New method improves ensemble diversity and generalization.
problem Ensemble diversity does not guarantee practical generalization.
method Introduced a new diversity metric and training method for extrapolating differently on local data patches.
result Improves generalization and diversity in practical settings, especially under data limits and covariate shift.
This work studies the impact of intra-/inter-class diversity on pre-training datasets and finds a balance for optimal performance.
problem The impact of intra-/inter-class diversity on supervised pre-training datasets and their effect on downstream tasks.
method Empirical study and theoretical analysis of the relationship between diversity types and downstream performance.
result The optimal class-to-sample ratio is invariant to the size of the pre-training dataset and can be predicted.
New findings show that minimal feature diversity at neural network initialization is harmful but can be mitigated with noise.
problem The importance of feature diversity at neural network initialization.
method A series of experiments comparing different initialization schemes, including adding noise.
result Minimal feature diversity is harmful but can be mitigated with noise, even standard GPU noise is sufficient.
New framework shows diverse training data improves subgroup and overall performance.
problem Lack of understanding how diverse training data affects subgroup and overall performance.
method Casts data collection as part of the learning process, analyzes dataset compositions, and guides dataset design.
result Diverse representation in training data improves subgroup and overall performance.
SharpBalance improves deep ensemble performance by balancing sharpness and diversity.
problem Improving deep ensemble performance in both in-distribution and out-of-distribution scenarios.
method Introducing SharpBalance, a novel training approach that balances sharpness and diversity within ensembles.
result SharpBalance effectively improves the sharpness-diversity trade-off and ensemble performance in ID and OOD scenarios.
Training task diversity improves ICL with linear attention.
problem Understanding the impact of training task diversity on in-context learning.
method Modeling training task vectors as a mixture of low-rank Gaussians.
result Our model explains why training with task diversity shortens the ICL plateau and achieves out-of-distribution generalization.
A method for efficient reinforcement learning query reformulation.
problem Efficiently learn diverse strategies for query reformulation.
method A framework with specialized sub-agents and a meta-agent trained on full data.
result Improved generalization performance and diversity of reformulation strategies.
New method shows random, diverse initializations are not essential for deep neural networks.
problem The necessity of random, diverse initializations in deep neural networks.
method Constructed a deep convolutional network with identical features by initializing weights to 0, enabling signal propagation and stable gradients.
result Random, diverse initializations are not necessary for training neural networks.
Mixreg improves RL generalization by mixing diverse training environments.
problem Overfitting in RL agents trained in limited environments.
method Trains on a mixture of diverse observations and imposes linearity constraints.
result Mixreg outperforms baselines on unseen testing environments.
New analysis shows diverse classes in pre-training boost NLP performance.
problem Improving sample efficiency in downstream NLP tasks.
method Proved that diverse classes in pre-training lead to better performance, using a large last linear layer singular value.
result Transfer learning excess risk improves with large ildeν and $O\left(\frac{1}{ ildeν \sqrt{n}}
ight)$ rate. MO-PaDGAN generates diverse, high-performance designs with multiple metrics.
problem Challenges in generating diverse, high-performance designs with multiple metrics.
method MO-PaDGAN uses a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and performances.
result MO-PaDGAN expands the design space towards high-performance regions and generates new designs with high diversity and performances.
Self-distillation improves model performance by increasing teacher diversity and smoothing predictions.
problem Improving model generalization and performance through self-distillation.
method Interpreting self-distillation as MAP estimation and proposing instance-specific label smoothing.
result Self-distillation enhances model performance by increasing teacher diversity and smoothing predictions.
Proposes a method to generate diverse translations by conditioning on target domain.
problem NMT models lack diversity in translations, even with search algorithms.
method Condition the decoder on a latent variable representing target domain, generated by a target encoder.
result Generated diverse translations without affecting performance or training time.
Proposes Ada-Sit method for mortality prediction of rare diseases.
problem Data insufficiency and clinical diversity of rare diseases make mortality prediction hard.
method Initialization-sharing multi-task learning method (Ada-Sit) for fast adaptation to similar tasks.
result Experimental results show the proposed model is effective for mortality prediction of diverse rare diseases.
New method selects diverse mini-batches for active learning.
problem Reduce labeled data for deep learning models.
method Sequential selection of diverse mini-batches using K-means clustering.
result Achieves comparable or better performance than previous methods.
