Adaptive Dataset Quantization: A New Direction for Dataset Pruning

Recent research has identified an 'Inductive Bottleneck' in Vision Transformers (ViTs), where these models exhibit a U-shaped entropy profile, compressing information in middle layers before expanding it for final classification. This phenomenon is linked to the semantic abstraction required by specific tasks and is not merely an architectural flaw but a data-dependent adaptation observed across various datasets such as UC Merced, Tiny ImageNet, and CIFAR-100.

A new approach to Vision Transformers (ViTs) has been introduced, featuring a Jumbo token that enhances processing speed by reducing patch token width while increasing global token width. This innovation aims to address the slow performance of ViTs without compromising their generality or accuracy, making them more practical for various applications.

A recent study has introduced a pruned version of Capsule Networks (CapsNets), demonstrating that it can operate up to 9.90 times faster than traditional architectures by eliminating 95% of Primary Capsules while maintaining accuracy across various datasets, including MNIST and CIFAR-10.

The CLUster attEntion Network (CLUENet) has been introduced as a novel deep architecture aimed at enhancing visual semantic understanding by addressing the limitations of existing convolutional and attention-based models, particularly their rigid receptive fields and complex architectures. This innovation incorporates global soft aggregation, hard assignment, and improved cluster pooling strategies to enhance local modeling and interpretability.

The paper introduces Arc Gradient Descent (ArcGD), a new optimizer that reformulates traditional gradient descent methods to incorporate phase-aware and user-controlled step dynamics. The evaluation of ArcGD shows it outperforming the Adam optimizer on a non-convex benchmark and a real-world ML dataset, particularly in challenging scenarios like the Rosenbrock function and CIFAR-10 image classification.

A new study presents a hybrid generative classification approach aimed at enhancing adversarial robustness in deep learning models. The proposed deep ensemble model integrates a pre-trained discriminative network for feature extraction with a generative classification network, achieving high accuracy and robustness against adversarial attacks without the need for adversarial training. Extensive experiments on CIFAR-10 and CIFAR-100 validate its effectiveness.

A new study proposes a structured initialization method for Vision Transformers (ViTs), aiming to integrate the strong inductive biases of Convolutional Neural Networks (CNNs) without altering the architecture. This approach is designed to enhance performance on small datasets while maintaining scalability as data increases.

The Iwin Transformer has been introduced as a novel hierarchical vision transformer that operates without position embeddings, utilizing interleaved window attention and depthwise separable convolution to enhance performance across various visual tasks. This architecture allows for direct fine-tuning from low to high resolution, achieving notable results such as 87.4% top-1 accuracy on ImageNet-1K.