Papers with Code 아카이브

Optical Character Recognition (OCR)

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하위 태스크: 10
평가표: 6
보존된 평가 기록: 20

Optical Character Recognition or Optical Character Reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo, license plates in cars...) or from subtitle text superimposed on an image (for example: from a television broadcast)

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/razvancaramalau/fedalv

공식 코드

Federated Active Learning for Target Domain Generalisation

https://github.com/razvancaramalau/mobyv2al

공식 코드

MoBYv2AL: Self-supervised Active Learning for Image Classification

https://github.com/rbturnbull/hespi

공식 코드

Hespi: A pipeline for automatically detecting information from hebarium specimen sheets

https://github.com/rcrupiisp/siamesedisambiguation

공식 코드

Disambiguation of Company names via Deep Recurrent Networks

https://github.com/rickymaulanafajri/fal-cur

공식 코드

FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering

https://github.com/rickymaulanafajri/spa

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A Structural-Clustering Based Active Learning for Graph Neural Networks

https://github.com/ridiculouz/llmaaa

공식 코드

LLMaAA: Making Large Language Models as Active Annotators

https://github.com/rio98/hybrid-representation-enhanced-bayesian-active-learning

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Hybrid Representation-Enhanced Sampling for Bayesian Active Learning in Musculoskeletal Segmentation of Lower Extremities

https://github.com/rlphilli/InterpretableActiveLearning

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Interpretable Active Learning

https://github.com/rose-stl-lab/interactive-neural-process

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Deep Bayesian Active Learning for Accelerating Stochastic Simulation

https://github.com/rose-stl-lab/mf-lal

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MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning

https://github.com/rose-stl-lab/multi-fidelity-deep-active-learning

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Disentangled Multi-Fidelity Deep Bayesian Active Learning

https://github.com/rothkopflab/nioc-neurips

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Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs

https://github.com/rotmanguy/mtal

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Multi-task Active Learning for Pre-trained Transformer-based Models

https://github.com/rowanlibr/sophisticated-learning

공식 코드

Planning to Learn: A Novel Algorithm for Active Learning during Model-Based Planning

https://github.com/rpinsler/active-bayesian-coresets

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Bayesian Batch Active Learning as Sparse Subset Approximation

https://github.com/rrmenon10/DISCERN

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DISCERN: Decoding Systematic Errors in Natural Language for Text Classifiers

https://github.com/ruathudo/post-ocr-correction

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An Unsupervised method for OCR Post-Correction and Spelling Normalisation for Finnish

https://github.com/ruidaniel/rbaca

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Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context Adaptation

https://github.com/ruidazeng/stratus

공식 코드

Cloud-Based Deep Learning: End-To-End Full-Stack Handwritten Digit Recognition

https://github.com/ruijiang81/AGB

공식 코드

Cost-Accuracy Aware Adaptive Labeling for Active Learning

https://github.com/ruiyum/active-openset-neat

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Inconsistency-Based Data-Centric Active Open-Set Annotation

https://github.com/ruiyum/stone

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STONE: A Submodular Optimization Framework for Active 3D Object Detection

https://github.com/ruoyuxie/noisy_parallel_data_alignment

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Noisy Parallel Data Alignment

https://github.com/rz-zhang/SeqMix

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SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup

https://github.com/rzayanov/irl-teaching-limited-feedback

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Interactively Teaching an Inverse Reinforcement Learner with Limited Feedback

https://github.com/s-a-malik/batchgfn

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BatchGFN: Generative Flow Networks for Batch Active Learning

https://github.com/s3vqa/s3vqa.github.io

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Select, Substitute, Search: A New Benchmark for Knowledge-Augmented Visual Question Answering

https://github.com/sabjoslo/active-learning-bias

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Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias

https://github.com/sachalevy/active-keyword-selection

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Active Keyword Selection to Track Evolving Topics on Twitter

https://github.com/sachinraja13/TabStructNet

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Table Structure Recognition using Top-Down and Bottom-Up Cues

https://github.com/sail-sg/activeprm

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Efficient Process Reward Model Training via Active Learning

https://github.com/salesforceairesearch/taco

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TACO: Learning Multi-modal Action Models with Synthetic Chains-of-Thought-and-Action

https://github.com/samgriesemer/seqinf

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Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference

https://github.com/samottaghi/adversarial-representation-active-learning

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Adversarial Representation Active Learning

https://github.com/sampolk/d-vic

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Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral Images

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.