Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition
Authors: Dat Huynh, Ehsan Elhamifar
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on four popular datasets of Deep Fashion, AWA2, CUB, and SUN, showing that our method significantly improves the state of the art. |
| Researcher Affiliation | Academia | Dat Huynh Northeastern University EMAIL Ehsan Elhamifar Northeastern University EMAIL |
| Pseudocode | Yes | Algorithm 1 Composing Dense Features |
| Open Source Code | No | The paper does not provide concrete access to source code for the described methodology. |
| Open Datasets | Yes | We conduct experiments on four popular datasets: Deep Fashion [4], AWA2 [68], CUB [69], and SUN [70]. |
| Dataset Splits | Yes | We follow the data splits of [2] for Deep Fashion and of [68] for AWA2, CUB, and SUN. |
| Hardware Specification | Yes | We implement our framework in Py Torch and optimize it using RMSprop[74] with the default setting, learning rate of 0.0001 and batch size of 50 having an equal number of samples per class. We pre-train DAZLE on seen classes and use it to compose dense features for at most 2000 and 4000 iterations, respectively, on a NVIDIA V100 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'RMSprop' but does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We implement our framework in Py Torch and optimize it using RMSprop[74] with the default setting, learning rate of 0.0001 and batch size of 50 having an equal number of samples per class. We pre-train DAZLE on seen classes and use it to compose dense features for at most 2000 and 4000 iterations, respectively... To prevent seen class bias, we add a margin of 1 to unseen class scores and 1 to seen class scores... We experiment in two settings: i) using pre-trained Image Net features (pre-trained setting) and ii) ο¬ne-tuning the Res Net backbone on each dataset... We use the feature map of the last convolutional layer whose size is 7 7 2048... To measure the robustness of our method, we ο¬x the hyperparameters at T = 5, k = 5, b = 50 (T = 10, k = 10, b = 50) for the pretrained (ο¬ne-tuned) setting on all datasets. |