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Found 9 papers, 6 papers with code
Using Early Readouts to Mediate Featural Bias in Distillation
Deep networks tend to learn spurious feature-label correlations in real-world supervised learning tasks.
Overcoming Simplicity Bias in Deep Networks using a Feature Sieve
Simplicity bias is the concerning tendency of deep networks to over-depend on simple, weakly predictive features, to the exclusion of stronger, more complex features.
Interactive Concept Bottleneck Models
Concept bottleneck models (CBMs) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions.
On Designing Light-Weight Object Trackers through Network Pruning: Use CNNs or Transformers?
A comparison between SOTA trackers using CNNs, transformers as well as the combination of the two is presented to study their stability at various compression ratios.
Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-learning
MetaDOCK compresses the meta-model as well as the task-specific inner models, thus providing significant reduction in model size for each task, and through constraining the number of active kernels for every task, it implicitly mitigates the issue of meta-overfitting.
GCR: Gradient Coreset Based Replay Buffer Selection For Continual Learning
To address this, replay-based CL approaches maintain and repeatedly retrain on a small buffer of data selected across encountered tasks.
ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations
Structured pruning methods are among the effective strategies for extracting small resource-efficient convolutional neural networks from their dense counterparts with minimal loss in accuracy.
Rescaling CNN through Learnable Repetition of Network Parameters
We show that small base networks when rescaled, can provide performance comparable to deeper networks with as low as 6% of optimization parameters of the deeper one.
Multi-Plateau Ensemble for Endoscopic Artefact Segmentation and Detection
Endoscopic artefact detection challenge consists of 1) Artefact detection, 2) Semantic segmentation, and 3) Out-of-sample generalisation.
