https://github.com/interpretml/DiCE
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
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/inverted-ai/torchdrivesim
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation
https://github.com/irh-ca-team-car/attention-data
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
https://github.com/issamlaradji/ssr
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
https://github.com/itzprashu1/prashant
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ivan-chai/torch-linear-assignment
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event Prediction
https://github.com/ivychill/reid
https://github.com/izhangrui/paper_to_read
Comparative evaluation of 2D feature correspondence selection algorithms
https://github.com/jackbandy/bookcorpus-datasheet
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
https://github.com/jackspp/rethinking-self-driving
https://github.com/jackyjsy/ACWGAN
https://github.com/jafarbakhshaliyev/wave-augs
Towards Diverse and Coherent Augmentation for Time-Series Forecasting
https://github.com/jamespark3922/adv-inf
https://github.com/jaryp/pixleattack
Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble Methods
https://github.com/jasonyux/krls
Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context
https://github.com/jbobin/pyGMCALab
https://github.com/jeanjerome/semisupervised_timeseries_infogan
https://github.com/jerinphilip/fairseq-ilmt
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/jerryyann/dpi
https://github.com/jiangsikai/Person_reID_baseline_pytorch
https://github.com/jimth001/Bi-Seq2Seq
https://github.com/jizongFox/deep-clustering-toolbox
https://github.com/jjeamin/PhotoSketch_Pytorch
https://github.com/jkcracker/colorization
https://github.com/joeynmt/joeynmt
Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages
https://github.com/jogo175/treediffusion
https://github.com/john-ai-lab/noisyrollout
NoisyRollout: Reinforcing Visual Reasoning with Data Augmentation
https://github.com/johnnyjana730/ACGAN_animate
https://github.com/jonahwinninghoff/Text-Summarization
https://github.com/jongwooko/nash-pruning-official
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/josephrocca/tfjs-acgan-browser-training
https://github.com/joshpc/StyledFontGAN
GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks
https://github.com/jsbaan/DPAC-DialogueGAN
https://github.com/jssprz/video_captioning_datasets
On the effectiveness of task granularity for transfer learning
https://github.com/juglab/DivNoising
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
https://github.com/juglab/HDN
Interpretable Unsupervised Diversity Denoising and Artefact Removal
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.