https://github.com/sunnyhelen/jperceiver
공식 코드JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes
Papers with Code 아카이브
Transfer Learning
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Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.
<span style="color:grey; opacity: 0.6">( Image credit: Subodh Malgonde )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sunpihai-up/fuse
공식 코드https://github.com/super-shayan/feml
공식 코드https://github.com/super-shayan/qlinear
공식 코드https://github.com/super-tadory/1p-frac
공식 코드https://github.com/supernlogn/squeezeDetTL
공식 코드A Framework of Transfer Learning in Object Detection for Embedded Systems
https://github.com/supertyd/XTrack
공식 코드XTrack: Multimodal Training Boosts RGB-X Video Object Trackers
https://github.com/surafelml/Afro-NMT
공식 코드Low Resource Neural Machine Translation: A Benchmark for Five African Languages
https://github.com/surrey-nlp/homo_gen
공식 코드Automatically Generating Chinese Homophone Words to Probe Machine Translation Estimation Systems
https://github.com/susaha/ctrl-uda
공식 코드Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation
https://github.com/sutd-visual-computing-group/KML-Classification
공식 코드Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning
https://github.com/sutdqinyan/multi-stage-soh
공식 코드https://github.com/suu990901/LLaMA-InfoEntropy-Loss
공식 코드MiLe Loss: a New Loss for Mitigating the Bias of Learning Difficulties in Generative Language Models
https://github.com/suvarnak/generativefslcovid
공식 코드Generative Transfer Learning: Covid-19 Classification with a few Chest X-ray Images
https://github.com/svanesch/tustinNetTransferLearning
공식 코드Accounts of using the Tustin-Net architecture on a rotary inverted pendulum
https://github.com/svishwa/crowdcount-cascaded-mtl
공식 코드CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
https://github.com/svjan5/medtype
공식 코드https://github.com/svrtk/masc-multi-task-segmentation-and-classification
공식 코드https://github.com/swagato-c/bangla-hwcr-present
공식 코드https://github.com/swaggy66/M-ABSA
공식 코드M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis
https://github.com/sweatybridge/text-to-anime
공식 코드https://github.com/sweta20/Detecting-Cyberbullying-Across-SMPs
공식 코드Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms
https://github.com/swlzq/Semi-Online-KD
공식 코드https://github.com/swordcheng/FCSR-GAN
공식 코드FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task Learning
https://github.com/swufung/ADMMSoftmax
공식 코드ADMM-SOFTMAX : An ADMM Approach for Multinomial Logistic Regression
https://github.com/sy-wada/blue_benchmark_with_transformers
공식 코드Pre-training technique to localize medical BERT and enhance biomedical BERT
https://github.com/sydney-machine-learning/ganclassimbalanced
공식 코드SMOTified-GAN for class imbalanced pattern classification problems
https://github.com/sylyoung/DeepTransferEEG
공식 코드T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs
https://github.com/sylyoung/abaw4-hust-ant
공식 코드Facial Affect Analysis: Learning from Synthetic Data & Multi-Task Learning Challenges
https://github.com/synapse-developpement/DiscEval
공식 코드A Pragmatics-Centered Evaluation Framework for Natural Language Understanding
https://github.com/synapse-developpement/pragmeval
공식 코드A Pragmatics-Centered Evaluation Framework for Natural Language Understanding
https://github.com/synbol/awesome-parameter-efficient-transfer-learning
공식 코드Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey
https://github.com/szc12153/sparse_meta_tuning
공식 코드Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
https://github.com/szzexpoi/rex
공식 코드https://github.com/t6yang/vilref
공식 코드ViLReF: An Expert Knowledge Enabled Vision-Language Retinal Foundation Model
https://github.com/tacju/partimagenet
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.