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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/lcysyzxdxc/bench4bench

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Information Density Principle for MLLM Benchmarks

https://github.com/lczech/grenedalf

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grenedalf: population genetic statistics for the next generation of pool sequencing

https://github.com/lczech/pool-seq-pop-gen-stats

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grenedalf: population genetic statistics for the next generation of pool sequencing

https://github.com/ld-ing/qdhf

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Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization

https://github.com/ldenninger/camcontexti2v

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CamContextI2V: Context-aware Controllable Video Generation

https://github.com/ldz666666/riddle

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RiDDLE: Reversible and Diversified De-identification with Latent Encryptor

https://github.com/leaplabthu/adafocusv2

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AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition

https://github.com/leaplabthu/flatten-transformer

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FLatten Transformer: Vision Transformer using Focused Linear Attention

https://github.com/learningmatter-mit/GLAMOUR

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GLAMOUR: Graph Learning over Macromolecule Representations

https://github.com/leduckhai/multimed

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MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder

https://github.com/lee-zix/cen

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Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning

https://github.com/leehaoyuan/ration

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Rationale-based Opinion Summarization

https://github.com/lehaifeng/SCAttNet

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SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images

https://github.com/lemon234071/AdaLabel

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Diversifying Dialog Generation via Adaptive Label Smoothing

https://github.com/lemuria-wchen/DialogVED

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DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation

https://github.com/lennart-finke/simple_stories_generate

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Parameterized Synthetic Text Generation with SimpleStories

https://github.com/lettucecfd/lettuce

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Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-optimization

https://github.com/lgalke/aae-recommender

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Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject Labels

https://github.com/lhc1224/cross-view-affordance-grounding

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Learning Affordance Grounding from Exocentric Images

https://github.com/lhc1224/cross-view-ag

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Learning Affordance Grounding from Exocentric Images

https://github.com/lhf12278/saa

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Person Text-Image Matching via Text-Feature Interpretability Embedding and External Attack Node Implantation

https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth

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Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation

https://github.com/liangchen527/sladd

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Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection

https://github.com/liangxinedu/MDAM

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Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems

https://github.com/lifu-tu/Study-NLP-Robustness

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An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models

https://github.com/ligaripash/facecoresetnet

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FaceCoresetNet: Differentiable Coresets for Face Set Recognition

https://github.com/lil-lab/newsroom

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Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies

https://github.com/lil-lab/nlvr

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A Corpus for Reasoning About Natural Language Grounded in Photographs

https://github.com/lilianmarey/music_pathlets

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Modeling Musical Genre Trajectories through Pathlet Learning

https://github.com/lindehesse/insightr-net

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INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical Examples

https://github.com/linear95/dsp

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Everyone Deserves A Reward: Learning Customized Human Preferences

https://github.com/lingjzhu/zipa

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ZIPA: A family of efficient models for multilingual phone recognition

https://github.com/lingl1024/causalfewshot

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Revisiting Few-Shot Learning from a Causal Perspective

https://github.com/lingxiao-li/hae

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The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image Generation

https://github.com/linhaowei1/clog

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CLoG: Benchmarking Continual Learning of Image Generation Models

https://github.com/linhuixiao/clip-vg

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CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.