https://github.com/cvlab-unibo/refrec
공식 코드RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation
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/cvmi-lab/syntheticdata
공식 코드Is synthetic data from generative models ready for image recognition?
https://github.com/cvqluu/MTL-Speaker-Embeddings
공식 코드https://github.com/cwei01/tdss
공식 코드Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation
https://github.com/cyjie429/pmd
공식 코드Progressive Distillation Based on Masked Generation Feature Method for Knowledge Graph Completion
https://github.com/cylance/perturbed-sequence-model
공식 코드PROPS: Probabilistic personalization of black-box sequence models
https://github.com/cywang97/unispeech
공식 코드UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
https://github.com/d-gurgurov/Injecting-Commonsense-Knowledge-into-LLMs
공식 코드Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters
https://github.com/daintlab/stochastic_cross_attention
공식 코드StochCA: A Novel Approach for Exploiting Pretrained Models with Cross-Attention
https://github.com/daiquanyu/AdaGCN
공식 코드Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution
https://github.com/dair-iitd/fusic-kbqa
공식 코드https://github.com/dair-iitd/torpido
공식 코드https://github.com/damithdr/questionanswering
공식 코드https://github.com/damowerko/mtt
공식 코드Multi-Target Tracking with Transferable Convolutional Neural Networks
https://github.com/danielfrees/mlpremier
공식 코드https://github.com/danieljkim0118/taskweb
공식 코드https://github.com/danielkyr/smal
공식 코드Under the Cover Infant Pose Estimation using Multimodal Data
https://github.com/danielshalam/bam
공식 코드Unsupervised Representation Learning by Balanced Self Attention Matching
https://github.com/danilprov/rethinking_lupi
공식 코드Rethinking Knowledge Transfer in Learning Using Privileged Information
https://github.com/danjohnvelasco/Filipino-ULMFiT
공식 코드Pagsusuri ng RNN-based Transfer Learning Technique sa Low-Resource Language
https://github.com/danobohud/TransferLearningforPSC
공식 코드Transfer Learning for Protein Structure Classification at Low Resolution
https://github.com/dariopi/synthetic_data_generation
공식 코드https://github.com/darthsid95/clean-sid
공식 코드Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN Training
https://github.com/darthsid95/spiderstylegan
공식 코드Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN Training
https://github.com/dasol-choi/komultitext
공식 코드https://github.com/data-and-decision-lab/self-improving-RL
공식 코드https://github.com/data-iitd/reformd
공식 코드https://github.com/data-iitd/tigger
공식 코드TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs
https://github.com/dataiku-research/transferability_metrics_for_object_detection
공식 코드https://github.com/david3684/adarank
공식 코드https://github.com/davidandym/task-conflict-in-text-to-text-learners
공식 코드Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?
https://github.com/davidbellamy/labrador
공식 코드Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data
https://github.com/davidgolub/QuestionGeneration
공식 코드Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
https://github.com/davidschulte/hf-dataset-selector
공식 코드Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning
https://github.com/davidup1/urlbert
공식 코드Continuous Multi-Task Pre-training for Malicious URL Detection and Webpage Classification
https://github.com/davidyanande/arc
공식 코드Efficient Adaptation of Large Vision Transformer via Adapter Re-Composing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.