DIVA generates diverse tasks for complex simulators, enabling adaptive agent training.
problem Lack of diverse training data for complex, open-ended simulators.
method Evolutionary approach using domain randomization and procedural generation.
result Successfully trains adaptive agent behavior in complex simulators.
PaDGAN generates diverse, high-quality designs with improved performance.
problem Lack of diversity and performance improvement in generated designs.
method Integrates Determinantal Point Processes for diversity and quality, using GAN framework.
result PaDGAN generates higher quality designs with better diversity and without mode collapse.
Generative model learns diverse generations using DPP to avoid mode collapse.
problem Mode collapse in generative models, limiting output to few modes.
method Inspired by DPP, proposes an unsupervised penalty loss to enhance diversity.
result Generative DPP approach consistently resists mode-collapse and outperforms state-of-the-art methods.
Method learns diverse models to resist spurious correlations.
problem Models learn spurious correlations in i.i.d. training data.
method Multiple models with minimized conditional total correlation.
result Framework significantly outperforms baseline on distribution shift.
This research proves guarantees on sequence models' generalization to longer and novel sequences.
problem Generalization to longer sequences and novel token combinations in sequence models.
method Provable guarantees on length and compositional generalization for various sequence models.
result Limited capacity models achieve both length and compositional generalization with diverse training distributions.
Improves GANs by enforcing diverse feature learning.
problem GANs can collapse to a single configuration and be unstable.
method Enforces diverse feature learning by penalizing correlated features.
result Enforces diverse features, stabilizes training, and improves image synthesis.
High-capacity neural network ensembles often benefit more from high-capacity models than from increased diversity.
problem The performance of high-capacity neural network ensembles is often harmed by interventions that promote predictive diversity.
method A large-scale study of nearly 600 neural network classification ensembles, examining various interventions and architectures.
result Discouraging predictive diversity can be benign in large-network ensembles, and higher-capacity models often yield better performance than diverse architectures.
Model learns diverse molecular transformations for optimization.
problem Optimizing molecules in various ways.
method Junction tree encoder-decoder with adversarial training.
result Model outperforms previous methods on molecular optimization tasks.
DVERGE diversifies adversarial vulnerabilities to enhance robust ensemble models.
problem Diverse adversarial vulnerabilities for robust ensemble models.
method Isolates and diversifies adversarial vulnerabilities through distillation and training.
result Achieves higher robustness against transfer attacks compared to previous methods.
OKDDip uses diverse peers to improve online knowledge distillation.
problem Early saturation in group-based distillation.
method Two-level distillation with multiple auxiliary peers and a group leader, using attention-based aggregation weights.
result OKDDip consistently gives better performance than state-of-the-art approaches.
DEN learns diverse tasks to generalize to unseen tasks.
problem Generalization from a diverse set of classification tasks with limited data.
method Three-block architecture: covariate transformation, distribution embedding, and classification.
result DEN outperforms existing methods in various synthetic and real tasks.
A new confidence measure improves self-training in biased data.
problem Improving self-training in biased data.
method Proposes a new confidence measure, T-similarity, based on ensemble diversity of linear classifiers.
result Empirically shows the benefit of T-similarity for pseudo-labeling policies on various datasets.
Lung segmentation accuracy varies little across diverse datasets.
problem Limited clinical applicability of automated lung segmentation methods.
method Comparison of four deep learning approaches and two standard algorithms on diverse datasets.
result Standard U-net approach yields higher accuracy on routine imaging data.
New method improves diversity in GAN-generated images.
problem Lack of diversity in GAN-generated images.
method Introducing moment reconstruction losses to replace the reconstruction loss in GAN training.
result Improved diversity in generated images without sacrificing visual fidelity.
DASH improves ensemble generalizability by encouraging diverse, flat loss landscapes.
problem Improving generalization and robustness of deep ensembles.
method DASH promotes diversity and flatness in deep ensembles by encouraging base learners to move towards low-loss regions of minimal sharpness.
result DASH improves ensemble generalizability, as demonstrated by extensive empirical evidence.
Proposes models to generate more interesting story endings.
problem Generating diverse and interesting story endings for a given context.
method Trains models to focus on keyphrases and promotes non-generic words.
result Models generate more diverse and interesting story endings.
This paper improves ensemble learning for vision tasks by encouraging diversity in predictions.
problem Generating effective ensembles of neural networks for multi-modal data.
method Explicitly optimize a diversity inducing adversarial loss for learning stochastic latent variables.
result Significant improvements in classification accuracy and out-of-distribution detection compared to baselines.
