-diffusion-gan-Tensorflow
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
https://github.com/takuhirok/CP-GAN
https://github.com/takuhirok/rGAN
https://github.com/tanyuqian/progressive-generation
Jointly Measuring Diversity and Quality in Text Generation Models
https://github.com/taoqiongxing/Enhancing-Relation-Extraction-using-Syntactic-Indicators-and-Sentential-Contexts
Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts
https://github.com/taoyds/sparc
https://github.com/taroogura/Person_reID_baseline_pytorch
https://github.com/tatianapassali/artificial-disfluency-generation
Building a Conversational Agent Overnight with Dialogue Self-Play
https://github.com/teapearce/conditional_diffusion_mnist
https://github.com/techmn/satmae_pp
Remote Sensing Image Scene Classification: Benchmark and State of the Art
https://github.com/tencent-ailab/3m-asr
SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts
https://github.com/tencent/censeoqoe
Considering user agreement in learning to predict the aesthetic quality
https://github.com/tensorflow/models/tree/master/research/lm_commonsense
https://github.com/tensorpack/tensorpack/tree/master/examples/GAN
https://github.com/tfolkman/deep-learning-experiments
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/tgisaturday/dalle-lightning
https://github.com/tgisaturday/dalle-lightning-tpu
https://github.com/tgisaturday/taming-transformers-tpu
https://github.com/thecodemasterk/Text-to-Text-transfer-transformers
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/thefonseca/predictive-coding
Moments in Time Dataset: one million videos for event understanding
https://github.com/thoppe/personal_cv
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
https://github.com/threeracha/Chuibbo-Flask-Server
https://github.com/thu-coai/CDial-GPT
https://github.com/thu-keg/omnievent
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/thu-ml/dpot
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
https://github.com/thudm/swissarmytransformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.