https://github.com/git-disl/heterobust
공식 코드Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness
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/git-disl/llm-topla
공식 코드https://github.com/gkaradzhov/cognitive-diversity-groups-cogsci24
공식 코드https://github.com/gloria2tt/t3m
공식 코드https://github.com/gmum/AutoLoRA
공식 코드AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models
https://github.com/gmum/proto-segmentation
공식 코드ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts
https://github.com/gofigure-lanl/figure-segmentation
공식 코드DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding
https://github.com/gohtanii/DiverSeg-dataset
공식 코드Rethinking Image Super-Resolution from Training Data Perspectives
https://github.com/gokulkarthik/text2speech
공식 코드Towards Building Text-To-Speech Systems for the Next Billion Users
https://github.com/gol2022/impus
공식 코드IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models
https://github.com/gonz-mart/towards-understanding-the-interplay-of-generative-artificial-intelligence-and-the-internet
공식 코드Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet
https://github.com/google-deepmind/cube
공식 코드Beyond Aesthetics: Cultural Competence in Text-to-Image Models
https://github.com/google-research-datasets/C4_200M-synthetic-dataset-for-grammatical-error-correction
공식 코드Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models
https://github.com/google-research-datasets/Nutrition5k
공식 코드Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food
https://github.com/google-research-datasets/Textual-Entailment-New-Protocols
공식 코드New Protocols and Negative Results for Textual Entailment Data Collection
https://github.com/google-research-datasets/ToTTo
공식 코드Plan-then-Generate: Controlled Data-to-Text Generation via Planning
https://github.com/google-research-datasets/conceptual-12m
공식 코드Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
https://github.com/google-research-datasets/cube
공식 코드Beyond Aesthetics: Cultural Competence in Text-to-Image Models
https://github.com/google-research-datasets/indic-gen-bench
공식 코드https://github.com/google-research-datasets/simulated-dialogue
공식 코드Building a Conversational Agent Overnight with Dialogue Self-Play
https://github.com/google-research/batch_rl
공식 코드https://github.com/google-research/big_vision
공식 코드No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models
https://github.com/google-research/google-research
공식 코드https://github.com/google-research/google-research/tree/master/flare_removal
공식 코드https://github.com/google-research/google-research/tree/master/socraticmodels
공식 코드Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
https://github.com/google-research/masksketch
공식 코드MaskSketch: Unpaired Structure-guided Masked Image Generation
https://github.com/google-research/pix2struct
공식 코드Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
https://github.com/google-research/robotics_transformer
공식 코드https://github.com/google-research/task_adaptation
공식 코드A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
https://github.com/google/example_extrapolation
공식 코드https://github.com/google/ml-compiler-opt
공식 코드MLGO: a Machine Learning Guided Compiler Optimizations Framework
https://github.com/google/uncertainty-baselines
공식 코드Hyperparameter Ensembles for Robustness and Uncertainty Quantification
https://github.com/google/uncertainty-baselines/blob/master/baselines/cifar/hyperbatchensemble.py
공식 코드Hyperparameter Ensembles for Robustness and Uncertainty Quantification
https://github.com/google/wmt19-paraphrased-references
공식 코드https://github.com/gordonberman/MotionMapper
공식 코드Mapping the stereotyped behaviour of freely-moving fruit flies
https://github.com/got-10k/toolkit
공식 코드GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.