Unified framework for supervised classification with diverse training data.
problem Handling different types of training data for supervised classification.
method Generalized robust risk minimization (GRRM) with probabilistic transformations.
result GRRM can handle various training data types and new supervision schemes.
Generative model creates diverse neural network weights efficiently.
problem Creating high-performance and diverse weights for neural networks.
method Trains a hypernetwork mapping latent vectors to high-performance weights, balancing accuracy and diversity.
result Generated weights form a diverse manifold, improving classification accuracy.
We present a framework to understand GAN training as alternating density ratio estimation and approximate divergence minimization. This provides an interpretation for the mismatched GAN generator and discriminator objectives often used in practice, and explains the problem of poor sample diversity. We also derive a fam…
MosaicMRI expands public datasets for musculoskeletal MRI, revealing cross-anatomical correlations.
problem Limited diversity in public MRI datasets hinders model evaluation across different anatomical settings.
method Developed a large, diverse dataset (MosaicMRI) and conducted experiments on a baseline model (VarNet).
result Models trained on combined anatomies outperform anatomy-specific models in low-sample regimes.
SEERL uses ensemble methods to improve reinforcement learning efficiency.
problem High sample complexity and computational expense in reinforcement learning.
method Directed perturbation of model parameters to learn diverse policies, selection of an adequately diverse set of policies.
result Our approach outperforms state-of-the-art scores in Atari 2600 and Mujoco.
Ensemble of models with uncorrelated loss functions improves adversarial robustness.
problem Adversarial attacks on deep neural networks.
method Diversity Training: training an ensemble of models with uncorrelated loss functions.
result Our method significantly improves adversarial robustness of ensembles.
Deep learning dynamics and NTK evolution studied through diverse measures.
problem Understanding the training dynamics of deep neural networks and their loss landscapes.
method Phenomenological analysis of training dynamics in multiple architectures and datasets.
result Training dynamics exhibit a chaotic initial transient followed by a stable phase, with the NTK evolving to match full network performance.
Meta-CoTGAN improves adversarial text generation by preventing mode collapse.
problem Mode collapse in adversarial text generation.
method Meta-Cooperative Training Paradigm with a language model.
result Meta-CoTGAN effectively slows down mode collapse and improves generation quality and diversity.
Unified evaluation for both quality and diversity in NLP.
problem Measuring both quality and diversity in NLP models.
method Proposes HUSE, a metric combining human and statistical evaluation.
result HUSE detects both quality and diversity defects in NLP models.
Improved GANs model geological facies with diversity and unbiased distribution.
problem Generating unbiased and representative geological models from training images.
method Info-WGAN combining InfoGAN, Wasserstein distance, and Gradient Penalty.
result Generated samples have equal probability distribution as training data.
Proposes a new method for ensembling neural subnetworks.
problem Computational expense and limited flexibility of traditional deep ensembles.
method Sequential Bayesian neural subnetwork ensembling.
result Outperforms traditional ensembles in various metrics.
aMCL uses annealing to improve hypothesis diversity in ambiguous tasks.
problem Limitations of Winner-takes-all in predicting plausible hypotheses.
method Combines simulated annealing with Multiple Choice Learning (MCL).
result Enhanced exploration of hypothesis space during training.
Improved RL training for DMs reduces mode collapse and preserves diversity.
problem Mode collapse and training instability in RL fine-tuned diffusion models.
method Dynamic hierarchical RL training with sliding-window parameter regularisation.
result Models trained with HRF achieve better preservation of diversity in downstream tasks.
New measures quantify how data augmentation improves model performance.
problem Understanding the effectiveness of data augmentation in deep learning.
method Introduced Affinity and Diversity measures to quantify augmentation performance.
result Augmentation performance is best achieved by optimizing both Affinity and Diversity.
Master-slave architecture tackles combinatorial multi-armed bandits with diversity constraints.
problem Solving top-K combinatorial multi-armed bandits with non-linear feedback and diversity constraints. method Master-slave architecture with six slave models, teacher learning, and policy co-training.
result Significantly outperforms existing algorithms in synthetic and real datasets.
New method improves text classification without labeled target data.
problem Improving text classification under domain shift without labeled target data.
method Diversity-based generalization using multi-head attention with diversity constraints.
result Method matches state-of-the-art performance without labeled target data.