Rotated MNIST
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Benchmarks
Rotated MNIST
Most implemented
DIVA: Domain Invariant Variational Autoencoders
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
Equivariance-bridged SO(2)-Invariant Representation Learning using Graph Convolutional Network
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
Deep Rotation Equivariant Network
Papers
Optimal Training-Time Scaling in Gradual Adaptation
In gradual adaptation, how should the training time on each task change as the number of intermediate tasks increases? We study this question for overparameterized linear regression tasks that change smoothly and share a…
Rotated MNISTLearning Coherent Representations: A Topological Approach to Interpretability
Deep neural networks learn representations where individual features often lack interpretable meaning; a single neuron may activate for scattered, unrelated inputs. We introduce coherence, a geometric property inspired b…
Rotated MNISTComputing a Characteristic Orientation for Rotation-Independent Image Analysis
Handling geometric transformations, particularly rotations, remains a challenge in deep learning for computer vision. Standard neural networks lack inherent rotation invariance and typically rely on data augmentation or …
Data AugmentationRotated MNISTDopamine: Brain Modes, Not Brains
Parameter-efficient fine-tuning (PEFT) methods such as \lora{} adapt large pretrained models by adding small weight-space updates. While effective, weight deltas are hard to interpret mechanistically, and they do not dir…
parameter-efficient fine-tuningRotated MNISTDomain Expansion: A Latent Space Construction Framework for Multi-Task Learning
Training a single network with multiple objectives often leads to conflicting gradients that degrade shared representations, forcing them into a compromised state that is suboptimal for any single task--a problem we term…
Multi-Task LearningGaze EstimationRotated MNISTMitigating Catastrophic Forgetting in Streaming Generative and Predictive Learning via Stateful Replay
Many deployed learning systems must update models on streaming data under memory constraints. The default strategy, sequential fine-tuning on each new phase, is architecture-agnostic but often suffers catastrophic forget…
Time Series ForecastingContinual LearningRotated MNIST