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/brent-murray/tr3d_pointaugdgcnn
PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
https://github.com/brooklyn1900/SPCNet
Scene Text Detection with Supervised Pyramid Context Network
https://github.com/bruno-31/groupsc
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding
https://github.com/bunny98/Text-to-Image-Using-GAN
https://github.com/buriburisuri/ac-gan
https://github.com/bytedance/dplm
Elucidating the Design Space of Multimodal Protein Language Models
https://github.com/bzylich/persona-chatbot
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/c00k1ez/plain-transformers
https://github.com/caihuaiguang/CHG-Shapley-for-Data-Selection
https://github.com/canturan10/satellighte
Remote Sensing Image Scene Classification: Benchmark and State of the Art
https://github.com/carla-recourse/CARLA
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
https://github.com/carpedm20/BEGAN-tensorflow
https://github.com/carzit/sd-webui-samplers-scheduler
https://github.com/caskcsg/longcontext
Quest: Query-centric Data Synthesis Approach for Long-context Scaling of Large Language Model
https://github.com/cccntu/ft5-demo
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/cccntu/ft5-demo-space
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ceezh/llovi
https://github.com/cenyk1230/GATNE
https://github.com/chameleonTK/continual-learning-for-HAR
https://github.com/chemprop/chemprop
Multi-Objective Molecule Generation using Interpretable Substructures
https://github.com/chen700564/supercd
https://github.com/chengJoin/InterML-for-FS-SEI
Few-Shot Specific Emitter Identification via Hybrid Data Augmentation and Deep Metric Learning
https://github.com/chenguolin/DiffSplat
Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model
https://github.com/chihchenghsieh/eventlogdice
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
https://github.com/chrismgeorge/Artistic_Additions_To_Style_Transfer
https://github.com/chuanmeng/dukenet
Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue
https://github.com/chwneo7/VAE-code
Model-Free Renewable Scenario Generation Using Generative Adversarial Networks
https://github.com/chy-upc/abd
Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation
https://github.com/cindyxinyiwang/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/clips/gsoc2019_bias
https://github.com/clu0/unet.cu
https://github.com/cluebenchmark/superclue-math6
SuperCLUE-Math6: Graded Multi-Step Math Reasoning Benchmark for LLMs in Chinese
https://github.com/clvrai/ACGAN-PyTorch
https://github.com/code-implementation1/Code1/tree/main/DDM
Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary Detection
https://github.com/codecaution/evomoe
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
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