https://github.com/siqueira-hc/Efficient-Facial-Feature-Learning-with-Wide-Ensemble-based-Convolutional-Neural-Networks
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks
Papers with Code 아카이브
Diversity
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
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/sithu31296/semantic-segmentation
https://github.com/sjtu-marl/bd_rd_psro
Modelling Behavioural Diversity for Learning in Open-Ended Games
https://github.com/skojaku/stacklp
Stacking Models for Nearly Optimal Link Prediction in Complex Networks
https://github.com/skoltech-nlp/russe_detox_2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/skrantidatta/Attention-based-Skin-Cancer-Classification
https://github.com/skymanaditya1/FaceOff
https://github.com/smearle/gym-pcgrl
Illuminating Diverse Neural Cellular Automata for Level Generation
https://github.com/smrjan/robotic-vision
https://github.com/smruthidabbiru/Advanced-ChatBot
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/snjstudent/DRIT_pp
DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
https://github.com/soCzech/MasterThesis
TransNet V2: An effective deep network architecture for fast shot transition detection
https://github.com/sobieskibj/rcsb
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
https://github.com/softsys4ai/FlexiBO
ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial Defense
https://github.com/sonalinayak/nlg-eval
https://github.com/songquanpeng/BlendGAN
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
https://github.com/sonycslparis/audio-metrics
Reliable Fidelity and Diversity Metrics for Generative Models
https://github.com/soobin-um/minority-guidance
https://github.com/sophiajw/HistAuGAN
DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
https://github.com/soran-ghaderi/torchebm
Energy-Based Models for Code Generation under Compilability Constraints
https://github.com/soskek/bookcorpus
https://github.com/soskek/homemade_bookcorpus
https://github.com/souvikdgp16/dialo_gpt_daily_dialog
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/souvikshanku/translit-former
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/soycoder/dlrm-fb
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems
https://github.com/spandan-madan/in_distribution_adversarial_examples
When and how CNNs generalize to out-of-distribution category-viewpoint combinations
https://github.com/sruthigorantla/underranking_and_group_fairness
https://github.com/sseol11/DialoGPT
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/sss20young/Chuibbo-Flask-Server
https://github.com/stancld/GeneratingHeadline_GANs
https://github.com/stancld/GeneratingHeadlines_GANs
https://github.com/stevezheng23/fairseq_extension
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/styfeng/SMERTI
https://github.com/subex/STDW
Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method
https://github.com/superizer/vqvae
https://github.com/surajsharma21/Fake-Faces-DCGAN
GDPP: Learning Diverse Generations Using Determinantal Point Process
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.