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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/ethnhe/FS6D-PyTorch

공식 코드

FS6D: Few-Shot 6D Pose Estimation of Novel Objects

https://github.com/ethz-coss/LLM_voting

공식 코드

LLM Voting: Human Choices and AI Collective Decision Making

https://github.com/eustomaqua/EPFD

공식 코드

Ensemble Pruning based on Objection Maximization with a General Distributed Framework

https://github.com/eustomaqua/SAEP

공식 코드

Sub-Architecture Ensemble Pruning in Neural Architecture Search

https://github.com/evtricks/diffstega

공식 코드

DiffStega: Towards Universal Training-Free Coverless Image Steganography with Diffusion Models

https://github.com/exa-laboratories/t-kam

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Structured and Informed Probabilistic Modeling with the Thermodynamic Kolmogorov-Arnold Model

https://github.com/explain3d/dam

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DAM: Diffusion Activation Maximization for 3D Global Explanations

https://github.com/explainableml/loft

공식 코드

LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance

https://github.com/explorerlu1024/mimad-gan

공식 코드

MIM-GAN-based Anomaly Detection for Multivariate Time Series Data

https://github.com/eyalmichaeli/saspa-aug

공식 코드

Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation

https://github.com/eyalseg/ecrl

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Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep Neuroevolution

https://github.com/f2-song/scalingalignment

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Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment

https://github.com/facebookresearch/Clinical-Trial-Parser

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Information Extraction of Clinical Trial Eligibility Criteria

https://github.com/facebookresearch/Ego-Exo

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Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos

https://github.com/facebookresearch/Generic-Grouping

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Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity

https://github.com/facebookresearch/GraphLog

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Evaluating Logical Generalization in Graph Neural Networks

https://github.com/facebookresearch/ModelRatatouille

공식 코드

Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization

https://github.com/facebookresearch/analyzing-uncertainty-nmt

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Analyzing Uncertainty in Neural Machine Translation

https://github.com/facebookresearch/audio2photoreal

공식 코드

From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations

https://github.com/facebookresearch/dig-in

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DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity

https://github.com/facebookresearch/dlrm

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Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems

https://github.com/facebookresearch/evalgim

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EvalGIM: A Library for Evaluating Generative Image Models

https://github.com/facebookresearch/fjd

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On the Evaluation of Conditional GANs

https://github.com/facebookresearch/flip

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Scaling Language-Image Pre-training via Masking

https://github.com/facebookresearch/habitat-lab

공식 코드

Habitat-Matterport 3D Semantics Dataset

https://github.com/facebookresearch/lavila

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Learning Video Representations from Large Language Models

https://github.com/facebookresearch/length-and-compositional-generalization

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On Provable Length and Compositional Generalization

https://github.com/facebookresearch/llip

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Modeling Caption Diversity in Contrastive Vision-Language Pretraining

https://github.com/facebookresearch/omni3d

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Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild

https://github.com/facebookresearch/ppuda

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Pretraining a Neural Network before Knowing Its Architecture

https://github.com/facebookresearch/rlfh-gen-div

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Understanding the Effects of RLHF on LLM Generalisation and Diversity

https://github.com/facebookresearch/sieve

공식 코드

Sieve: Multimodal Dataset Pruning Using Image Captioning Models

https://github.com/facebookresearch/silk

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SiLK -- Simple Learned Keypoints

https://github.com/facebookresearch/simmc2

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SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations

https://github.com/facebookresearch/syncmatch

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Self-Supervised Correspondence Estimation via Multiview Registration

https://github.com/fadedcosine/pos-guided-neural-text-generation

공식 코드

Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling

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