https://github.com/jliartis/art-recognition
공식 코드Synergy of Machine and Deep Learning Models for Multi-Painter Recognition
Papers with Code 아카이브
Transfer Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/jlibovicky/assess-multilingual-bert
공식 코드https://github.com/jlim13/keystroke-inference-attack-synthetic-dataset-generator-
공식 코드Revisiting the Threat Space for Vision-based Keystroke Inference Attacks
https://github.com/jliu4ai/dpa
공식 코드Dynamic Prototype Adaptation with Distillation for Few-shot Point Cloud Segmentation
https://github.com/jmachadofreitas/multitask-ijcnn2022
공식 코드Compressed Hierarchical Representations for Multi-Task Learning and Task Clustering
https://github.com/jmdvinodjmd/HyperITE
공식 코드https://github.com/jmdvinodjmd/hcr-net
공식 코드HCR-Net: A deep learning based script independent handwritten character recognition network
https://github.com/jmpu/webconf21-deepfakes-in-the-wild
공식 코드https://github.com/jngwenye/pncloning
공식 코드https://github.com/jnwilliams/fuse_prompt_inversion
공식 코드FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across Tokenizers
https://github.com/joangog/object-detection
공식 코드https://github.com/joaodsmarques/are-ecgs-enough-deep-learning-classifiers
공식 코드Are ECGs enough? Deep learning classification of cardiac anomalies using only electrocardiograms
https://github.com/jochemloedeman/pgn
공식 코드Prompt Generation Networks for Input-Space Adaptation of Frozen Vision Transformers
https://github.com/joel99/objectnav
공식 코드https://github.com/joelniklaus/swissjudgementprediction
공식 코드An Empirical Study on Cross-X Transfer for Legal Judgment Prediction
https://github.com/joelowj/unified_mom_mmoe
공식 코드https://github.com/joeshenouda/effects-mtl-nns
공식 코드The Effects of Multi-Task Learning on ReLU Neural Network Functions
https://github.com/joeshenouda/vv-spaces-nn-width
공식 코드https://github.com/johanna-einsiedler/covid-19-air-pollution
공식 코드https://github.com/john-ai-lab/rapid
공식 코드Long-Context Inference with Retrieval-Augmented Speculative Decoding
https://github.com/johncai117/DAMSL
공식 코드https://github.com/johnheo/fast-compress-vit
공식 코드Training-Free Acceleration of ViTs with Delayed Spatial Merging
https://github.com/johnmath/tmi-pets24
공식 코드TMI! Finetuned Models Leak Private Information from their Pretraining Data
https://github.com/joncarter1/wav2sleep
공식 코드wav2sleep: A Unified Multi-Modal Approach to Sleep Stage Classification from Physiological Signals
https://github.com/joneswack/opu-kernel-experiments
공식 코드Kernel computations from large-scale random features obtained by Optical Processing Units
https://github.com/jongdory/damt
공식 코드Domain Aware Multi-Task Pretraining of 3D Swin Transformer for T1-weighted Brain MRI
https://github.com/joonha425/sslpdl
공식 코드Self-Supervised Learning with Probabilistic Density Labeling for Rainfall Probability Estimation
https://github.com/jordan-bird/synthetic-fruit-image-generator
공식 코드Fruit Quality and Defect Image Classification with Conditional GAN Data Augmentation
https://github.com/jordipons/musicnn
공식 코드musicnn: Pre-trained convolutional neural networks for music audio tagging
https://github.com/jordipons/neural-classifiers-with-few-audio
공식 코드https://github.com/jordipons/sklearn-audio-transfer-learning
공식 코드musicnn: Pre-trained convolutional neural networks for music audio tagging
https://github.com/josangyeon/dsg-kd
공식 코드DSG-KD: Knowledge Distillation from Domain-Specific to General Language Models
https://github.com/josarajar/HTRTF
공식 코드Boosting Handwriting Text Recognition in Small Databases with Transfer Learning
https://github.com/josesosajs/multimae-meets-eo
공식 코드https://github.com/josuastadelmaier/t-cell-response-prediction
공식 코드https://github.com/jpdefrutos/ddmr
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.