https://github.com/computational-cell-analytics/micro-sam
Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
Papers with Code 아카이브
NLP based Person Retrival
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/conan7882/adversarial-autoencoders
https://github.com/conceptofmind/LaMDA-pytorch
https://github.com/conceptofmind/NesT-flax
https://github.com/conceptofmind/lamda-rlhf-pytorch
https://github.com/cordercorder/knn-models
https://github.com/coronazap/bert_client
https://github.com/cosmoquester/2021-dialogue-summary-competition
https://github.com/cosmoquester/seq2seq
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/cosmoquester/speech-recognition
https://github.com/cosmoquester/transformers-bart-pretrain
https://github.com/cospplay/bert-master
https://github.com/cove-adml/adml-anon
https://github.com/coxy1989/tfmr
https://github.com/cpm0722/transformer_pytorch
https://github.com/cpuimage/SINet
https://github.com/cristinecosta/CompVis
https://github.com/cs-giung/FSRNet-pytorch
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
https://github.com/csukuangfj/optimized_transducer
Improving RNN Transducer Modeling for End-to-End Speech Recognition
https://github.com/cswry/osediff
Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-Resolution
https://github.com/ctr4si/A-Hierarchical-Latent-Structure-for-Variational-Conversation-Modeling
A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion
https://github.com/cuber2460/bert
https://github.com/cui0523/Code6/tree/main/duconv
Multiple Generative Models Ensemble for Knowledge-Driven Proactive Human-Computer Dialogue Agent
https://github.com/cui0523/Code6/tree/main/gnmt_v2
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/cui0523/Code6/tree/main/mass
MASS: Masked Sequence to Sequence Pre-training for Language Generation
https://github.com/cui0523/Code6/tree/main/speech_transformer
A Simplified Fully Quantized Transformer for End-to-end Speech Recognition
https://github.com/curt-tigges/query2label
Query2Label: A Simple Transformer Way to Multi-Label Classification
https://github.com/custodio78/Speech_Recognition
https://github.com/cuteyyt/ImageSteganography
https://github.com/cuzyoung/crossearth
MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining
https://github.com/cvi-szu/linly
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
https://github.com/cy-xu/spatially_aware_ai
https://github.com/cyb1212/Monocular-Depth-Estimation-vis-Transfer-learning-Winter-school-
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/cyberzhg/keras-pos-embd
https://github.com/cycloneboy/pdf_table
An End-to-End Multi-Task Learning Model for Image-based Table Recognition
https://github.com/cyhuang-tw/speechcaps
PromptTTS: Controllable Text-to-Speech with Text Descriptions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.