Cover Image

Good Data Is All Imitation Learning Needs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Samadi, Amir, Koufos, Konstantinos, Debattista, Kurt, Dianati, Mehrdad
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SAFE-RL: Saliency-Aware Counterfactual Explainer for Deep Reinforcement Learning Policies
by: Samadi, Amir, et al.
Published: (2024)

Taming Transformers for Realistic Lidar Point Cloud Generation
by: Haghighi, Hamed, et al.
Published: (2024)

A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving Systems
by: Haghighi, Hamed, et al.
Published: (2023)

Run-time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations
by: Yatbaz, Hakan Yekta, et al.
Published: (2024)

Integrity Monitoring of 3D Object Detection in Automated Driving Systems using Raw Activation Patterns and Spatial Filtering
by: Yatbaz, Hakan Yekta, et al.
Published: (2024)

Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation Patterns
by: Yatbaz, Hakan Yekta, et al.
Published: (2024)

Data-driven Camera and Lidar Simulation Models for Autonomous Driving: A Review from Generative Models to Volume Renderers
by: Haghighi, Hamed, et al.
Published: (2024)

Benchmarking the Robustness of Panoptic Segmentation for Automated Driving
by: Wang, Yiting, et al.
Published: (2024)

Do We Need All the Synthetic Data? Targeted Image Augmentation via Diffusion Models
by: Nguyen, Dang, et al.
Published: (2025)

OCT Data is All You Need: How Vision Transformers with and without Pre-training Benefit Imaging
by: Han, Zihao, et al.
Published: (2025)

CliffordNet: All You Need is Geometric Algebra
by: Ji, Zhongping
Published: (2026)

Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need
by: Zhou, Da-Wei, et al.
Published: (2023)

Visual Imitation Learning with Calibrated Contrastive Representation
by: Wang, Yunke, et al.
Published: (2024)

Multi-Camera View Scaling for Data-Efficient Robot Imitation Learning
by: Xie, Yichen, et al.
Published: (2026)

Virtual Category Learning: A Semi-Supervised Learning Method for Dense Prediction with Extremely Limited Labels
by: Chen, Changrui, et al.
Published: (2023)

Building Deep Graph Predictors with Graph Imitation Learning
by: Eberhard, André, et al.
Published: (2026)

A Good Start Matters: Enhancing Continual Learning with Data-Driven Weight Initialization
by: Harun, Md Yousuf, et al.
Published: (2025)

Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning
by: Giammarino, Vittorio, et al.
Published: (2024)

GrootVL: Tree Topology is All You Need in State Space Model
by: Xiao, Yicheng, et al.
Published: (2024)

Data-to-Model Distillation: Data-Efficient Learning Framework
by: Sajedi, Ahmad, et al.
Published: (2024)

A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification
by: Fein-Ashley, Jacob, et al.
Published: (2024)

Cross-Validation Is All You Need: A Statistical Approach To Label Noise Estimation
by: Chen, Jianan, et al.
Published: (2023)

Deep Nonlinear Hyperspectral Unmixing Using Multi-task Learning
by: Mehrdad, Saeid, et al.
Published: (2024)

Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games
by: Schäfer, Lukas, et al.
Published: (2023)

Self-supervised Deep Hyperspectral Inpainting with the Plug and Play and Deep Image Prior Models
by: Li, Shuo, et al.
Published: (2025)

Covariance-Aware Goodness for Scalable Forward-Forward Learning
by: Jiang, Xiaoyi, et al.
Published: (2026)

LUMOS: Language-Conditioned Imitation Learning with World Models
by: Nematollahi, Iman, et al.
Published: (2025)

You Don't Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning
by: Moutakanni, Théo, et al.
Published: (2024)

Is Intermediate Fusion All You Need for UAV-based Collaborative Perception?
by: Hao, Jiuwu, et al.
Published: (2025)

IConE: Batch Independent Collapse Prevention for Self-Supervised Representation Learning
by: Almpanakis, Konstantinos, et al.
Published: (2026)

Stable Diffusion Models are Secretly Good at Visual In-Context Learning
by: Oorloff, Trevine, et al.
Published: (2025)

Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight
by: Xing, Jiaxu, et al.
Published: (2024)

Quantization-Aware Imitation-Learning for Resource-Efficient Robotic Control
by: Park, Seongmin, et al.
Published: (2024)

CARIL: Confidence-Aware Regression in Imitation Learning for Autonomous Driving
by: Delavari, Elahe, et al.
Published: (2025)

Segment Anything Model is a Good Teacher for Local Feature Learning
by: Wu, Jingqian, et al.
Published: (2023)

EgoBridge: Domain Adaptation for Generalizable Imitation from Egocentric Human Data
by: Punamiya, Ryan, et al.
Published: (2025)

Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps
by: Kim, Jeeyung, et al.
Published: (2024)

Is Hyperbolic Space All You Need for Medical Anomaly Detection?
by: Gonzalez-Jimenez, Alvaro, et al.
Published: (2025)

Efficient Training of Deep Networks using Guided Spectral Data Selection: A Step Toward Learning What You Need
by: Sharifi, Mohammadreza, et al.
Published: (2025)

Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning
by: Ding, Runyu, et al.
Published: (2024)