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/shahroztariq/CLRNet
공식 코드A Convolutional LSTM based Residual Network for Deepfake Video Detection
https://github.com/shahrukhx01/nnti_hindi_bengali_sentiment_analysis/tree/main/src
공식 코드https://github.com/shaier/comet
공식 코드https://github.com/shakshi12/misleading-covid-vaccination-tweets
공식 코드https://github.com/shamanez/BERT-like-is-All-You-Need
공식 코드https://github.com/shamanez/VUSFA-Variational-Universal-Successor-Features-Approximator
공식 코드https://github.com/shamim-hussain/egt
공식 코드Global Self-Attention as a Replacement for Graph Convolution
https://github.com/shamim-hussain/egt_pytorch
공식 코드Global Self-Attention as a Replacement for Graph Convolution
https://github.com/shamim-hussain/egt_triangular
공식 코드Global Self-Attention as a Replacement for Graph Convolution
https://github.com/shamim-hussain/tgt
공식 코드https://github.com/shankhanil006/ssl-vqa
공식 코드Knowledge Guided Semi-Supervised Learning for Quality Assessment of User Generated Videos
https://github.com/shaohan-chen/transfer-learning-in-mallows-cp
공식 코드Transfer Learning in Information Criteria-based Feature Selection
https://github.com/shaohua0116/MultiDigitMNIST
공식 코드Regularizing Deep Multi-Task Networks using Orthogonal Gradients
https://github.com/shaojiawei07/selective-fd
공식 코드Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher
https://github.com/shaokai1209/fdan
공식 코드Multi-modal Speech Emotion Recognition via Feature Distribution Adaptation Network
https://github.com/sharc-lab/edge-moe
공식 코드https://github.com/sharpiless/nanocrystals-deep-learning
공식 코드https://github.com/sharpiless/nanocrystals-tem-segmentation
공식 코드https://github.com/sharpiless/rgal
공식 코드Relation-Guided Adversarial Learning for Data-free Knowledge Transfer
https://github.com/sheffieldnlp/cwi
공식 코드Strong Baselines for Complex Word Identification across Multiple Languages
https://github.com/shengcailiao/QAConv
공식 코드Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification
https://github.com/shenglandu/pushboundary
공식 코드Push-the-Boundary: Boundary-aware Feature Propagation for Semantic Segmentation of 3D Point Clouds
https://github.com/sheoyon-jhin/pad
공식 코드https://github.com/shervinmin/DeepCovid
공식 코드Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning
https://github.com/sherwinbahmani/ynet_adaptive
공식 코드Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective
https://github.com/shihan-ma/biomime
공식 코드Conditional Generative Models for Simulation of EMG During Naturalistic Movements
https://github.com/shikib/generative_slot_filling
공식 코드GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling
https://github.com/shiming-chen/hsva
공식 코드HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning
https://github.com/shiming-chen/msdn
공식 코드MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning
https://github.com/shin285/KOMORAN
공식 코드Don't Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja
https://github.com/shiningsunnyday/ucl
공식 코드Improving Representational Continuity via Continued Pretraining
https://github.com/shiqiyu/opengait
공식 코드Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark
https://github.com/shiv12spingo/PINN_Research/tree/main/Solidification_Problem
공식 코드A Physics Informed Neural Network (PINN) Methodology for Coupled Moving Boundary PDEs
https://github.com/shivaat/MTLB-STRUCT
공식 코드https://github.com/shiyi-zh0408/nae_cvpr2024
공식 코드Narrative Action Evaluation with Prompt-Guided Multimodal Interaction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.