Search Results for author: Yunqiang Li

Found 8 papers, 3 papers with code

Understanding weight-magnitude hyperparameters in training binary networks

1 code implementation4 Mar 2023 Joris Quist, Yunqiang Li, Jan van Gemert

Our analysis makes it possible to understand how magnitude-based hyperparameters influence the training of binary networks which allows for new optimization filters specifically designed for binary neural networks that are independent of their real-valued interpretation.

Equal Bits: Enforcing Equally Distributed Binary Network Weights

1 code implementation2 Dec 2021 Yunqiang Li, Silvia L. Pintea, Jan C. van Gemert

We investigate experimentally that equal bit ratios are indeed preferable and show that our method leads to optimization benefits.

Binarization Quantization

Less bits is more: How pruning deep binary networks increases weight capacity

no code implementations1 Jan 2021 Yunqiang Li, Silvia Laura Pintea, Jan van Gemert

We make the observation that pruning weights adds the value 0 as an additional symbol and thus increases the information capacity of the network.

Deep Unsupervised Image Hashing by Maximizing Bit Entropy

1 code implementation22 Dec 2020 Yunqiang Li, Jan van Gemert

This layer is shown to minimize a penalized term of the Wasserstein distance between the learned continuous image features and the optimal half-half bit distribution.

Deep Hashing Semantic Retrieval

Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels

no code implementations16 Oct 2020 Xiangwei Shi, Seyran Khademi, Yunqiang Li, Jan van Gemert

Current weakly supervised object localization and segmentation rely on class-discriminative visualization techniques to generate pseudo-labels for pixel-level training.

Segmentation Weakly-Supervised Object Localization +2

WeightAlign: Normalizing Activations by Weight Alignment

no code implementations14 Oct 2020 Xiangwei Shi, Yunqiang Li, Xin Liu, Jan van Gemert

Such methods are less stable than BN as they critically depend on the statistics of a single input sample.

Domain Adaptation Semantic Segmentation

Push for Quantization: Deep Fisher Hashing

no code implementations31 Aug 2019 Yunqiang Li, Wenjie Pei, Yufei zha, Jan van Gemert

In this paper we push for quantization: We optimize maximum class separability in the binary space.

Quantization Semantic Similarity +1

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