https://github.com/franciszzj/tp-sis
Text Promptable Surgical Instrument Segmentation with Vision-Language Models
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/frgfm/Holocron
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
https://github.com/friedmanroy/hi-generation
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/ftramer/lm-extraction-benchmark
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
https://github.com/fudan-generative-vision/hallo
Hallo: Hierarchical Audio-Driven Visual Synthesis for Portrait Image Animation
https://github.com/fuxiao0719/GeoWizard
GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image
https://github.com/g4vrel/DDPM
https://github.com/gabimelo/portuguese_wsc
https://github.com/gabrielsantosrv/coco-caption
https://github.com/gasolsun36/dynamicrag
APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning
https://github.com/gasolsun36/iter-cot
APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning
https://github.com/gbotev1/cgmfpim
https://github.com/genforce/fairgen
Improving the Fairness of Deep Generative Models without Retraining
https://github.com/giacoballoccu/rep-path-reasoning-recsys
Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path Quality
https://github.com/gicheonkang/gst-visdial
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
https://github.com/gillian850413/Insight_Stress_Analysis
Dreaddit: A Reddit Dataset for Stress Analysis in Social Media
https://github.com/github-zbx/ava_datasets
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection
https://github.com/gitliber/image-colorization
https://github.com/gitlimlab/ACGAN-PyTorch
https://github.com/glassroom/heinsen_attention
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
https://github.com/gmftbyGMFTBY/HashRetrieval
Group-wise Contrastive Learning for Neural Dialogue Generation
https://github.com/gmum/PALATE
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
https://github.com/google-research-datasets/eev
EEV: A Large-Scale Dataset for Studying Evoked Expressions from Video
https://github.com/google-research-datasets/gap-coreference
Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns
https://github.com/google-research/lm-extraction-benchmark
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
https://github.com/google-research/t5x
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/t5x_retrieval
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google/evojax
https://github.com/google/seqio
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/got-10k/toolkit-matlab
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
https://github.com/goyalanil/Multiview_Dataset_MNIST
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
https://github.com/guidance-ai/guidance
Automatic Chain of Thought Prompting in Large Language Models
https://github.com/guilgautier/DPPy_paper
https://github.com/gulucaptain/dynamictrl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/gunwoofer/projet-INF8225
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.