SiamJEPA uses Siamese student encoders to improve JEPA-based representation learning.
problem Improving self-supervised representation learning in JEPA models.
method Proposes SiamJEPA with masked Siamese student encoders and EMA teacher network.
result Siamese student encoders improve representation separability and learning speed.
Unified Siamese network for wireless positioning and channel charting.
problem Wireless positioning and channel charting using CSI.
method Unified Siamese neural network architecture for both supervised and unsupervised learning.
result Siamese networks achieve similar or better performance than existing methods.
New method predicts drug interactions from drug images.
problem Predicting drug interactions from molecular structures.
method Siamese neural network using drug structure images.
result First work predicting DDIs from drug images.
Improved cover song detection with neural networks.
problem Identifying cover songs from original recordings.
method Siamese Convolutional Neural Networks trained on cover song audio clips.
result Mean precision@1 of 65% over mini-batches, significantly outperforming random guessing.
Improved RA detection with SNN outperforming baseline by 26.8% EER.
problem Improving RA detection systems' generalizability and discriminability.
method Multi-task learning with Siamese Neural Networks (SNN) and additional reconstruction loss.
result SNN outperforms baseline by 26.8% EER, and further improvement by 13.8% with reconstruction loss.
Paper introduces new loss functions for Siamese networks using FDA.
problem Training Siamese networks with improved loss functions.
method Proposes Fisher Discriminant Triplet (FDT) and Fisher Discriminant Contrastive (FDC) loss functions based on FDA.
result Shows effectiveness of FDT and FDC on MNIST and histopathology datasets.
Adaptive Siamese network improves local feature descriptor learning efficiency.
problem Estimating the size of neural networks for local feature descriptors.
method Adaptive pruning Siamese architecture based on neuron activation.
result Learned local feature descriptors outperform state-of-the-art methods in patch matching.
Paper presents a Siamese network for identifying more convincing evidence.
problem Identifying the more convincing argument in discussions.
method Proposes a Siamese neural network architecture for labeling evidence as more convincing.
result The Siamese network outperforms baselines on a new challenging data set.
Proposes a Siamese NN for algorithm selection focusing on alike performing instances.
problem Lack of effective meta-features for algorithm selection via meta-learning.
method Siamese Neural Network architecture with 'Algorithm-Performance Personas' concept.
result Proposed metric outperforms standard performance metrics in training sample selection.
A new method for tracking objects using diverse templates.
problem Improving visual tracking performance and robustness.
method Proposes a framework that uses additional object templates and a new diversity measure in siamese feature space.
result Achieves strong empirical results on tracking benchmarks, improving performance and robustness.
New method learns subspace affinity function for scalable and inductive clustering.
problem Scaling and inductiveness of self-expressiveness methods for high-dimensional data.
method Siamese neural network architecture for metric learning.
result Model scales to larger datasets and clusters out-of-sample data.
SSPN uses deep learning to estimate risk scores in survival analysis with competing risks.
problem Nonidentifiability of cause-specific survival curves in competing risk survival analysis.
method Siamese Survival Prognosis Network (SSPN) that avoids estimating cause-specific survival curves and optimizes an approximation to the C-discrimination index.
result SSPN estimates pairwise concordant time-dependent risks, improving risk scoring in survival analysis with competing risks.
Flexible models of non-flexible polyhedra explained.
problem Non-flexible Siamese dipyramids behave like flexible ones.
method Simple mathematical method to explain model flexibility.
result Physical models of Siamese dipyramids are flexible.
Siamese neural networks help learn shared variability from multiple sensors.
problem Learning common variability from multiple sensors.
method Applying Siamese neural networks to learn equivalence classes.
result Siamese networks can learn shared variability without model or label information.
A method to approximate Wasserstein distance for faster computations.
problem Heavy computational cost of Wasserstein distance.
method Embedding Euclidean distance to mimic Wasserstein, using siamese architecture and decoder network.
result Fast computation of optimization problems in Wasserstein space.
A method learns to imitate from a single video demonstration using contrastive training and Siamese networks.
problem Learning to imitate from video demonstrations without direct access to state or action information.
method Contrastive training with Siamese recurrent neural networks to learn rewards and an RL policy to minimize distance.
result The method significantly improves policy learning and outperforms current techniques in various environments.
