https://github.com/ma921/XRDidentifier
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Papers with Code 아카이브
Computational Efficiency
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Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/manashpratim/Flower_Recognition
https://github.com/marcojira/fld
https://github.com/marcojira/fls
https://github.com/marialeyvallina/generalized_contrastive_loss
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition
https://github.com/marload/ConvNets-TensorFlow2
https://github.com/marty1885/sparsenet-pytorch
How Can We Be So Dense? The Benefits of Using Highly Sparse Representations
https://github.com/matsagad/mres-project
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
https://github.com/matthias-wright/jax-fid
https://github.com/mdda/deep-learning-models
https://github.com/mdipcit/GTCNN
Gated Texture CNN for Efficient and Configurable Image Denoising
https://github.com/medford-group/bdqm-vip
A Universal Framework for Featurization of Atomistic Systems
https://github.com/mediatek-neuropilot/mai22-real-time-video-sr
https://github.com/membrai/deblurringmim
https://github.com/mengduanjinghua/spectral-spatial-mamba-for-hsic
Spectral-Spatial Mamba for Hyperspectral Image Classification
https://github.com/metodj/anytimeclassification
https://github.com/mftnakrsu/DeepDream
https://github.com/mgabriel-lt/ap-demodulation
Fast and Accurate Amplitude Demodulation of Wideband Signals
https://github.com/michael-cummins/FedBack
Controlling Participation in Federated Learning with Feedback
https://github.com/microsoft/promptwizard
https://github.com/mila-udem/babyai
https://github.com/milesial/Pytorch-UNet
U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation
https://github.com/mindspore-ai/contrib/tree/master/application/Efficient%20ConvBN%20Blocks%20for%20Transfer%20Learning%20and%20Beyond
https://github.com/mindspore-ai/models/tree/master/official/cv/inceptionv3
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/CrissCrossAttention.py
https://github.com/mindspore-courses/d2l-mindspore/tree/master/chapter_13_chapter_computer-vision
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/inception_v3.py
https://github.com/mindspore-lab/mindone
https://github.com/miniminisu/dcgan-code-cu-foam-3D
Reconstruction of three-dimensional porous media using generative adversarial neural networks
https://github.com/minlee077/BARS
https://github.com/mir-group/pair_allegro
High-performance training and inference for deep equivariant interatomic potentials
https://github.com/mjack3/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/mjhucla/TF-mRNN
https://github.com/mjvakili/cassava_disease_classification
https://github.com/ml-jku/TPDistance
https://github.com/mlech26l/qnn_robustness_benchmarks
Scalable Verification of Quantized Neural Networks (Technical Report)
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.