Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One (1912.03263v3)

Abstract: We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based model for the joint distribution p(x,y). In this setting, the standard class probabilities can be easily computed as well as unnormalized values of p(x) and p(x|y). Within this framework, standard discriminative architectures may beused and the model can also be trained on unlabeled data. We demonstrate that energy based training of the joint distribution improves calibration, robustness, andout-of-distribution detection while also enabling our models to generate samplesrivaling the quality of recent GAN approaches. We improve upon recently proposed techniques for scaling up the training of energy based models and presentan approach which adds little overhead compared to standard classification training. Our approach is the first to achieve performance rivaling the state-of-the-artin both generative and discriminative learning within one hybrid model.

• The paper introduces a framework that transforms standard classifiers into hybrid energy-based models to jointly handle generative and discriminative tasks.
• The paper demonstrates competitive performance in both classification and generative sampling, with metrics like IS and FID matching state-of-the-art results.
• The paper outlines an efficient optimization strategy that improves model calibration, out-of-distribution detection, and adversarial robustness.

An Overview of "Your classifier is secretly an energy based model and you should treat it like one"

In this paper, the authors propose a novel computational pathway to enhance learning models by reinterpreting a standard discriminative classifier, which estimates conditional probabilities $p(y | x)$, as an Energy-Based Model (EBM) for the joint distribution $p(x, y)$. By employing this paradigm, they leverage discriminative architectures to simultaneously address generative and discriminative tasks while integrating unlabeled data during training. This approach, herein referred to as JEM (Joint Energy-based Model), suggests that every standard classifier inherently constitutes a hybrid model possessing both discriminative and generative capabilities.

Contributions and Theoretical Underpinnings

The paper makes several contributions that can be highlighted as follows:

1. Framework Introduction: It introduces an intuitive framework to model labels and data jointly, transforming conventional classifiers into hybrid models that perform well in both generative and discriminative contexts.
2. Energy-Based Reinterpretation: Within this framework, the logits of classifier architectures are repurposed to represent the energy function defining the joint density $p(x, y)$, enabling the training of models with improved robustness, calibration, and out-of-distribution detection.
3. Optimization Strategy: By optimizing the product of $p(x)$ and $p(y | x)$, the presented models circumvent the intractability issues commonly associated with traditional EBM training, and they demonstrate high-caliber performance with little overhead compared to traditional classifier training methods.

Empirical Results

The paper substantiates its claims through empirical evaluation, exhibiting performance that rivals state-of-the-art methods in both the generative and discriminative realms:

• Hybrid Modeling: JEM produces competitive results both for classification accuracy and generative sample quality, measured via Inception Score (IS) and Frechet Inception Distance (FID). For example, on the CIFAR10 dataset, JEM achieved an IS of 8.76, which competes favorably with standard generative models such as SNGAN and NCSN.
• Model Calibration: JEM displays improved calibration, enhancing reliability in real-world applications where decision confidence is critical. It optimizes Expected Calibration Error (ECE), surpassing conventional discriminative models when measured on datasets like CIFAR100.
• Out-of-Distribution Detection: Utilizing the energy formulation, JEM sets forth a compelling performance in detecting OOD examples. Furthermore, a novel score based on gradient norms is proposed, demonstrating superior efficacy in distinguishing between in-distribution and OOD data.
• Robustness Against Adversarial Attacks: Without sacrificing classification performance, JEM shows an increased adversarial robustness. With minimal SGLD steps, JEM fortifies defenses against perturbation-based adversarial attacks across multiple norms, which is crucial for deployment in security-sensitive environments.

Implications and Future Directions

From a practical standpoint, this research presents a seamless pathway for integrating generative modeling advantages into mainstream discriminative models, potentially modifying standard practices in machine learning by dissolving the generative-discriminative model dichotomy. Its insights into better calibration and OOD detection are particularly relevant in enhancing the robustness needed for AI systems tasked with safety-critical decisions.

Theoretically, the formulation and implementation of a hybridized model under the EBM framework signal a fertile ground for further exploration. Future research could examine the scalability and efficiency of this approach in larger and more complex environments or potentially synergize it with alternative training methods for EBMs, such as Score Matching or Noise Contrastive Estimation, to mitigate training instabilities.

In conclusion, while challenges remain in terms of training stability and computational resource requirements, the promising results achieved by integrating energy-based interpretations into existing classifier architectures advocate for continuing exploration and refinement of such hybridized approaches in artificial intelligence research.