https://github.com/aimadeus/Transfer_learning_melanoma
공식 코드Deep Transfer Learning for Automated Diagnosis of Skin Lesions from Photographs
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/aimagelab/scad-lod-2024
공식 코드An Attention-based Representation Distillation Baseline for Multi-Label Continual Learning
https://github.com/aioz-ai/CVPRW21_GPS
공식 코드https://github.com/aioz-ai/MICCAI21_MMQ
공식 코드Multiple Meta-model Quantifying for Medical Visual Question Answering
https://github.com/akalikong/ilora
공식 코드Customizing Language Models with Instance-wise LoRA for Sequential Recommendation
https://github.com/akaraspt/tinysleepnet
공식 코드Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring Models
https://github.com/akaraspt/transferability_sleep
공식 코드Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring Models
https://github.com/akariasai/attempt
공식 코드ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts
https://github.com/akariasai/evidentiality_qa
공식 코드Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks
https://github.com/akarimoon/xtask_mt
공식 코드Cross-Task Consistency Learning Framework for Multi-Task Learning
https://github.com/aklein1995/heterogeneous_agents_curiosity_vizdoom
공식 코드https://github.com/aksh555/cl-neril
공식 코드https://github.com/akshaymehra24/TaskTransferAnalysis
공식 코드Understanding the Transferability of Representations via Task-Relatedness
https://github.com/akshitac8/ow-detr
공식 코드https://github.com/aksub99/FissionNet
공식 코드Inverse Design of Potential Singlet Fission Molecules using a Transfer Learning Based Approach
https://github.com/akumar2709/fides_asiaccs
공식 코드A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service Inference
https://github.com/alanansell/bistil
공식 코드Distilling Efficient Language-Specific Models for Cross-Lingual Transfer
https://github.com/alarca94/magrec
공식 코드Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation
https://github.com/alberta-lee/nc-24
공식 코드Online Parallel Multi-Task Relationship Learning via Alternating Direction Method of Multipliers
https://github.com/aldahdooh/SFAD
공식 코드Revisiting Model's Uncertainty and Confidences for Adversarial Example Detection
https://github.com/alebryvas/berk266
공식 코드Abstractive Summarization of Spoken andWritten Instructions with BERT
https://github.com/alejomonbar/coinn
공식 코드https://github.com/aleksficek/covidnews-ner
공식 코드How to tackle an emerging topic? Combining strong and weak labels for Covid news NER
https://github.com/alelab-upenn/graph-scattering-transforms
공식 코드https://github.com/aleph-alpha/trigrams
공식 코드https://github.com/alexanderhberger/cross-dim_i2g
공식 코드Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers
https://github.com/alexandra-chron/siatl
공식 코드An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models
https://github.com/alexandra-chron/wassa-2018
공식 코드NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification
https://github.com/alexandrosstergiou/Squeeze-and-Recursion-Temporal-Gates
공식 코드Multi-Temporal Convolutions for Human Action Recognition in Videos
https://github.com/alexrakowski/dcid
공식 코드https://github.com/alexthebad/ap10k
공식 코드https://github.com/alibaba-damo-academy/3D-Speaker
공식 코드Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision
https://github.com/alibaba-mmai-research/dist
공식 코드Disentangling Spatial and Temporal Learning for Efficient Image-to-Video Transfer Learning
https://github.com/alibaba/EasyNLP
공식 코드EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
https://github.com/alibaba/federatedscope
공식 코드https://github.com/alibabaresearch/damo-convai
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.