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Self-Driving Cars

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Self-driving cars : the task of making a car that can drive itself without human guidance.

<span style="color:grey; opacity: 0.6">( Image credit: Learning a Driving Simulator )</span>

코드

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

https://github.com/jackstelling/biasmitigation

공식 코드

"Just Drive": Colour Bias Mitigation for Semantic Segmentation in the Context of Urban Driving

https://github.com/jianf-wang/np-semiseg

공식 코드

NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation

https://github.com/jinghanY/physicalAttackImageComposition

공식 코드

Finding Physical Adversarial Examples for Autonomous Driving with Fast and Differentiable Image Compositing

https://github.com/jonathsch/lidar-synthesis

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LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels

https://github.com/joshuadbruton/tsc

공식 코드

Translated Skip Connections -- Expanding the Receptive Fields of Fully Convolutional Neural Networks

https://github.com/kirk86/RAIOOD

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On the Importance of Regularisation & Auxiliary Information in OOD Detection

https://github.com/lkeab/gsnet

공식 코드

GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision

https://github.com/longph1989/Socrates

공식 코드

SOCRATES: Towards a Unified Platform for Neural Network Analysis

https://github.com/lxuniverse/NICE

공식 코드

Neural Image Compression and Explanation

https://github.com/mahautm/emecom_pop_data

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Referential communication in heterogeneous communities of pre-trained visual deep networks

https://github.com/marko-vasic/moet

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MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning

https://github.com/mchancan/citylearn

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MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navigation

https://github.com/mchancan/deepseqslam

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DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition

https://github.com/meghabyte/bottleneck

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Policy Learning with a Language Bottleneck

https://github.com/mehmetaygun/4d-pls

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4D Panoptic LiDAR Segmentation

https://github.com/mit-han-lab/spvnas

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Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

https://github.com/mit-spark/perceptionmonitoring

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Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identification

https://github.com/mlatcl/fbp-vs-soa

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Towards better data discovery and collection with flow-based programming

https://github.com/murpheylab/maxdiffrl

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Maximum diffusion reinforcement learning

https://github.com/northocean/msn

공식 코드

MSN: Multi-Style Network for Trajectory Prediction

https://github.com/nranjbar/interpretable_machine_learning

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Using Decision Tree as Local Interpretable Model in Autoencoder-based LIME

https://github.com/nyu-ce-projects/depth-estimation

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Self-Supervised Learning based Depth Estimation from Monocular Images

https://github.com/panmt/mbrl_with_isolated_imaginations

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Model-Based Reinforcement Learning with Isolated Imaginations

https://github.com/phoenixrider12/visual_failure_mitigation

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Detecting and Mitigating System-Level Anomalies of Vision-Based Controllers

https://github.com/pit30m/pit30m

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Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars

https://github.com/rana2149/actnetformer

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ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in Videos

https://github.com/ravikt/gril

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Imitation Learning with Human Eye Gaze via Multi-Objective Prediction

https://github.com/roboak/nav-q

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Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Cars

https://github.com/rosewang2008/rmaddpg

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R-MADDPG for Partially Observable Environments and Limited Communication

https://github.com/runpeidong/pointdistiller

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PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection

https://github.com/safednn-group/nap-ood

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Detection of out-of-distribution samples using binary neuron activation patterns

https://github.com/safreita1/unmask

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UnMask: Adversarial Detection and Defense Through Robust Feature Alignment

https://github.com/sancharisen/EMPIR

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EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks

https://github.com/shruti-jadon/Semantic-Segmentation-Loss-Functions

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A survey of loss functions for semantic segmentation

https://github.com/sinop97/drive_sim_road_generation

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Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability

https://github.com/stefanos50/CARLA2Real

공식 코드

CARLA2Real: a tool for reducing the sim2real gap in CARLA simulator

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