Improved acoustic word embeddings using shared decoder in multi-view encoders.
problem Learning discriminative acoustic word embeddings from text labels.
method Combining Siamese multi-view encoders with a shared decoder network to maximize the relationship between acoustic and text embeddings.
result 11.1% relative improvement in average precision on acoustic word discrimination task with WSJ dataset.
A new method explains Siamese neural networks using feature comparison and autoencoder.
problem Explaining the decision-making process of Siamese neural networks.
method Feature comparison at embedding level and autoencoder reconstruction.
result The method effectively explains Siamese neural networks using MNIST dataset.
SidAE combines autoencoders and Siamese networks for self-supervised feature extraction.
problem Efficiently extract meaningful features from unlabeled data.
method Proposes SidAE, a combination of autoencoders and Siamese networks for self-supervised image classification.
result SidAE outperforms self-supervised baselines across multiple datasets and scenarios, especially with limited labeled data.
Enhanced deep learning model predicts stock price movement using LOB data.
problem Challenges in predicting stock price movement from high-dimensional, volatile LOB data.
method Siamese architecture with multi-head attention and LSTM modules.
result Significant improvement in stock price prediction performance over strong baselines.
Proposes Siamese network for generating adversarial examples.
problem Machine learning models are vulnerable to adversarial examples.
method Siamese network trained on unlabeled mismatched dataset to generate adversarial perturbations.
result Adversarial perturbations learned from Siamese network are also adversarial w.r.t. target model.
SDF improves Siamese neural networks using deep forest.
problem Overfitting in neural networks with limited data.
method Modified training set with concatenated pairs, weighted class distributions, quadratic optimization.
result Reduces distances between similar pairs and increases between dissimilar points.
This research uses Siamese networks to identify partial mouse brain images from the Allen atlas.
problem Identifying precise mouse brain microscopy images from the Allen atlas.
method Siamese Networks with contrastive learning to find corresponding atlas plates for partial images.
result Siamese CNNs achieved 25% TOP-1 and 100% TOP-5 accuracy in identifying brain slices from the Allen atlas.
Novel model identifies unseen classes with a single example.
problem Weakly supervised one-shot detection of unseen classes.
method Siamese similarity network with attention mechanism.
result Significantly outperforms baseline methods in experiments.
A video-based re-identification method using attention mechanisms.
problem Associating videos of the same person from different cameras.
method Siamese framework with attention mechanisms for spatial and temporal information.
result Achieves better performance than state-of-the-art on iLIDS-VID dataset.
Beta-SOD detects and corrects noisy object re-identification using cosine similarity and Beta mixtures.
problem Noisy object re-identification in image datasets.
method Reframed Re-ID as a similarity task, using Siamese networks and Beta mixture models.
result Superior performance in noisy conditions compared to state-of-the-art methods.
New Siamese Capsule Networks improve face verification in limited data settings.
problem Few-shot face verification with limited labeled data.
method Siamese Capsule Networks with contrastive loss and normalized pose features.
result Siamese Capsule Networks outperform baselines in few-shot face verification.
A neural network approach to compute stable metrics for numerical simulation data.
problem Computing stable and generalizing metrics for diverse numerical simulation data.
method A Siamese neural network architecture with a specialized loss function trained on a controlled data generation setup.
result LSiM outperforms existing metrics for vector spaces and image-based metrics.
MRAI-NET learns MRI scanner-independent features for better tissue segmentation.
problem Invariance of MRI voxelwise classifiers across different scanners.
method Siamese neural network to extract acquisition-invariant feature vectors.
result MRAI-NET outperforms traditional classifiers in small sample settings.
S3C2 uses Siamese networks for semi-supervised clustering with pairwise constraints.
problem Semi-supervised clustering with pairwise constraints.
method S3C2 decomposes SSC into two classification tasks: first, using Siamese networks to label unlabeled pairs; second, using the labeled dataset for clustering.
result S3C2 outperforms existing SSC methods on various datasets.
Improved Schizophrenia diagnosis using brain signal features with limited observations.
problem Ambulatory diagnoses of neuronal diseases with limited brain signal data.
method Pairwise distance learning approach using Siamese neural network and cosine contrastive loss.
result Improved accuracy and sensitivity in Schizophrenia diagnosis (+10pp).
Paper improves DOA estimation in sparse arrays using Siamese neural networks.
problem Challenges in DOA estimation with limited snapshots in sparse linear arrays.
method Introduces a Siamese neural network with a sparse augmentation layer for enhanced signal feature embedding.
result Demonstrates improved DOA estimation accuracy in sparse arrays.
