On November 11-13, I was in a hackathon! I learned so much that weekend, including how to speedrun a tutorial and how to quickly execute on a research question. I'll be more keen to form or join a team next time, which should help get more done. Here's the link to my results:
- https://poppingtonic.itch.io/ob
- https://github.com/poppingtonic/transformer-visualization
The Microsoft Rice Disease Classification Challenge introduced a dataset comprising RGB and RGNiR (RG-Near-infra-Red) images. This second image type increased the difficulty of the challenge such that all of the winning models worked with RGB only. In this challenge we applied a res2next50 encoder that was first pre-trained with self-supervised learning through the SwAV algorithm, to represent each RGB and their corresponding RGNIR images with the same weights. The encoder was then fine-tuned and self-distilled to classify the images which produced a public test set score of 0.228678639, and a private score of 0.183386940. K-fold cross-validation was not used for this challenge result. To better understand the impact of self-supervised pre-training on the problem of classifying each image type, we apply t-distributed Stochastic Neighbour Embedding (t-SNE) on the logits (predictions before applying softmax). We show how this method graphically provides some of the value of a confusion matrix, by locating some incorrect predictions. We then render the visualisation by overlaying the raw images in each data point, and note that to this model, the RGNIR images do not appear to be inherently more difficult to categorise. We make no comparisons through sweeps, RGB-only models or RGNIR-only models. This is left to future work.
This report tries to explain a simple-to-understand method for visualising the distribution of raw predictions from a vision classifier on a random sample of data in the validation set.
We do this to, at a glance;
explain the model in ways that can help us improve it.
to understand the data itself, asking the question, if the model struggled to classify RGNIR images more than RGB images.
Combining data from multiple sensors seems to be a good way to increase the number of training set examples, which has a known positive effect on train/test performance, among other measures of generalisation. Additional sensors are often deployed to capture different features from the baseline sensors, which may help to resolve their deficiencies. Less well studied is the question of when the additional sensor(s) add noise or require more representational capacity from the model, whether this reduces its capacity to perform the task on even the baseline sensor data.
This work is an example of post-hoc interpretability, which addresses the black-box nature of our models, where we do not have access to their internal representations, or ignore the structure of the model whose behaviour we are trying to explain. This means that we only use raw predictions and labels (0.0 = blast, 1.0 = brown, 2.0 = healthy) on each data point, ignoring the model’s layer structure, learned features, dimensionality, weights and biases. This lets us use general methods for clustering data such as t-SNE. To plot a 2D image, we initialise using PCA to reduce dimensionality to 2 components, and apply perplexity=50. Note the overlaps i.e the presence of false-positives in each class, indicating the need for k-fold cross-validation.
To show the effect that the image type had on classification, we overlay each datapoint with the raw image it represents. This follows from related work by Karpathy and Iwana et. al which use this methodology to produce informative visualisations with some explanatory value, although in this case the effect is more salient due to the two image types. We see where the RGNIR images tend to cluster in relation to their location in the global cluster regions in the chart above. Note the density of RGNIR images in the “tip” of the “blast” cluster (blue region in the first plot, scroll up then back), and in the bottom middle, indicating that while some RGNIR images were easy to correctly classify as “blast”, others were more easily confused with “brown” than they were with “healthy”. Qualitatively, there appear to be more false-positive RGNIR images than not, which might indicate higher uncertainty or noise in the predictions due to conflicting sensor data. This might be an artefact of the data augmentation methods used to train SwAV and the classifier. A lot more region-overlapping in the centroid of the image, together with the presence of both image types indicates some confusion for the classification task.
There are many reasons not to put much weight on the analysis above. T-SNE is valuable only after multiple runs have been observed. We might also want to include comparisons with weights from different epochs, early in training. More generally, statistical grounding improves the quality of good interpretability methods. In conclusion, the separation could be improved by applying readily available methods and there is no a priori reason to expect the pretraining strategy to contribute to better separation of classes. It helps with representing the images more fairly, but not decisively for the classification problem. All this work can be reproduced with the notebooks available here. The repository also has links to model weights: Rice Disease Classification through Self-Supervised Pre-training.
We show that when correctly applied, t-SNE, or potentially other types of dimensionality reduction methods can produce plots that can help us understand which of our training strategies could be changed in order to improve the model’s test set scores. In this case, we identify cross-validation as a potential intervention. We also learn more about our data using a method that is reproducible and reusable for other domains.
