Imitation Learning Inputting Image Feature to Each Layer of Neural Network

• URL: http://arxiv.org/abs/2401.09691v2
• Date: Fri, 19 Jan 2024 12:43:36 GMT
• Title: Imitation Learning Inputting Image Feature to Each Layer of Neural Network
• Authors: Koki Yamane, Sho Sakaino, Toshiaki Tsuji
• Abstract summary: Imitation learning enables robots to learn and replicate human behavior from training data. Recent advances in machine learning enable end-to-end learning approaches that directly process high-dimensional observation data, such as images. This paper presents a useful method to address this challenge, which amplifies the influence of data with a relatively low correlation to the output.
• Score: 1.6574413179773757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Imitation learning enables robots to learn and replicate human behavior from training data. Recent advances in machine learning enable end-to-end learning approaches that directly process high-dimensional observation data, such as images. However, these approaches face a critical challenge when processing data from multiple modalities, inadvertently ignoring data with a lower correlation to the desired output, especially when using short sampling periods. This paper presents a useful method to address this challenge, which amplifies the influence of data with a relatively low correlation to the output by inputting the data into each neural network layer. The proposed approach effectively incorporates diverse data sources into the learning process. Through experiments using a simple pick-and-place operation with raw images and joint information as input, significant improvements in success rates are demonstrated even when dealing with data from short sampling periods.

Related papers

• Distribution-Level Feature Distancing for Machine Unlearning: Towards a Better Trade-off Between Model Utility and Forgetting [4.220336689294245]
  Recent studies have presented various machine unlearning algorithms to make a trained model unlearn the data to be forgotten. We propose Distribution-Level Feature Distancing (DLFD), a novel method that efficiently forgets instances while preventing correlation collapse. Our method synthesizes data samples so that the generated data distribution is far from the distribution of samples being forgotten in the feature space.
  arXiv  Detail & Related papers  (2024-09-23T06:51:10Z)
• Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution [62.71425232332837]
  We show that training amortized models with noisy labels is inexpensive and surprisingly effective. This approach significantly accelerates several feature attribution and data valuation methods, often yielding an order of magnitude speedup over existing approaches.
  arXiv  Detail & Related papers  (2024-01-29T03:42:37Z)
• Semi-supervised Relation Extraction via Data Augmentation and Consistency-training [2.2209333405427585]
  Semi-supervised learning methods aim to leverage unlabelled data in addition to learning from limited labelled data points. Recently, strong data augmentation combined with consistency-based semi-supervised learning methods have advanced the state of the art in several SSL tasks. In this work, we leverage the recent advances in controlled text generation to perform high quality data augmentation for the Relation extraction task.
  arXiv  Detail & Related papers  (2023-06-16T19:45:42Z)
• Towards Adaptable and Interactive Image Captioning with Data Augmentation and Episodic Memory [8.584932159968002]
  We present an IML pipeline for image captioning which allows us to incrementally adapt a pre-trained model to a new data distribution based on user input. We find that, while data augmentation worsens results, even when relatively small amounts of data are available, episodic memory is an effective strategy to retain knowledge from previously seen clusters.
  arXiv  Detail & Related papers  (2023-06-06T08:38:10Z)
• Semi-Supervised Image Captioning by Adversarially Propagating Labeled Data [95.0476489266988]
  We present a novel data-efficient semi-supervised framework to improve the generalization of image captioning models. Our proposed method trains a captioner to learn from a paired data and to progressively associate unpaired data. Our extensive and comprehensive empirical results both on (1) image-based and (2) dense region-based captioning datasets followed by comprehensive analysis on the scarcely-paired dataset.
  arXiv  Detail & Related papers  (2023-01-26T15:25:43Z)
• Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs [59.96266198512243]
  We propose a decentralized and federated learning algorithm for tasks that are positively and negatively correlated. Our algorithm uses gradients to calculate the correlations among tasks automatically, and dynamically adjusts the communication graph to connect mutually beneficial tasks and isolate those that may negatively impact each other. We conduct experiments on a synthetic Gaussian dataset and a large-scale celebrity attributes (CelebA) dataset.
  arXiv  Detail & Related papers  (2022-12-21T18:58:24Z)
• Automatic Data Augmentation via Invariance-Constrained Learning [94.27081585149836]
  Underlying data structures are often exploited to improve the solution of learning tasks. Data augmentation induces these symmetries during training by applying multiple transformations to the input data. This work tackles these issues by automatically adapting the data augmentation while solving the learning task.
  arXiv  Detail & Related papers  (2022-09-29T18:11:01Z)
• Learning from Few Examples: A Summary of Approaches to Few-Shot Learning [3.6930948691311016]
  Few-Shot Learning refers to the problem of learning the underlying pattern in the data just from a few training samples. Deep learning solutions suffer from data hunger and extensively high computation time and resources. Few-shot learning that could drastically reduce the turnaround time of building machine learning applications emerges as a low-cost solution.
  arXiv  Detail & Related papers  (2022-03-07T23:15:21Z)
• Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation [56.264157127549446]
  Speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction. One of the main challenges in SER is data scarcity. We propose a transfer learning strategy combined with spectrogram augmentation.
  arXiv  Detail & Related papers  (2021-08-05T10:39:39Z)
• The Imaginative Generative Adversarial Network: Automatic Data Augmentation for Dynamic Skeleton-Based Hand Gesture and Human Action Recognition [27.795763107984286]
  We present a novel automatic data augmentation model, which approximates the distribution of the input data and samples new data from this distribution. Our results show that the augmentation strategy is fast to train and can improve classification accuracy for both neural networks and state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-05-27T11:07:09Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.