KitcheNette predicts and recommends food ingredient pairings.
problem Limited study of food ingredient pairings despite many existing pairings.
method Siamese neural networks trained on a dataset of 300K scores.
result KitcheNette outperforms other models and discovers novel pairings.
New model creates code semantics vectors for better understanding.
problem Improving code understanding and embedding quality.
method Siamese recurrent neural network on Python source code.
result Model significantly outperforms bag-of-tokens embeddings.
Paper proposes a method for efficient online classification using siamese networks and active learning.
problem Online learning challenges with limited, nonstationary, and imbalanced data.
method Synergistic combination of siamese neural networks and active learning with multi-sliding window approach.
result Significantly outperforms baselines and state-of-the-art algorithms in terms of learning speed and performance.
New method accelerates large margin metric learning for nearest neighbor classification.
problem Efficiently learning metrics for nearest neighbor classification.
method Triplet mining and stratified sampling for large margin metric learning.
result Improved efficiency and scalability of optimization.
DEN creates interpretable visualizations using Siamese networks.
problem Creating interpretable visualizations of complex datasets.
method Differentiating Embedding Networks (DEN) using Siamese neural networks and loss functions.
result DEN outperforms existing techniques on FashionMNIST and interpretable features are identified.
Model predicts hit songs in pop music using Siamese CNN with ranking loss.
problem Predicting hit songs in the pop music industry.
method A multi-objective Siamese CNN model trained with Euclidean and ranking losses, using A/B sampling and neural attention.
result The model with A/B sampling achieves higher accuracy in hit song prediction.
CAZSL learns to generalize physical interactions from context.
problem Designing models that can generalize over unknown objects during manipulation.
method Context-aware zero-shot learning using Siamese network architecture, embedding space masking, and regularization.
result CAZSL models can generalize to different parameters or features of interacting objects.
AutoEmbedder clusters unlabeled data using semi-supervised DNN embedding.
problem Clustering unlabeled data efficiently and effectively.
method Semi-supervised DNN embedding system using Siamese network architecture.
result AutoEmbedder outperforms existing DNN-based semi-supervised methods.
Model learns to sort music clips in sequence.
problem Finding an optimal permutation of music clips.
method Proposed a music puzzle game for self-supervised learning of neural networks.
result Improved architecture (SEN) performs better on music medley.
Computer vision SSL methods show effectiveness on time series data.
problem Evaluate if computer vision SSL frameworks are effective on time series data.
method Evaluated on UCR and UEA archives, proposed a new method improving VICReg.
result Computer vision SSL frameworks can be effective on time series data.
Graph change-point detection method learns graph similarity from data.
problem Detect abrupt changes in dynamic networks.
method Siamese graph neural network for graph similarity learning.
result Method detects changes in diverse types of networks with minimal data history.
Study explores how dataset breadth and depth affect Siamese Neural Network performance.
problem Impact of dataset breadth and depth on Siamese Neural Network performance.
method Experiments with three keystroke datasets varying breadth and depth factors.
result Increasing dataset breadth improves model performance, while depth's impact varies by dataset type.
Two solutions for multi-modal record linkage using Deep Learning inspired by Visual Question Answering.
problem Matching records from multiple sources representing the same entity.
method Two fusion modules: Recurrent Neural Network + Convolutional Neural Network and Stacked Attention Network. A Siamese Neural Network computes similarity.
result Recurrent Neural Network + Convolutional Neural Network fusion module outperforms a simple model.
DRSVM uses deep learning to rank relative attributes between image pairs.
problem Classifying relative attributes between image pairs.
method Deep Siamese network with rank SVM loss function.
result DRSVM outperforms state-of-the-art methods on multiple datasets.
Framework learns structural and functional brain network embeddings while preserving their properties.
problem Joint learning of structural and functional brain networks while preserving their intrinsic properties.
method Siamese community-preserving graph convolutional network (SCP-GCN) that learns from both structural and functional connectivity.
result Superior performance in neurological disorder analysis compared to existing methods.
Proposes k-Same-Siamese-GAN for de-identifying facial images efficiently and preserving privacy.
problem Ensuring privacy of facial images while preserving useful data.
method Uses k-Same-Anonymity, GAN, hyperparameter tuning, and mixed precision training.
result Efficient de-identification of high-resolution facial images with privacy guarantees.