https://github.com/nlp-uoregon/famie
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
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/nlpodyssey/verbaflow
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
https://github.com/nmvdlei/PPMI_MAGNN
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
https://github.com/nobug-code/Diversity_Networks_CNN_Pytorch
Diversity Networks: Neural Network Compression Using Determinantal Point Processes
https://github.com/nocotan/pytorch-lightning-gans
https://github.com/noriyukipy/tfchat
https://github.com/noriyukipy/tfdlg
https://github.com/nrimsky/devinterp
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
https://github.com/nutonomy/nuscenes-devkit
Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking
https://github.com/nvakhoa/mpad
Few-Shot Object Detection via Synthetic Features with Optimal Transport
https://github.com/nvlabs/long-video-gan
Towards Accurate Generative Models of Video: A New Metric & Challenges
https://github.com/nvlabs/traffic-behavior-simulation
https://github.com/nybupt/athena
ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial Defense
https://github.com/nyu-dl/dl4dial-bayesian-calibration
Importance of Search and Evaluation Strategies in Neural Dialogue Modeling
https://github.com/o4lc/greg-ood
DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
https://github.com/ollenilsson19/qdgym
Quality and Diversity Optimization: A Unifying Modular Framework
https://github.com/omerb01/puq
https://github.com/oncescuandreea/audio-retrieval
https://github.com/onion-liu/aahq-dataset
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
https://github.com/open-mmlab/mmdetection3d
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
https://github.com/open-mmlab/mmpose
https://github.com/openvinotoolkit/anomalib/tree/development/anomalib/models/reverse_distillation
Anomaly Detection via Reverse Distillation from One-Class Embedding
https://github.com/oslumbers/diverse-psro
Modelling Behavioural Diversity for Learning in Open-Ended Games
https://github.com/osmr/imgclsmob
PolyNet: A Pursuit of Structural Diversity in Very Deep Networks
https://github.com/oswaldoludwig/Parallel-Seq2Seq
End-to-end Adversarial Learning for Generative Conversational Agents
https://github.com/ovidiuchile/AEA2019
A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Drone
https://github.com/owennju/fgrada
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation
https://github.com/p-mc-grath/DMMFODS
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
https://github.com/pb-brainiac/semseg_od
Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift
https://github.com/pearl-utexas/sope
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning
https://github.com/pedrodbs/InterestingnessXRL
https://github.com/pender/chatbot-rnn
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/pfnet-research/nips17-adversarial-attack
Adversarial Transformation Networks: Learning to Generate Adversarial Examples
https://github.com/philsyn/diffwave-unconditional
https://github.com/philsyn/diffwave-vocoder
https://github.com/phiyodr/dacl10k-toolkit
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.