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Found 5 papers, 4 papers with code
Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models
Our Landmark DEQ (LDEQ) achieves state-of-the-art performance on the challenging WFLW facial landmark dataset, reaching $3. 92$ NME with fewer parameters and a training memory cost of $\mathcal{O}(1)$ in the number of recurrent modules.
Ranked #2 on
Face Alignment
on WFLW
Non-greedy Gradient-based Hyperparameter Optimization Over Long Horizons
We demonstrate that the hyperparameters of this optimizer can be learned non-greedily without gradient degradation over $\sim 10^4$ inner gradient steps, by only requiring $\sim 10$ outer gradient steps.
Gradient-based Hyperparameter Optimization Over Long Horizons
Gradient-based hyperparameter optimization has earned a widespread popularity in the context of few-shot meta-learning, but remains broadly impractical for tasks with long horizons (many gradient steps), due to memory scaling and gradient degradation issues.
Meta-Learning in Neural Networks: A Survey
We survey promising applications and successes of meta-learning such as few-shot learning and reinforcement learning.
Zero-shot Knowledge Transfer via Adversarial Belief Matching
Finally, we also propose a metric to quantify the degree of belief matching between teacher and student in the vicinity of decision boundaries, and observe a significantly higher match between our zero-shot student and the teacher, than between a student distilled with real data and the teacher.
