https://github.com/sajadamouei/Person-Re-ID-with-light-weight-network
공식 코드An Improved Person Re-identification Method by light-weight convolutional neural network
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/sajjad-sh33/medical-transfer-learning
공식 코드Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation Models
https://github.com/salesforce/PCL
공식 코드Prototypical Contrastive Learning of Unsupervised Representations
https://github.com/salesforce/codet5
공식 코드https://github.com/salesforce/decaNLP
공식 코드The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/salesforce/fewxc
공식 코드Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning
https://github.com/salesforce/query-focused-sum
공식 코드https://github.com/salesforce/simpletod
공식 코드https://github.com/salesforceairesearch/pretrain-time-series-cloudops
공식 코드Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain
https://github.com/salihgunduz/epftoolbox_transfer_learning
공식 코드https://github.com/salmanim20/indo_medicinal_plant
공식 코드IndoHerb: Indonesia Medicinal Plants Recognition using Transfer Learning and Deep Learning
https://github.com/salmon1802/ektf
공식 코드https://github.com/salomonhotegni/mdmtn
공식 코드Multi-Objective Optimization for Sparse Deep Multi-Task Learning
https://github.com/saltudelft/codefill
공식 코드CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences
https://github.com/samakos/document-ai-
공식 코드https://github.com/sami-fak/distributedmtlspca
공식 코드Multi-task learning on the edge: cost-efficiency and theoretical optimality
https://github.com/samin-mehdizadeh/llama-persian-adaptation
공식 코드Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation
https://github.com/samohtaerg/geoerm
공식 코드GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian Manifolds
https://github.com/samra-irshad/3d-boundary-constrained-networks
공식 코드Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks
https://github.com/samsung/mt-llm-nlu
공식 코드Large Language Models for Expansion of Spoken Language Understanding Systems to New Languages
https://github.com/samxmot/RANDGAN
공식 코드RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray
https://github.com/sandipan211/ZSD-SC-Resolver
공식 코드Resolving Semantic Confusions for Improved Zero-Shot Detection
https://github.com/sangwoomo/freezeD
공식 코드Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs
https://github.com/saniaesk/Breast-Cancer-Classification
공식 코드Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study
https://github.com/sanoojan/clip-drdg
공식 코드https://github.com/saodem74/Transfer-Learning-in-Reinforcement-Learning
공식 코드Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations
https://github.com/sarashoouri/efficientmtl
공식 코드Efficient Computation Sharing for Multi-Task Visual Scene Understanding
https://github.com/sarulab-speech/utmosv2
공식 코드https://github.com/sashankmvv/Emotion-recognition
공식 코드Facial Emotion Recognition: A multi-task approach using deep learning
https://github.com/saurabhhssaurabh/reviews_summarization
공식 코드MRCBert: A Machine Reading ComprehensionApproach for Unsupervised Summarization
https://github.com/sauradip/fewshotQAT
공식 코드Few-Shot Temporal Action Localization with Query Adaptive Transformer
https://github.com/sayarghoshroy/infopopularity
공식 코드https://github.com/sbelharbi/learning-class-invariant-features
공식 코드Neural Networks Regularization Through Representation Learning
https://github.com/sberdevices/golos
공식 코드https://github.com/scabini/wastewater_vision
공식 코드https://github.com/scale-lab/adamtl
공식 코드AdaMTL: Adaptive Input-dependent Inference for Efficient Multi-Task Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.