https://github.com/ennauata/housegan
House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation
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/eps696/stargan2
https://github.com/eric-erki/robotics-rl-srl
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning
https://github.com/eric-yim/distribution_reconstruction
https://github.com/eric-yim/image_reconstruction
https://github.com/eric-yim/vqvae
https://github.com/eriklindernoren/PyTorch-GAN
https://github.com/ermongroup/SDEdit
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
https://github.com/ethan5437/PR-VIST
Plot and Rework: Modeling Storylines for Visual Storytelling
https://github.com/evaboost/evaboost
Pruning Techniques for Mixed Ensembles of Genetic Programming Models
https://github.com/evan11401/CS_IOC5008_0856043_HW2
https://github.com/fabio-sim/Depth-Anything-ONNX
https://github.com/facebookresearch/atlas
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/facebookresearch/contextualized-vendi-score-guidance
Improving Geo-diversity of Generated Images with Contextualized Vendi Score Guidance
https://github.com/facebookresearch/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/facebookresearch/habitat-challenge
https://github.com/facebookresearch/meru
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
https://github.com/facebookresearch/minihack
Generalization to New Sequential Decision Making Tasks with In-Context Learning
https://github.com/facebookresearch/multimodal
Hierarchical Text-Conditional Image Generation with CLIP Latents
https://github.com/facebookresearch/pytorch_GAN_zoo
https://github.com/facebookresearch/rcdm
https://github.com/facebookresearch/responsiblenlp
Improving Model Evaluation using SMART Filtering of Benchmark Datasets
https://github.com/facebookresearch/vissl
A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
https://github.com/fairseq/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/fangzong12/FeatherNet
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
https://github.com/fangzy96/tabcutmix
https://github.com/fengyang95/pyCFTrackers
https://github.com/fer-moreira/ColorizerBot
https://github.com/fgnt/spatiospectral_diarization
Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks
https://github.com/filipbasara0/simple-diffusion
https://github.com/fjxmlzn/DoppelGANger
PacGAN: The power of two samples in generative adversarial networks
https://github.com/fkhawar/Conformative-Filtering
https://github.com/forever208/adm-es
https://github.com/forever208/ddpm-ip
https://github.com/formiel/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/fotol1/RecSysPapersReviews
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.