https://github.com/guy-oren/DIRT-OST
Diverse Image-to-Image Translation via Disentangled Representations
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/han-shi/SparseBERT
https://github.com/happen2me/kqa-pro-datasets
https://github.com/harvard-visionlab/lrm-steering
Hierarchical Text-Conditional Image Generation with CLIP Latents
https://github.com/hbenbel/Colorization
https://github.com/heathcliff-saku/viat
Towards Viewpoint-Invariant Visual Recognition via Adversarial Training
https://github.com/hemanth346/mde_bs
https://github.com/hichoe95/rarity-score
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images
https://github.com/hiyouga/llama-efficient-tuning
Long and Diverse Text Generation with Planning-based Hierarchical Variational Model
https://github.com/hlt-mt/fbk-fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/hltchkust/cantonese-asr
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/hnuzhy/bpjdet
https://github.com/holounic/DvD-TD3
Effective Diversity in Population Based Reinforcement Learning
https://github.com/hongxin001/ODNL
Combating noisy labels by agreement: A joint training method with co-regularization
https://github.com/hsalhab/Coloring-in-the-Deep
https://github.com/hsfzxjy/dprn
https://github.com/hsgodhia/hred
Mutual Information and Diverse Decoding Improve Neural Machine Translation
https://github.com/huangjiancong1/rlkit_autotune
Hyperparameter Auto-tuning in Self-Supervised Robotic Learning
https://github.com/huanyuhello/CoDiNet
CoDiNet: Path Distribution Modeling with Consistency and Diversity for Dynamic Routing
https://github.com/huawei-noah/Full-Stack-Filters
https://github.com/huggingface/torchMoji
https://github.com/huggingface/transformers
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/hughbzhang/HUSE
Unifying Human and Statistical Evaluation for Natural Language Generation
https://github.com/human-aimachine-art/pytorch-STROTSS-improved
https://github.com/humancompatibleai/go_attack
https://github.com/huybery/MvDSCN
https://github.com/hyeonahkimm/genetic_gfn
Sample-efficient Multi-objective Molecular Optimization with GFlowNets
https://github.com/hytseng0509/DRIT_hr
Diverse Image-to-Image Translation via Disentangled Representations
https://github.com/hyunghona/emu
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning
https://github.com/iamkanghyunchoi/ait
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples
https://github.com/ibm/graph_ensemble_learning
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ibrahim-elsawy/test
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies
https://github.com/icoz69/stablellava
StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation
https://github.com/ihdia/instance-segmentation-v1
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts
https://github.com/ikasu93/Data-Science-Bias-Guideline
InclusiveFaceNet: Improving Face Attribute Detection with Race and Gender Diversity
https://github.com/instadeepai/poppy
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.