https://github.com/microsoft/NeuralSpeech
공식 코드PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/microsoft/ProphetNet
공식 코드https://github.com/microsoft/dialoglm
공식 코드DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization
https://github.com/microsoft/foldingdiff
공식 코드https://github.com/microsoft/methods2test
공식 코드Unit Test Case Generation with Transformers and Focal Context
https://github.com/microsoft/muzic
공식 코드GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework
https://github.com/microsoft/unilm
공식 코드WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
https://github.com/microsoft/vert-papers
공식 코드CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition
https://github.com/microsoft/vq-diffusion
공식 코드https://github.com/mihirp1998/alignprop
공식 코드Aligning Text-to-Image Diffusion Models with Reward Backpropagation
https://github.com/mikemccabe210/amortizedassimilation
공식 코드https://github.com/mikexcohen/GED_tutorial
공식 코드https://github.com/mil-tokyo/FSDDIM
공식 코드https://github.com/minar09/awesome-virtual-try-on
공식 코드https://github.com/mindflow-institue/dermosegdiff
공식 코드DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation
https://github.com/mindspore-ai/mindscience/tree/master/MindSPONGE/applications/MEGAProtein
공식 코드https://github.com/mindspore-lab/mindediting
공식 코드Efficient View Synthesis and 3D-based Multi-Frame Denoising with Multiplane Feature Representations
https://github.com/mingxiao-li/ts-dpm
공식 코드Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time Steps
https://github.com/mingyuan-zhang/MotionDiffuse
공식 코드MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model
https://github.com/mingyuan-zhang/ReMoDiffuse
공식 코드https://github.com/mingyuanzhou/Beta-Diffusion
공식 코드https://github.com/mingyuyng/diffjscc
공식 코드Diffusion-Aided Joint Source Channel Coding For High Realism Wireless Image Transmission
https://github.com/minhmanho/f2f_ldm
공식 코드https://github.com/mirthai/fast-ddpm
공식 코드Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation
https://github.com/mirthai/gdm-ve
공식 코드Geodesic Diffusion Models for Medical Image-to-Image Generation
https://github.com/mit-han-lab/fastcomposer
공식 코드FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention
https://github.com/mit-han-lab/pytorch-quantum
공식 코드QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization
https://github.com/mit-han-lab/torchquantum
공식 코드QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization
https://github.com/mit-spark/midiffusion
공식 코드https://github.com/mivanovska/tomatodiff
공식 코드TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models
https://github.com/ml-gsai/radd
공식 코드Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data
https://github.com/mlaves/uncertainty-deep-image-prior
공식 코드Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior
https://github.com/mli-lab/deepdewedge
공식 코드https://github.com/mli-lab/imaging_mlps
공식 코드https://github.com/mli-lab/scaling_laws_for_deep_learning_based_image_reconstruction
공식 코드https://github.com/mlpc-ucsd/TokenCompose
공식 코드TokenCompose: Text-to-Image Diffusion with Token-level Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.