The document discusses challenges in autonomous driving technology and potential solutions from three research papers. It notes that deep learning-based approaches have limitations when distributions shift due to changes in weather or road conditions. It proposes that detecting distribution shifts and using self-supervised or semi-supervised learning could help address these issues. Specifically, it recommends research on detecting and adapting to distribution shifts, and leveraging unlabeled data through self-supervised vision transformers or predicting view assignments with support samples.
This document discusses generative adversarial networks (GANs) and their relationship to reinforcement learning. It begins with an introduction to GANs, explaining how they can generate images without explicitly defining a probability distribution by using an adversarial training process. The second half discusses how GANs are related to actor-critic models and inverse reinforcement learning in reinforcement learning. It explains how GANs can be viewed as training a generator to fool a discriminator, similar to how policies are trained in reinforcement learning.
The document summarizes a research paper that compares the performance of MLP-based models to Transformer-based models on various natural language processing and computer vision tasks. The key points are:
1. Gated MLP (gMLP) architectures can achieve performance comparable to Transformers on most tasks, demonstrating that attention mechanisms may not be strictly necessary.
2. However, attention still provides benefits for some NLP tasks, as models combining gMLP and attention outperformed pure gMLP models on certain benchmarks.
3. For computer vision, gMLP achieved results close to Vision Transformers and CNNs on image classification, indicating gMLP can match their data efficiency.
The document discusses challenges in autonomous driving technology and potential solutions from three research papers. It notes that deep learning-based approaches have limitations when distributions shift due to changes in weather or road conditions. It proposes that detecting distribution shifts and using self-supervised or semi-supervised learning could help address these issues. Specifically, it recommends research on detecting and adapting to distribution shifts, and leveraging unlabeled data through self-supervised vision transformers or predicting view assignments with support samples.
This document discusses generative adversarial networks (GANs) and their relationship to reinforcement learning. It begins with an introduction to GANs, explaining how they can generate images without explicitly defining a probability distribution by using an adversarial training process. The second half discusses how GANs are related to actor-critic models and inverse reinforcement learning in reinforcement learning. It explains how GANs can be viewed as training a generator to fool a discriminator, similar to how policies are trained in reinforcement learning.
The document summarizes a research paper that compares the performance of MLP-based models to Transformer-based models on various natural language processing and computer vision tasks. The key points are:
1. Gated MLP (gMLP) architectures can achieve performance comparable to Transformers on most tasks, demonstrating that attention mechanisms may not be strictly necessary.
2. However, attention still provides benefits for some NLP tasks, as models combining gMLP and attention outperformed pure gMLP models on certain benchmarks.
3. For computer vision, gMLP achieved results close to Vision Transformers and CNNs on image classification, indicating gMLP can match their data efficiency.
3. 紹介する論文について
Generative Adversarial Nets
Advances in Neural Information Processing Systems 27 (NIPS 2014)
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza,
Bing Xu, David Warde-Farley, Sherjil Ozair,
Aaron Courville, Yoshua Bengio
• 競合する二つのネットワークの学習
• 「ピカソのような」生成モデルと「前例のない」識別モデル概要
背景
• これまでの機械学習では大量のデータが必要
• 「膨大な手作業の可能性」を解消したい
“The most interesting idea in the last 10 years in ML, in my opinion.”
–Yann LeCun
