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/juliawolleb/anomaly_berdiff
Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI
https://github.com/junaid54541/Skin-Cancer-Classification-Tflite-Model
https://github.com/jungokasai/t2r
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/junnyu/paddle_t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/junwei-pan/recscope
On the Embedding Collapse when Scaling up Recommendation Models
https://github.com/junyuandeng/dm-calib
GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image
https://github.com/jwieting/beyond-bleu
Beyond BLEU: Training Neural Machine Translation with Semantic Similarity
https://github.com/jychoi118/p2-weighting
https://github.com/k-Petropoulos/colorization
https://github.com/kabirdabholkar/nlb_tools_fewshot
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
https://github.com/kajyuuen/daaja
https://github.com/kakaobrain/coyo-dataset
Hierarchical Text-Conditional Image Generation with CLIP Latents
https://github.com/kalifou/robotics-rl-srl
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning
https://github.com/kamalkraj/e5-mistral-7b-instruct
https://github.com/kaonashi-tyc/zi2zi
https://github.com/karlchahine/neural-cover-selection-for-image-steganography
https://github.com/karthikv792/gpt-plan-benchmark
https://github.com/karynaur/High-Fidelity-Pluralistic-ICT
High-Fidelity Pluralistic Image Completion with Transformers
https://github.com/karynaur/ICT
High-Fidelity Pluralistic Image Completion with Transformers
https://github.com/katejarne/RRN_dynamics
https://github.com/katejarne/excitatory-inhibitory
https://github.com/kaung-htet-myat/Multi-teachers-Knowledge-Distillation
https://github.com/kaushal-py/seg-degan
Data-free Knowledge Distillation for Segmentation using Data-Enriching GAN
https://github.com/kav-institute/mdn_trajectory_forecasting
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
https://github.com/kayuksel/generative-opt
https://github.com/keke921/gclloss
Feature Fusion from Head to Tail for Long-Tailed Visual Recognition
https://github.com/keke921/gnm-pt
Feature Fusion from Head to Tail for Long-Tailed Visual Recognition
https://github.com/kenjiqq/aesthetics-scorer
DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models
https://github.com/kennethjones17/chatbot
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/kensho-technologies/timtc_vocabs_models
https://github.com/keonlee9420/DiffGAN-TTS
Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
https://github.com/keonlee9420/DiffSinger
https://github.com/keshavvinayak01/Dramatic-Chatbot
https://github.com/kevinlu1211/deep-photo-aesthetics
https://github.com/kevinmusgrave/pytorch-adapt
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.