16 research outputs found
Multi-level Distance Regularization for Deep Metric Learning
We propose a novel distance-based regularization method for deep metric
learning called Multi-level Distance Regularization (MDR). MDR explicitly
disturbs a learning procedure by regularizing pairwise distances between
embedding vectors into multiple levels that represents a degree of similarity
between a pair. In the training stage, the model is trained with both MDR and
an existing loss function of deep metric learning, simultaneously; the two
losses interfere with the objective of each other, and it makes the learning
process difficult. Moreover, MDR prevents some examples from being ignored or
overly influenced in the learning process. These allow the parameters of the
embedding network to be settle on a local optima with better generalization.
Without bells and whistles, MDR with simple Triplet loss achieves
the-state-of-the-art performance in various benchmark datasets: CUB-200-2011,
Cars-196, Stanford Online Products, and In-Shop Clothes Retrieval. We
extensively perform ablation studies on its behaviors to show the effectiveness
of MDR. By easily adopting our MDR, the previous approaches can be improved in
performance and generalization ability.Comment: Accepted to AAAI 202
AutoLR: Layer-wise Pruning and Auto-tuning of Learning Rates in Fine-tuning of Deep Networks
Existing fine-tuning methods use a single learning rate over all layers. In
this paper, first, we discuss that trends of layer-wise weight variations by
fine-tuning using a single learning rate do not match the well-known notion
that lower-level layers extract general features and higher-level layers
extract specific features. Based on our discussion, we propose an algorithm
that improves fine-tuning performance and reduces network complexity through
layer-wise pruning and auto-tuning of layer-wise learning rates. The proposed
algorithm has verified the effectiveness by achieving state-of-the-art
performance on the image retrieval benchmark datasets (CUB-200, Cars-196,
Stanford online product, and Inshop). Code is available at
https://github.com/youngminPIL/AutoLR.Comment: Accepted to AAAI 202
Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval
Optimising a ranking-based metric, such as Average Precision (AP), is
notoriously challenging due to the fact that it is non-differentiable, and
hence cannot be optimised directly using gradient-descent methods. To this end,
we introduce an objective that optimises instead a smoothed approximation of
AP, coined Smooth-AP. Smooth-AP is a plug-and-play objective function that
allows for end-to-end training of deep networks with a simple and elegant
implementation. We also present an analysis for why directly optimising the
ranking based metric of AP offers benefits over other deep metric learning
losses. We apply Smooth-AP to standard retrieval benchmarks: Stanford Online
products and VehicleID, and also evaluate on larger-scale datasets: INaturalist
for fine-grained category retrieval, and VGGFace2 and IJB-C for face retrieval.
In all cases, we improve the performance over the state-of-the-art, especially
for larger-scale datasets, thus demonstrating the effectiveness and scalability
of Smooth-AP to real-world scenarios.Comment: Accepted at ECCV 202