Energy-based Models (EBMs) offer a powerful approach to generative modeling, but their training can be challenging due to instability and computational expense.
Energy-based Models (EBMs) are a class of generative models that have gained popularity in recent years due to their desirable properties, such as generality, simplicity, and compositionality. However, training EBMs on high-dimensional datasets can be unstable and computationally expensive. Researchers have proposed various techniques to improve the training process and performance of EBMs, including incorporating latent variables, using contrastive representation learning, and leveraging variational auto-encoders.
Recent research has focused on improving the stability and speed of EBM training, as well as enhancing their performance in tasks such as image generation, trajectory prediction, and adversarial purification. Some studies have explored the use of EBMs in semi-supervised learning, where they can be trained jointly with labeled and unlabeled data or pre-trained on observations alone. These approaches have shown promising results across different data modalities, such as image classification and natural language labeling.
Practical applications of EBMs include:
1. Image generation: EBMs have been used to generate high-quality images on benchmark datasets like CIFAR10, CIFAR100, CelebA-HQ, and ImageNet 32x32.
2. Trajectory prediction: EBMs have been employed to predict human trajectories in autonomous platforms, such as self-driving cars and social robots, with improved accuracy and social compliance.
3. Adversarial purification: EBMs have been utilized as a defense mechanism against adversarial attacks on image classifiers by purifying attacked images into clean images.
A company case study involves OpenAI, which has developed state-of-the-art generative models like GPT-3, leveraging energy-based models to improve the performance of their models in various tasks, including natural language processing and computer vision.
In conclusion, Energy-based Models offer a promising approach to generative modeling, with potential applications in various domains. As researchers continue to develop novel techniques to improve their training and performance, EBMs are expected to play an increasingly important role in the field of machine learning.
Energy-based Models (EBM)
Energy-based Models (EBM) Further Reading
1.M-EBM: Towards Understanding the Manifolds of Energy-Based Models http://arxiv.org/abs/2303.04343v1 Xiulong Yang, Shihao Ji2.MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC http://arxiv.org/abs/2006.06897v2 Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu3.Guiding Energy-based Models via Contrastive Latent Variables http://arxiv.org/abs/2303.03023v1 Hankook Lee, Jongheon Jeong, Sejun Park, Jinwoo Shin4.Model Based Planning with Energy Based Models http://arxiv.org/abs/1909.06878v2 Yilun Du, Toru Lin, Igor Mordatch5.Learning Energy-Based Model with Variational Auto-Encoder as Amortized Sampler http://arxiv.org/abs/2012.14936v2 Jianwen Xie, Zilong Zheng, Ping Li6.Trajectory Prediction with Latent Belief Energy-Based Model http://arxiv.org/abs/2104.03086v1 Bo Pang, Tianyang Zhao, Xu Xie, Ying Nian Wu7.Learning Probabilistic Models from Generator Latent Spaces with Hat EBM http://arxiv.org/abs/2210.16486v2 Mitch Hill, Erik Nijkamp, Jonathan Mitchell, Bo Pang, Song-Chun Zhu8.Non-Generative Energy Based Models http://arxiv.org/abs/2304.01297v1 Jacob Piland, Christopher Sweet, Priscila Saboia, Charles Vardeman II, Adam Czajka9.Adversarial purification with Score-based generative models http://arxiv.org/abs/2106.06041v1 Jongmin Yoon, Sung Ju Hwang, Juho Lee10.An empirical study of domain-agnostic semi-supervised learning via energy-based models: joint-training and pre-training http://arxiv.org/abs/2010.13116v1 Yunfu Song, Huahuan Zheng, Zhijian OuEnergy-based Models (EBM) Frequently Asked Questions
What is the energy-based model of probability?
Energy-based models (EBMs) are a class of generative models that define a probability distribution over data points by associating a scalar energy value with each data point. The probability of a data point is inversely proportional to its energy, meaning that lower energy values correspond to higher probabilities. The energy function is designed to capture the structure and patterns in the data, and the goal of training an EBM is to learn the parameters of this energy function so that it assigns low energy to observed data points and high energy to unlikely or implausible data points.
What is the advantage of energy-based models?
Energy-based models offer several advantages over other generative models: 1. Generality: EBMs can represent a wide range of probability distributions and can be applied to various types of data, such as images, text, and time series. 2. Simplicity: EBMs are conceptually simple, as they only require defining an energy function and learning its parameters. 3. Compositionality: EBMs can be easily combined with other models or used as building blocks for more complex architectures. 4. Flexibility: EBMs can be used for both supervised and unsupervised learning tasks, as well as for semi-supervised learning, where they can be trained jointly with labeled and unlabeled data.
Is diffusion model an energy-based model?
Yes, diffusion models can be considered a type of energy-based model. Diffusion models are generative models that learn to generate data by simulating a diffusion process, which is a random walk in the data space. The diffusion process is guided by an energy function, which determines the probability of transitioning between data points. By learning the parameters of this energy function, diffusion models can generate new data points that resemble the observed data. In this sense, diffusion models share the key characteristics of energy-based models, such as associating a scalar energy value with each data point and defining a probability distribution based on these energy values.
How do energy-based models differ from other generative models like GANs and VAEs?
Energy-based models (EBMs) differ from other generative models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) in their approach to defining and learning probability distributions over data points. While GANs learn a generator network that maps random noise to data points and a discriminator network that distinguishes between real and generated data, EBMs directly learn an energy function that assigns scalar energy values to data points. VAEs, on the other hand, learn a probabilistic encoder and decoder that map data points to and from a latent space, while EBMs do not necessarily rely on latent variables.
What are the main challenges in training energy-based models?
Training energy-based models can be challenging due to several factors: 1. Instability: The training process can be unstable, as small changes in the energy function's parameters can lead to large changes in the probability distribution, making it difficult to find a good solution. 2. Computational expense: Computing the partition function, which is required for normalizing the probability distribution, can be computationally expensive, especially for high-dimensional data. 3. Mode collapse: EBMs may suffer from mode collapse, where the model only captures a few dominant modes in the data distribution and fails to represent the full diversity of the data.
What are some techniques to improve the training of energy-based models?
Researchers have proposed various techniques to improve the training process and performance of energy-based models, including: 1. Incorporating latent variables: Introducing latent variables can help capture the underlying structure of the data and improve the model's expressiveness. 2. Using contrastive representation learning: This approach involves learning representations that are invariant to different data transformations, which can help stabilize the training process and improve generalization. 3. Leveraging variational auto-encoders: Combining EBMs with VAEs can help address some of the challenges in training EBMs, such as mode collapse and computational expense.
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