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Deep Learning

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

https://github.com/hustwutao/Deep-Clustering

Clustering with Deep Learning: Taxonomy and New Methods

https://github.com/huyng/incertae

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

https://github.com/hytxx/image-outpainting-optional-mask

Painting Outside the Box: Image Outpainting with GANs

https://github.com/hyungjik/TSC_practice

Deep learning for time series classification: a review

https://github.com/i3p9/deepfake-detection-with-xception

Xception: Deep Learning with Depthwise Separable Convolutions

https://github.com/iFe1er/SMU

SMU: smooth activation function for deep networks using smoothing maximum technique

https://github.com/iFe1er/SMU_pytorch

SMU: smooth activation function for deep networks using smoothing maximum technique

https://github.com/iVMCL/AOGNet-v2

AOGNets: Compositional Grammatical Architectures for Deep Learning

https://github.com/ibm/multimodal-3d-image-segmentation

3D Segmentation with Fully Trainable Gabor Kernels and Pearson's Correlation Coefficient

https://github.com/icanswim/cxr

Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification

https://github.com/idearibosome/embracenet

EmbraceNet: A robust deep learning architecture for multimodal classification

https://github.com/iirishikaii/cleverhans

The Limitations of Deep Learning in Adversarial Settings

https://github.com/imanlab/deep_movement_primitives

Geometric Loss Functions for Camera Pose Regression with Deep Learning

https://github.com/impredicative/irc-url-title-bot

Deep Learning for Image Super-resolution: A Survey

https://github.com/impredicative/urltitle

Neural network models and deep learning - a primer for biologists

https://github.com/imrahulr/adversarial_robustness_pytorch

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/innvariant/deepstruct

deepstruct -- linking deep learning and graph theory

https://github.com/intellabs/fp8-emulation-toolkit

FP8 Formats for Deep Learning

https://github.com/inzva/fake-academic-paper-generation

Tensor2Tensor for Neural Machine Translation

https://github.com/iovdin/l4-pytorch

L4: Practical loss-based stepsize adaptation for deep learning

https://github.com/isds-neu/percnn

Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

https://github.com/isl-org/Open3D-ML

Point-Voxel CNN for Efficient 3D Deep Learning

https://github.com/isrugeek/robotics_recognition

Multimodal Deep Learning for Robust RGB-D Object Recognition

https://github.com/ivMCL/AttentiveNorm_Detection

AOGNets: Compositional Grammatical Architectures for Deep Learning

https://github.com/iwasakishuto/DeepScreening

Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding Specificities

https://github.com/j9650/CoDeepNEAT

Evolving Deep Neural Networks

https://github.com/jackaduma/CycleGAN-VC2

MOSNet: Deep Learning based Objective Assessment for Voice Conversion

https://github.com/jacobwang925/path-integral-pinn

DeepXDE: A deep learning library for solving differential equations

https://github.com/jacobzhaoziyuan/DSAL

Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation

https://github.com/jamesrynn/Basic_DNN

Deep Learning: An Introduction for Applied Mathematicians

https://github.com/janeyoung2018/symbolic-math

Deep Learning for Symbolic Mathematics

https://github.com/jasperzhong/GNNFlow

Temporal Graph Networks for Deep Learning on Dynamic Graphs

https://github.com/javier-hernandezo/faceqgen

FaceQnet: Quality Assessment for Face Recognition based on Deep Learning

https://github.com/javier-hernandezo/faceqnet

FaceQnet: Quality Assessment for Face Recognition based on Deep Learning

https://github.com/jaychempan/LAE-DINO

FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery

https://github.com/jdromano2/tpot-nn

Is deep learning necessary for simple classification tasks?

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