Kerem Turgutlu for self-supervised: https://keremturgutlu.github.io/self_supervised
Zachary Mueller: https://walkwithfastai.com
Jeremy Howard: https://fast.ai
Daniel Gitu, Ben Mainye and Alfred Ongere for helping proofread a draft of this document.
Open Philanthropy for funding part of this work.
When training a classifier, we eventually find predictions that are correct with high confidence. Naively applied, self-distillation in this case meant assigning labels to high-confidence test set examples. We collect these new labels and create a new “train.csv” which is used to fine-tune the best checkpoint with the dataset updated to include resampled test set examples, with their predicted labels. The final private test set predictions were produced after 2 rounds of self-distillation.
t-SNE is a dimensionality reduction method useful for producing beautiful visualisations of high dimensional data. It gives each high-dimensional data point a location on a 2D or 3D map. This relies on the parameter n_components, which we set to 2 for a 2-dimensional image. t-SNE is a non-linear, and adaptive transformation, operating on each data point based on a balance between its neighbours (local information) and the whole sample dataset (global information). For this, the hyperparameter ‘perplexity’ is applied. We set this to 50 in the presented plot, after sampling values below that (2, 10, 30), and above (100) to observe the different plots that are generated.
This is an analogy used in informal arguments related to AI's potential for catastrophic risk. The value of the analogy in this name was, in my view, that it pointed out the idea of "a random outcome that nobody asked for". Paperclips are what you'd call a niche interest, for humans nearly everywhere in the past or future. So an incredibly powerful computer that somehow managed to maximize the number of paperclips on earth over everything else, against the wishes of its controllers, would produce a random outcome that nobody asked for, especially those who don't care one bit about paperclips.
As someone who wants to interpret and explain decisions from deep learning models, I like to highlight difficult datasets as a subject for study. My current challenge is Iwana et. al’s “Judging a Book By its Cover”, which introduced a 200k+ image multi-feature dataset of book covers from Amazon. The paper posed a genre classification challenge. The original tasks are very challenging for a convolutional neural network to tackle. My own attempts with a resnet-50 fine-tuned from ImageNet only slightly beat the published top result, with 0.306 top-1 accuracy. Training procedure was SGD with warm restarts, discriminative fine-tuning, cyclical learning rate decay schedule, progressive image resizing and data augmentation at test time.
This is a T-SNE visualisation of my model’s test set performance on a sample of the dataset:
The spikes represent 30% of the images, with 70% in the centroid, underrepresented for their label. T-SNE ran with perplexity=15.
The visualisation suggests that relying on the convolutional inductive bias only works for a small number of naturalistic covers represented in the dataset (the spikes in the image), but fails to find any genre-unique similarity between most varieties of plain human-designed text, fonts and graphic design. It might also be due to different forms of imbalance in the dataset. This visualisation and theory is worth more exploration. Testing multimodal models with a Text+Vision inductive bias on this dataset might shed some light on this. For example, evaluating and visualising contrastive language-image pretraining (CLIP) in inference mode. The CLIP paper claims that it can do OCR. Can it classify the recognized text as well? Here we would evaluate CLIP’s zero-shot performance based on the task of classifying the text in the book cover image by genre. The task would be: each image would have a question asking "Is this a picture of a <genre>?"
There's an interpretability project here, which would be to visualise multimodal embeddings activated by this task and use that to explain why it works better, if it does. This would be one entry point for work on visualising and explaining large language models because it often feels like visualisation is simpler with multimodal tasks.
1. I recently taught AI Saturdays Nairobi about DEViSE (Deep visual-semantic embedding) methods, which can be useful in visual image search, dataset curation, semantic image search, and [possibly] blocking movie spoilers you'd rather not see..? Notebook is available here: devise-food101-v2.ipynb.
2. In April, I participated in the inaugural AI4D (AI for Development) network of excellence in Artificial Intelligence for Sub-Saharan Africa. See updates from the event here: AI4D-SSA.
3. Sign up to participate in the Omdena AI Challenge! See the details here: Omdena AI Challenge.
4. Nairobi Women in Machine Learning and Data Science is holding an event in June to encourage people to contribute to critical infra in ML. This time it's scikit-learn: Scikit-Learn Sprint (contribute to open source).
