Diffusion Models Beat GANs at Image Classification: This AI Research Finds Diffusion Models Outperform Comparable Generative-Discriminator Methods Like BigBiGAN for Classification Tasks

Diffusion Models Beat GANs at Image Classification: This AI Research Finds Diffusion Models Outperform Comparable Generative-Discriminator Methods Like BigBiGAN for Classification Tasks

Learning unified and unsupervised visual representations is a crucial but difficult task. Many computer vision problems fall into two basic categories: discriminative or generative. A model that can assign labels to individual images or slices of images is trained by learning the discriminant representation. To use generative learning, you would create a model that creates or edits images and performs related operations such as inpainting, super-resolving, etc. Students of unified representation pursue both goals simultaneously, and the final model can discriminate and create unique visual artifacts. This kind of learning about unified representation is difficult.

One of the first deep learning techniques that solves both families of problems simultaneously is BigBiGAN. However, the classification and generation performance of the more current methods exceeds that of BigBiGAN using more specialized models. In addition to BigBiGAN’s higher accuracy and FID shortcomings, it also has a considerably higher training load than other approaches, is slower and larger than comparable GANs due to its encoder, and costs more than ResNet-based discriminative approaches due to its GAN. PatchVAE aims to improve the performance of VAE for acknowledgment tasks by focusing on mid-level patch learning. Unfortunately, its improvements in classification still fall far short of supervised approaches, and image production performance suffers markedly.

Recent research has made great strides with good results in generation and categorization, both with and without supervision. Unified learning of self-supervised representation has yet to be addressed because this area is yet to be explored with respect to the number of work in learning self-supervised image representation. Some researchers argue that discriminative and generative patterns inherently vary, and that the representations acquired from one are inappropriate for the other due to prior flaws. Generative models inherently require representations that capture low-level features, pixels, and textures for high-quality reconstruction and creation.

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On the other hand, discriminative models depend mainly on high-level information that distinguishes objects at an approximate level based not on specific pixel values ​​but rather on the semantics of the image content. Despite these assumptions, they indicate that current techniques such as MAE and MAGE, where the model needs to tend to low-level pixel information but learns patterns that are also excellent for classification tasks, support the initial success of BigBiGAN. Modern diffusion models have also been quite successful in meeting generation goals. Their categorization potential is, however, mostly untapped and unstudied. Researchers at the University of Maryland argue that instead of creating a student of unified representation, state-of-the-art diffusion models from scratch, powerful imaging models already have strong classification capabilities emerging.

Figure 1: a summary of the approach and results. They suggest that diffusion models can learn self-supervised unified image representations, performing admirably for both generation and classification. In terms of U-Net block number and diffusion noise time step, we investigate the feature extraction procedure. We also look at various feature map grouping dimensions. We examine a number of simple feature classification architectures, such as the linear (A), multilayer (B), CNN (C), and attention-based heads (D) perceptron. For classification heads trained on the frozen features for ImageNet-50, calculated at block number 24 and noise timestep 90, the results of those studies are shown on the right.

Figure 1 shows their remarkable success in these two fundamentally different challenges. Compared to BigBiGAN, their strategy for using diffusion models yields significantly higher imaging performance and better image categorization performance. As a result, they demonstrate that diffusion models are already very close to state-of-the-art self-supervised unified representation students in terms of optimization for both classification and concurrent generation. Feature selection in diffusion models is one of their main difficulties. It is very difficult to choose noise steps and feature blocking. They then examine the applicability of the various aspects and compare them. These feature maps can also be quite large in channel depth and spatial resolution.

They also offer several sorting heads to replace the linear sorting level to solve this problem, which can improve the sorting results without sacrificing generation performance or adding more parameters. They show that diffusion models can be used for classification problems without changing diffusion pre-training as they perform excellently as classifiers with proper feature extraction. As a result, their method can be used for any pre-trained diffusion model and can thus take advantage of upcoming improvements in size, speed and image quality of these models. The effectiveness of dissemination features for transferring learning to downstream tasks is also examined, and the features are directly compared to those of other approaches.

They select fine-grained visual classification (FGVC) for downstream activities, which appeals to the use of unsupervised features due to the indicated lack of data for many FGVC datasets. Because a diffusion-based approach does not rely on the kinds of color invariances that other studies have shown would limit unsupervised approaches in the context of FGVC transfer, this task is especially relevant using a diffusion-based approach. They use the well-known kernel centered alignment (CKA) to compare features, which allows for in-depth investigation into the significance of feature selection and how diffusion model features compare to those of ResNet and ViT.

Their contributions, in summary, are as follows:

With 26.21 FID (-12.37 vs. BigBiGAN) for unconditional image formation and an accuracy of 61.95% (+1.15% vs. BigBiGAN) for linear survey on ImageNet, they show that diffusion models can be employed as students of unified representation.

They provide analysis and distillation guidelines to obtain the most usable feature representations from the diffusion process.

To use diffusion representations in a classification scenario, contrast attention-based heads, CNNs, and specialized MLP heads with standard linear polls.

Using many well-known datasets, they examine the learning characteristics of transferring diffusion patterns with fine-grained visual categorization (FGVC) as a downstream activity.

They employ CKA to compare the many representations learned from diffusion models with alternative architectures and pre-training techniques, as well as with different levels and characteristics of diffusion.


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Aneesh Tickoo is a Consulting Intern at MarktechPost. She is currently pursuing her BA in Data Science and Artificial Intelligence from Indian Institute of Technology (IIT), Bhilai. She spends most of her time working on projects that harness the power of machine learning. Her research interest is image processing and she is passionate about building solutions around it. She loves connecting with people and collaborating on interesting projects.


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