Self-Learning
86 papers with code • 0 benchmarks • 1 datasets
Benchmarks
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LEARNING STYLE IDENTIFICATION
Most implemented papers
Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments
In this paper we present our proof of concept for autonomous self-learning robot navigation in an unknown environment for a real robot without a map or planner.
Domain Adaptation without Source Data
youngryan1993/SFDA-Domain-Adaptation-without-Source-Data
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Our key idea is to leverage a pre-trained model from the source domain and progressively update the target model in a self-learning manner.
Learning Program Synthesis for Integer Sequences from Scratch
We present a self-learning approach for synthesizing programs from integer sequences.
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
Recent work has managed to learn cross-lingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training.
Multi-Source Domain Adaptation and Semi-Supervised Domain Adaptation with Focus on Visual Domain Adaptation Challenge 2019
Panda-Peter/visda2019-multisource
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Semi-Supervised Domain Adaptation: For this task, we adopt a standard self-learning framework to construct a classifier based on the labeled source and target data, and generate the pseudo labels for unlabeled target data.
Self-Learning Transformations for Improving Gaze and Head Redirection
Furthermore, we show that in the presence of limited amounts of real-world training data, our method allows for improvements in the downstream task of semi-supervised cross-dataset gaze estimation.
Knowledge Inheritance for Pre-trained Language Models
thunlp/Knowledge-Inheritance
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NAACL 2022
Specifically, we introduce a pre-training framework named "knowledge inheritance" (KI) and explore how could knowledge distillation serve as auxiliary supervision during pre-training to efficiently learn larger PLMs.
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image Classification
Deep neural networks produce state-of-the-art results when trained on a large number of labeled examples but tend to overfit when small amounts of labeled examples are used for training.
Maximum Bayes Smatch Ensemble Distillation for AMR Parsing
IBM/transition-amr-parser
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NAACL 2022
AMR parsing has experienced an unprecendented increase in performance in the last three years, due to a mixture of effects including architecture improvements and transfer learning.
Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms
data-and-decision-lab/self-improving-RL
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In this work, we propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents using black-box verification methods.
